diff --git a/.gitignore b/.gitignore index 50a3724..8c081b3 100644 --- a/.gitignore +++ b/.gitignore @@ -1,36 +1,36 @@ - - - - -.idea/ -.vs/ - - -*.log -*.tmp -*.swp - - -frontend/node_modules/ -frontend/dist/ - - -*.venv/ -Backend/venv/ -Backend/__pycache__/ - - - - -*.dockerignore -*.env -docker-compose.override.yml - - -.DS_Store -Thumbs.db - - -*.lock -package-lock.json + + + + +.idea/ +.vs/ + + +*.log +*.tmp +*.swp + + +frontend/node_modules/ +frontend/dist/ + + +*.venv/ +Backend/venv/ +Backend/__pycache__/ + + + + +*.dockerignore +*.env +docker-compose.override.yml + + +.DS_Store +Thumbs.db + + +*.lock +package-lock.json yarn.lock \ No newline at end of file diff --git a/Backend/arch.py b/Backend/arch.py index 90e92ce..c9952b3 100644 --- a/Backend/arch.py +++ b/Backend/arch.py @@ -1,122 +1,122 @@ -from pptx import Presentation -from pptx.util import Inches -from pptx.enum.shapes import MSO_AUTO_SHAPE_TYPE, MSO_CONNECTOR - -# Vytvorenie novej prezentácie -prs = Presentation() -slide_layout = prs.slide_layouts[5] # Prázdny slide -slide = prs.slides.add_slide(slide_layout) - -# Definícia základných rozmerov a pozícií -left_margin = Inches(0.5) -top_margin = Inches(0.5) -block_width = Inches(3) -block_height = Inches(0.7) -vertical_gap = Inches(0.3) -horizontal_gap = Inches(0.5) - -# Blok 1: Používateľský dotaz & Chat history -box1 = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_margin, top_margin, block_width, block_height) -box1.text = "Používateľský dotaz\n& Chat history" - -# Blok 2: ConversationalAgent (pod box1) -box2_top = top_margin + block_height + vertical_gap -box2 = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_margin, box2_top, block_width, block_height) -box2.text = "ConversationalAgent" - -# Blok 3: Klasifikácia dotazu (pod box2) -box3_top = box2_top + block_height + vertical_gap -box3 = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_margin, box3_top, block_width, block_height) -box3.text = "Klasifikácia dotazu" - -# Vetvenie: Pozície pre dve vetvy -branch_top = box3_top + block_height + vertical_gap - -# Ľavá vetva ("Vyhladavanie") -left_branch_left = left_margin - Inches(0.2) -box4A = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_branch_left, branch_top, block_width, block_height) -box4A.text = "ElasticsearchStore\nvyhľadávanie" - -box5A_top = branch_top + block_height + vertical_gap -box5A = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_branch_left, box5A_top, block_width, block_height) -box5A.text = "Generovanie\ndynamického promptu" - -box6A_top = box5A_top + block_height + vertical_gap -box6A = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_branch_left, box6A_top, block_width, block_height) -box6A.text = "Generovanie\nodpovede" - -box7A_top = box6A_top + block_height + vertical_gap -box7A = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_branch_left, box7A_top, block_width, block_height) -box7A.text = "Finalizácia\nodpovede" - -# Pravá vetva ("Upresnenie") -right_branch_left = left_margin + block_width + horizontal_gap -box4B = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, right_branch_left, branch_top, block_width, block_height) -box4B.text = "Kombinovanie\ndotazov" - -box5B_top = branch_top + block_height + vertical_gap -box5B = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, right_branch_left, box5B_top, block_width, block_height) -box5B.text = "ElasticsearchStore\nvyhľadávanie" - -box6B_top = box5B_top + block_height + vertical_gap -box6B = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, right_branch_left, box6B_top, block_width, block_height) -box6B.text = "Generovanie\ndynamického promptu" - -box7B_top = box6B_top + block_height + vertical_gap -box7B = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, right_branch_left, box7B_top, block_width, block_height) -box7B.text = "Generovanie\nodpovedí (2 modely)" - -box8B_top = box7B_top + block_height + vertical_gap -box8B = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, right_branch_left, box8B_top, block_width, block_height) -box8B.text = "Validácia a\nhodnotenie odpovedí" - -box9B_top = box8B_top + block_height + vertical_gap -box9B = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, right_branch_left, box9B_top, block_width, block_height) -box9B.text = "Finalizácia\nodpovede" - -# Finálny blok: Výstup (zlúčenie vetiev) -final_box_top = max(box7A_top, box9B_top) + block_height + vertical_gap -final_box = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_margin, final_box_top, block_width, block_height) -final_box.text = "Výstup" - -# Funkcia na pridanie šípok medzi blokmi -def add_connector(slide, start_shape, end_shape): - start_x = start_shape.left + start_shape.width / 2 - start_y = start_shape.top + start_shape.height - end_x = end_shape.left + end_shape.width / 2 - end_y = end_shape.top - connector = slide.shapes.add_connector(MSO_CONNECTOR.STRAIGHT, start_x, start_y, end_x, end_y) - # Aktuálna verzia python-pptx nepodporuje nastavenie šípky, preto tento riadok odstraňte alebo zakomentujte: - # connector.line.end_arrowhead.style = 1 - return connector - -# Prepojenie blokov -add_connector(slide, box1, box2) -add_connector(slide, box2, box3) - -# Vetvenie z Box3 do oboch vetiev -mid_point = box3.left + box3.width / 2 -branch_mid_y = box3.top + box3.height + vertical_gap/2 -# Do ľavej vetvy: -connector_left = slide.shapes.add_connector(MSO_CONNECTOR.STRAIGHT, mid_point, box3.top + box3.height, left_branch_left + block_width/2, branch_mid_y) -# Do pravej vetvy: -connector_right = slide.shapes.add_connector(MSO_CONNECTOR.STRAIGHT, mid_point, box3.top + box3.height, right_branch_left + block_width/2, branch_mid_y) - -# Prepojenie blokov v ľavej vetve -add_connector(slide, box4A, box5A) -add_connector(slide, box5A, box6A) -add_connector(slide, box6A, box7A) - -# Prepojenie blokov v pravej vetve -add_connector(slide, box4B, box5B) -add_connector(slide, box5B, box6B) -add_connector(slide, box6B, box7B) -add_connector(slide, box7B, box8B) -add_connector(slide, box8B, box9B) - -# Spojenie oboch vetiev s finálnym blokom "Výstup" -add_connector(slide, box7A, final_box) -add_connector(slide, box9B, final_box) - -# Uloženie prezentácie -prs.save("architecture_diagram.pptx") +from pptx import Presentation +from pptx.util import Inches +from pptx.enum.shapes import MSO_AUTO_SHAPE_TYPE, MSO_CONNECTOR + +# Vytvorenie novej prezentácie +prs = Presentation() +slide_layout = prs.slide_layouts[5] # Prázdny slide +slide = prs.slides.add_slide(slide_layout) + +# Definícia základných rozmerov a pozícií +left_margin = Inches(0.5) +top_margin = Inches(0.5) +block_width = Inches(3) +block_height = Inches(0.7) +vertical_gap = Inches(0.3) +horizontal_gap = Inches(0.5) + +# Blok 1: Používateľský dotaz & Chat history +box1 = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_margin, top_margin, block_width, block_height) +box1.text = "Používateľský dotaz\n& Chat history" + +# Blok 2: ConversationalAgent (pod box1) +box2_top = top_margin + block_height + vertical_gap +box2 = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_margin, box2_top, block_width, block_height) +box2.text = "ConversationalAgent" + +# Blok 3: Klasifikácia dotazu (pod box2) +box3_top = box2_top + block_height + vertical_gap +box3 = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_margin, box3_top, block_width, block_height) +box3.text = "Klasifikácia dotazu" + +# Vetvenie: Pozície pre dve vetvy +branch_top = box3_top + block_height + vertical_gap + +# Ľavá vetva ("Vyhladavanie") +left_branch_left = left_margin - Inches(0.2) +box4A = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_branch_left, branch_top, block_width, block_height) +box4A.text = "ElasticsearchStore\nvyhľadávanie" + +box5A_top = branch_top + block_height + vertical_gap +box5A = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_branch_left, box5A_top, block_width, block_height) +box5A.text = "Generovanie\ndynamického promptu" + +box6A_top = box5A_top + block_height + vertical_gap +box6A = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_branch_left, box6A_top, block_width, block_height) +box6A.text = "Generovanie\nodpovede" + +box7A_top = box6A_top + block_height + vertical_gap +box7A = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_branch_left, box7A_top, block_width, block_height) +box7A.text = "Finalizácia\nodpovede" + +# Pravá vetva ("Upresnenie") +right_branch_left = left_margin + block_width + horizontal_gap +box4B = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, right_branch_left, branch_top, block_width, block_height) +box4B.text = "Kombinovanie\ndotazov" + +box5B_top = branch_top + block_height + vertical_gap +box5B = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, right_branch_left, box5B_top, block_width, block_height) +box5B.text = "ElasticsearchStore\nvyhľadávanie" + +box6B_top = box5B_top + block_height + vertical_gap +box6B = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, right_branch_left, box6B_top, block_width, block_height) +box6B.text = "Generovanie\ndynamického promptu" + +box7B_top = box6B_top + block_height + vertical_gap +box7B = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, right_branch_left, box7B_top, block_width, block_height) +box7B.text = "Generovanie\nodpovedí (2 modely)" + +box8B_top = box7B_top + block_height + vertical_gap +box8B = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, right_branch_left, box8B_top, block_width, block_height) +box8B.text = "Validácia a\nhodnotenie odpovedí" + +box9B_top = box8B_top + block_height + vertical_gap +box9B = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, right_branch_left, box9B_top, block_width, block_height) +box9B.text = "Finalizácia\nodpovede" + +# Finálny blok: Výstup (zlúčenie vetiev) +final_box_top = max(box7A_top, box9B_top) + block_height + vertical_gap +final_box = slide.shapes.add_shape(MSO_AUTO_SHAPE_TYPE.RECTANGLE, left_margin, final_box_top, block_width, block_height) +final_box.text = "Výstup" + +# Funkcia na pridanie šípok medzi blokmi +def add_connector(slide, start_shape, end_shape): + start_x = start_shape.left + start_shape.width / 2 + start_y = start_shape.top + start_shape.height + end_x = end_shape.left + end_shape.width / 2 + end_y = end_shape.top + connector = slide.shapes.add_connector(MSO_CONNECTOR.STRAIGHT, start_x, start_y, end_x, end_y) + # Aktuálna verzia python-pptx nepodporuje nastavenie šípky, preto tento riadok odstraňte alebo zakomentujte: + # connector.line.end_arrowhead.style = 1 + return connector + +# Prepojenie blokov +add_connector(slide, box1, box2) +add_connector(slide, box2, box3) + +# Vetvenie z Box3 do oboch vetiev +mid_point = box3.left + box3.width / 2 +branch_mid_y = box3.top + box3.height + vertical_gap/2 +# Do ľavej vetvy: +connector_left = slide.shapes.add_connector(MSO_CONNECTOR.STRAIGHT, mid_point, box3.top + box3.height, left_branch_left + block_width/2, branch_mid_y) +# Do pravej vetvy: +connector_right = slide.shapes.add_connector(MSO_CONNECTOR.STRAIGHT, mid_point, box3.top + box3.height, right_branch_left + block_width/2, branch_mid_y) + +# Prepojenie blokov v ľavej vetve +add_connector(slide, box4A, box5A) +add_connector(slide, box5A, box6A) +add_connector(slide, box6A, box7A) + +# Prepojenie blokov v pravej vetve +add_connector(slide, box4B, box5B) +add_connector(slide, box5B, box6B) +add_connector(slide, box6B, box7B) +add_connector(slide, box7B, box8B) +add_connector(slide, box8B, box9B) + +# Spojenie oboch vetiev s finálnym blokom "Výstup" +add_connector(slide, box7A, final_box) +add_connector(slide, box9B, final_box) + +# Uloženie prezentácie +prs.save("architecture_diagram.pptx") diff --git a/Backend/config.json b/Backend/config.json index 54e135e..b8193fc 100644 --- a/Backend/config.json +++ b/Backend/config.json @@ -1,3 +1,3 @@ -{ - "useCloud" : false +{ + "useCloud" : false } \ No newline at end of file diff --git a/Backend/index-server-es b/Backend/index-server-es index dca03d0..925934a 100644 --- a/Backend/index-server-es +++ b/Backend/index-server-es @@ -1,80 +1,80 @@ -from elasticsearch import Elasticsearch -from langchain.embeddings import HuggingFaceEmbeddings -from elasticsearch.helpers import bulk -import json - -# Настройка подключения к Elasticsearch с аутентификацией и HTTPS -es = Elasticsearch( - [{'host': 'localhost', 'port': 9200, 'scheme': 'https'}], - http_auth=('elastic', '3lvFhvVYrazLsj=M-R_g'), # замените на ваш пароль - verify_certs=False # Отключить проверку SSL-сертификата, если используется самоподписанный сертификат -) - -embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") - -def create_index(): - # Определяем маппинг для индекса - mapping = { - "mappings": { - "properties": { - "text": { - "type": "text", - "analyzer": "standard" - }, - "vector": { - "type": "dense_vector", - "dims": 384 # Размерность векторного представления - }, - "full_data": { - "type": "object", - "enabled": False # Отключаем индексацию вложенных данных - } - } - } - } - es.indices.create(index='drug_docs', body=mapping, ignore=400) - -def load_drug_data(json_path): - with open(json_path, 'r', encoding='utf-8') as f: - data = json.load(f) - return data - -def index_documents(data): - actions = [] - total_docs = len(data) - for i, item in enumerate(data, start=1): - doc_text = f"{item['link']} {item.get('pribalovy_letak', '')} {item.get('spc', '')}" - - vector = embeddings.embed_query(doc_text) - - action = { - "_index": "drug_docs", - "_id": i, - "_source": { - 'text': doc_text, - 'vector': vector, - 'full_data': item - } - } - actions.append(action) - - # Отображение прогресса - print(f"Индексируется документ {i}/{total_docs}", end='\r') - - # Опционально: индексируем пакетами по N документов - if i % 100 == 0 or i == total_docs: - bulk(es, actions) - actions = [] - - # Если остались неиндексированные документы - if actions: - bulk(es, actions) - - print("\nИндексирование завершено.") - -if __name__ == "__main__": - create_index() - data_path = "../../esDB/cleaned_general_info_additional.json" - drug_data = load_drug_data(data_path) - index_documents(drug_data) - +from elasticsearch import Elasticsearch +from langchain.embeddings import HuggingFaceEmbeddings +from elasticsearch.helpers import bulk +import json + +# Настройка подключения к Elasticsearch с аутентификацией и HTTPS +es = Elasticsearch( + [{'host': 'localhost', 'port': 9200, 'scheme': 'https'}], + http_auth=('elastic', '3lvFhvVYrazLsj=M-R_g'), # замените на ваш пароль + verify_certs=False # Отключить проверку SSL-сертификата, если используется самоподписанный сертификат +) + +embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") + +def create_index(): + # Определяем маппинг для индекса + mapping = { + "mappings": { + "properties": { + "text": { + "type": "text", + "analyzer": "standard" + }, + "vector": { + "type": "dense_vector", + "dims": 384 # Размерность векторного представления + }, + "full_data": { + "type": "object", + "enabled": False # Отключаем индексацию вложенных данных + } + } + } + } + es.indices.create(index='drug_docs', body=mapping, ignore=400) + +def load_drug_data(json_path): + with open(json_path, 'r', encoding='utf-8') as f: + data = json.load(f) + return data + +def index_documents(data): + actions = [] + total_docs = len(data) + for i, item in enumerate(data, start=1): + doc_text = f"{item['link']} {item.get('pribalovy_letak', '')} {item.get('spc', '')}" + + vector = embeddings.embed_query(doc_text) + + action = { + "_index": "drug_docs", + "_id": i, + "_source": { + 'text': doc_text, + 'vector': vector, + 'full_data': item + } + } + actions.append(action) + + # Отображение прогресса + print(f"Индексируется документ {i}/{total_docs}", end='\r') + + # Опционально: индексируем пакетами по N документов + if i % 100 == 0 or i == total_docs: + bulk(es, actions) + actions = [] + + # Если остались неиндексированные документы + if actions: + bulk(es, actions) + + print("\nИндексирование завершено.") + +if __name__ == "__main__": + create_index() + data_path = "../../esDB/cleaned_general_info_additional.json" + drug_data = load_drug_data(data_path) + index_documents(drug_data) + diff --git a/Backend/index-server-es.py b/Backend/index-server-es.py index 10df027..0d5408b 100644 --- a/Backend/index-server-es.py +++ b/Backend/index-server-es.py @@ -1,80 +1,80 @@ -from elasticsearch import Elasticsearch -from langchain.embeddings import HuggingFaceEmbeddings -from elasticsearch.helpers import bulk -import json - -# Настройка подключения к Elasticsearch с аутентификацией и HTTPS -es = Elasticsearch( - [{'host': 'localhost', 'port': 9200, 'scheme': 'https'}], - http_auth=('elastic', 'S7DoO3ma=G=9USBPbqq3'), # замените на ваш пароль - verify_certs=False # Отключить проверку SSL-сертификата, если используется самоподписанный сертификат -) - -embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") - -def create_index(): - # Определяем маппинг для индекса - mapping = { - "mappings": { - "properties": { - "text": { - "type": "text", - "analyzer": "standard" - }, - "vector": { - "type": "dense_vector", - "dims": 384 # Размерность векторного представления - }, - "full_data": { - "type": "object", - "enabled": False # Отключаем индексацию вложенных данных - } - } - } - } - es.indices.create(index='drug_docs', body=mapping, ignore=400) - -def load_drug_data(json_path): - with open(json_path, 'r', encoding='utf-8') as f: - data = json.load(f) - return data - -def index_documents(data): - actions = [] - total_docs = len(data) - for i, item in enumerate(data, start=1): - doc_text = f"{item['link']} {item.get('pribalovy_letak', '')} {item.get('spc', '')}" - - vector = embeddings.embed_query(doc_text) - - action = { - "_index": "drug_docs", - "_id": i, - "_source": { - 'text': doc_text, - 'vector': vector, - 'full_data': item - } - } - actions.append(action) - - # Отображение прогресса - print(f"Индексируется документ {i}/{total_docs}", end='\r') - - # Опционально: индексируем пакетами по N документов - if i % 100 == 0 or i == total_docs: - bulk(es, actions) - actions = [] - - # Если остались неиндексированные документы - if actions: - bulk(es, actions) - - print("\nИндексирование завершено.") - -if __name__ == "__main__": - create_index() - data_path = "/home/poiasnik/esDB/cleaned_general_info_additional.json" - drug_data = load_drug_data(data_path) - index_documents(drug_data) - +from elasticsearch import Elasticsearch +from langchain.embeddings import HuggingFaceEmbeddings +from elasticsearch.helpers import bulk +import json + +# Настройка подключения к Elasticsearch с аутентификацией и HTTPS +es = Elasticsearch( + [{'host': 'localhost', 'port': 9200, 'scheme': 'https'}], + http_auth=('elastic', 'S7DoO3ma=G=9USBPbqq3'), # замените на ваш пароль + verify_certs=False # Отключить проверку SSL-сертификата, если используется самоподписанный сертификат +) + +embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") + +def create_index(): + # Определяем маппинг для индекса + mapping = { + "mappings": { + "properties": { + "text": { + "type": "text", + "analyzer": "standard" + }, + "vector": { + "type": "dense_vector", + "dims": 384 # Размерность векторного представления + }, + "full_data": { + "type": "object", + "enabled": False # Отключаем индексацию вложенных данных + } + } + } + } + es.indices.create(index='drug_docs', body=mapping, ignore=400) + +def load_drug_data(json_path): + with open(json_path, 'r', encoding='utf-8') as f: + data = json.load(f) + return data + +def index_documents(data): + actions = [] + total_docs = len(data) + for i, item in enumerate(data, start=1): + doc_text = f"{item['link']} {item.get('pribalovy_letak', '')} {item.get('spc', '')}" + + vector = embeddings.embed_query(doc_text) + + action = { + "_index": "drug_docs", + "_id": i, + "_source": { + 'text': doc_text, + 'vector': vector, + 'full_data': item + } + } + actions.append(action) + + # Отображение прогресса + print(f"Индексируется документ {i}/{total_docs}", end='\r') + + # Опционально: индексируем пакетами по N документов + if i % 100 == 0 or i == total_docs: + bulk(es, actions) + actions = [] + + # Если остались неиндексированные документы + if actions: + bulk(es, actions) + + print("\nИндексирование завершено.") + +if __name__ == "__main__": + create_index() + data_path = "/home/poiasnik/esDB/cleaned_general_info_additional.json" + drug_data = load_drug_data(data_path) + index_documents(drug_data) + diff --git a/Backend/indexCloud.py b/Backend/indexCloud.py index 32e2665..1c3b033 100644 --- a/Backend/indexCloud.py +++ b/Backend/indexCloud.py @@ -1,41 +1,41 @@ -from elasticsearch import Elasticsearch -from langchain_huggingface import HuggingFaceEmbeddings -import json -import sys - -es = Elasticsearch( - cloud_id="tt:dXMtZWFzdC0yLmF3cy5lbGFzdGljLWNsb3VkLmNvbTo0NDMkOGM3ODQ0ZWVhZTEyNGY3NmFjNjQyNDFhNjI4NmVhYzMkZTI3YjlkNTQ0ODdhNGViNmEyMTcxMjMxNmJhMWI0ZGU=", - basic_auth=("elastic", "sSz2BEGv56JRNjGFwoQ191RJ") -) - -embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") - - -def load_drug_data(json_path): - with open(json_path, 'r', encoding='utf-8') as f: - data = json.load(f) - return data - - -def index_documents(data): - total_documents = len(data) - for i, item in enumerate(data, start=1): - doc_text = f"{item['link']} {item.get('pribalovy_letak', '')} {item.get('spc', '')}" - - vector = embeddings.embed_query(doc_text) - - es.index(index='drug_docs', id=i, body={ - 'text': doc_text, - 'vector': vector, - 'full_data': item - }) - - - sys.stdout.flush() - - - - -data_path = "../../data_adc_databaza/cleaned_general_info_additional.json" -drug_data = load_drug_data(data_path) -index_documents(drug_data) +from elasticsearch import Elasticsearch +from langchain_huggingface import HuggingFaceEmbeddings +import json +import sys + +es = Elasticsearch( + cloud_id="tt:dXMtZWFzdC0yLmF3cy5lbGFzdGljLWNsb3VkLmNvbTo0NDMkOGM3ODQ0ZWVhZTEyNGY3NmFjNjQyNDFhNjI4NmVhYzMkZTI3YjlkNTQ0ODdhNGViNmEyMTcxMjMxNmJhMWI0ZGU=", + basic_auth=("elastic", "sSz2BEGv56JRNjGFwoQ191RJ") +) + +embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") + + +def load_drug_data(json_path): + with open(json_path, 'r', encoding='utf-8') as f: + data = json.load(f) + return data + + +def index_documents(data): + total_documents = len(data) + for i, item in enumerate(data, start=1): + doc_text = f"{item['link']} {item.get('pribalovy_letak', '')} {item.get('spc', '')}" + + vector = embeddings.embed_query(doc_text) + + es.index(index='drug_docs', id=i, body={ + 'text': doc_text, + 'vector': vector, + 'full_data': item + }) + + + sys.stdout.flush() + + + + +data_path = "../../data_adc_databaza/cleaned_general_info_additional.json" +drug_data = load_drug_data(data_path) +index_documents(drug_data) diff --git a/Backend/index_JSON.py b/Backend/index_JSON.py index c6230c6..3a59ffd 100644 --- a/Backend/index_JSON.py +++ b/Backend/index_JSON.py @@ -1,70 +1,70 @@ -from elasticsearch import Elasticsearch -from langchain_huggingface import HuggingFaceEmbeddings -from elasticsearch.helpers import bulk -import json - -es = Elasticsearch([{'host': 'localhost', 'port': 9200, 'scheme': 'http'}]) - -embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") - -def create_index(): - - mapping = { - "mappings": { - "properties": { - "text": { - "type": "text", - "analyzer": "standard" - }, - "vector": { - "type": "dense_vector", - "dims": 384 - }, - "full_data": { - "type": "object", - "enabled": False - } - } - } - } - es.indices.create(index='drug_docs', body=mapping, ignore=400) - -def load_drug_data(json_path): - with open(json_path, 'r', encoding='utf-8') as f: - data = json.load(f) - return data - -def index_documents(data): - actions = [] - total_docs = len(data) - for i, item in enumerate(data, start=1): - doc_text = f"{item['link']} {item.get('pribalovy_letak', '')} {item.get('spc', '')}" - - vector = embeddings.embed_query(doc_text) - - action = { - "_index": "drug_docs", - "_id": i, - "_source": { - 'text': doc_text, - 'vector': vector, - 'full_data': item - } - } - actions.append(action) - - - - if i % 100 == 0 or i == total_docs: - bulk(es, actions) - actions = [] - - if actions: - bulk(es, actions) - - -if __name__ == "__main__": - create_index() - data_path = "../../data_adc_databaza/cleaned_general_info_additional.json" - drug_data = load_drug_data(data_path) - index_documents(drug_data) +from elasticsearch import Elasticsearch +from langchain_huggingface import HuggingFaceEmbeddings +from elasticsearch.helpers import bulk +import json + +es = Elasticsearch([{'host': 'localhost', 'port': 9200, 'scheme': 'http'}]) + +embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") + +def create_index(): + + mapping = { + "mappings": { + "properties": { + "text": { + "type": "text", + "analyzer": "standard" + }, + "vector": { + "type": "dense_vector", + "dims": 384 + }, + "full_data": { + "type": "object", + "enabled": False + } + } + } + } + es.indices.create(index='drug_docs', body=mapping, ignore=400) + +def load_drug_data(json_path): + with open(json_path, 'r', encoding='utf-8') as f: + data = json.load(f) + return data + +def index_documents(data): + actions = [] + total_docs = len(data) + for i, item in enumerate(data, start=1): + doc_text = f"{item['link']} {item.get('pribalovy_letak', '')} {item.get('spc', '')}" + + vector = embeddings.embed_query(doc_text) + + action = { + "_index": "drug_docs", + "_id": i, + "_source": { + 'text': doc_text, + 'vector': vector, + 'full_data': item + } + } + actions.append(action) + + + + if i % 100 == 0 or i == total_docs: + bulk(es, actions) + actions = [] + + if actions: + bulk(es, actions) + + +if __name__ == "__main__": + create_index() + data_path = "../../data_adc_databaza/cleaned_general_info_additional.json" + drug_data = load_drug_data(data_path) + index_documents(drug_data) diff --git a/Backend/model.py b/Backend/model.py index 8bae8ef..590a666 100644 --- a/Backend/model.py +++ b/Backend/model.py @@ -1,541 +1,541 @@ -import json -import requests -import logging -import time -import re -from requests.exceptions import HTTPError -from elasticsearch import Elasticsearch -from langchain_huggingface import HuggingFaceEmbeddings -from langchain_elasticsearch import ElasticsearchStore -from langchain.text_splitter import RecursiveCharacterTextSplitter -import psycopg2 -from psycopg2.extras import RealDictCursor - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -config_file_path = "config.json" -with open(config_file_path, 'r') as config_file: - config = json.load(config_file) - -mistral_api_key = "hXDC4RBJk1qy5pOlrgr01GtOlmyCBaNs" -if not mistral_api_key: - raise ValueError("Mistral API key not found in configuration.") - - -############################################################################### -# Simple functions for translation (stub) -############################################################################### -def translate_to_slovak(text: str) -> str: - return text - - -def translate_preserving_medicine_names(text: str) -> str: - return text - - -############################################################################### -# Function for evaluating the completeness of the answer -############################################################################### -def evaluate_complete_answer(query: str, answer: str) -> dict: - evaluation_prompt = ( - f"Vyhodnoť nasledujúcu odpoveď na základe týchto kritérií:\n" - f"1. Odpoveď obsahuje odporúčania liekov vrátane názvu, stručného vysvetlenia a, ak bolo žiadané, aj dávkovanie alebo čas užívania.\n" - f"2. Ak otázka obsahovala dodatočné požiadavky, odpoveď má samostatnú časť, ktorá tieto požiadavky rieši.\n\n" - f"Otázka: '{query}'\n" - f"Odpoveď: '{answer}'\n\n" - "Na základe týchto kritérií daj odpovedi hodnotenie od 0 do 10, kde 10 znamená, že odpoveď je úplne logická a obsahuje všetky požadované informácie. " - "Vráť len číslo." - ) - score_str = llm_small.generate_text(prompt=evaluation_prompt, max_tokens=50, temperature=0.3) - try: - score = float(score_str.strip()) - except Exception as e: - logger.error(f"Error parsing evaluation score: {e}") - score = 0.0 - return {"rating": round(score, 2), "explanation": "Evaluation based on required criteria."} - - -############################################################################### -# Function for validating the response logic -############################################################################### -def validate_answer_logic(query: str, answer: str) -> str: - validation_prompt = ( - f"Otázka: '{query}'\n" - f"Odpoveď: '{answer}'\n\n" - "Analyzuj prosím túto odpoveď. Ak odpoveď neobsahuje všetky dodatočné informácie, na ktoré sa pýtal používateľ, " - "alebo ak odporúčania liekov nie sú úplné (napr. chýba dávkovanie alebo čas užívania, ak boli takéto požiadavky v otázke), " - "vytvor opravenú odpoveď, ktorá je logicky konzistentná s otázkou. " - "Odpovedz v slovenčine a iba čistou, konečnou odpoveďou bez ďalších komentárov." - ) - try: - validated_answer = llm_small.generate_text(prompt=validation_prompt, max_tokens=800, temperature=0.5) - logger.info(f"Validated answer: {validated_answer}") - return validated_answer - except Exception as e: - logger.error(f"Error during answer validation: {e}") - return answer - - -############################################################################### -# Function for creating a dynamic prompt with information from documents -############################################################################### -def build_dynamic_prompt(query: str, documents: list) -> str: - documents_str = "\n".join(documents) - prompt = ( - f"Otázka: '{query}'.\n" - "Na základe nasledujúcich informácií o liekoch:\n" - f"{documents_str}\n\n" - "Analyzuj uvedenú otázku a zisti, či obsahuje dodatočné požiadavky okrem odporúčania liekov. " - "Ak áno, v odpovedi najprv uveď odporúčané lieky – pre každý liek uveď jeho názov, stručné vysvetlenie a, ak je to relevantné, " - "odporúčané dávkovanie alebo čas užívania, a potom v ďalšej časti poskytn ú odpoveď na dodatočné požiadavky. " - "Odpovedaj priamo a ľudským, priateľským tónom v číslovanom zozname, bez zbytočných úvodných fráz. " - "Odpoveď musí byť v slovenčine. " - "Prosím, odpovedaj v priateľskom, zdvorilom a profesionálnom tóne, bez akýchkoľvek agresívnych či drzých výrazov." - ) - return prompt - - -############################################################################### -# Function to get user data from the database via endpoint /api/get_user_data -############################################################################### -def get_user_data_from_db(chat_id: str) -> str: - try: - response = requests.get("http://localhost:5000/api/get_user_data", params={"chatId": chat_id}) - if response.status_code == 200: - data = response.json() - return data.get("user_data", "") - else: - logger.warning(f"Nezískané user_data, status: {response.status_code}") - except Exception as e: - logger.error(f"Error retrieving user_data from DB: {e}", exc_info=True) - return "" - - -############################################################################### -# Class for calling Mistral LLM -############################################################################### -class CustomMistralLLM: - def __init__(self, api_key: str, endpoint_url: str, model_name: str): - self.api_key = api_key - self.endpoint_url = endpoint_url - self.model_name = model_name - - def generate_text(self, prompt: str, max_tokens=812, temperature=0.7, retries=3, delay=2): - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } - payload = { - "model": self.model_name, - "messages": [{"role": "user", "content": prompt}], - "max_tokens": max_tokens, - "temperature": temperature - } - attempt = 0 - while attempt < retries: - try: - response = requests.post(self.endpoint_url, headers=headers, json=payload) - response.raise_for_status() - result = response.json() - logger.info(f"Full response from model {self.model_name}: {result}") - return result.get("choices", [{}])[0].get("message", {}).get("content", "No response") - except HTTPError as e: - if response.status_code == 429: - logger.warning(f"Rate limit exceeded. Waiting {delay} seconds before retry.") - time.sleep(delay) - attempt += 1 - else: - logger.error(f"HTTP Error: {e}") - raise e - except Exception as ex: - logger.error(f"Error: {str(ex)}") - raise ex - raise Exception("Reached maximum number of retries for API request") - - -############################################################################### -# Function for generating a detailed evaluation description -############################################################################### -# def detailed_evaluation_description(query: str, answer: str, rating: float) -> str: -# prompt = ( -# f"Podrobne opíš, prečo odpoveď: '{answer}' na otázku: '{query}' dosiahla hodnotenie {rating} zo 10. " -# "Uveď relevantné aspekty, ktoré ovplyvnili toto hodnotenie, vrátane úplnosti, presnosti a kvality vysvetlenia." -# ) -# description = llm_small.generate_text(prompt=prompt, max_tokens=150, temperature=0.5) -# return description.strip() -# -# Ak chcete vidieť podrobné hodnotenie, odkomentujte funkciu detailed_evaluation_description a príslušné časti kódu. - - -############################################################################### -# Initialisation of Embeddings and Elasticsearch -############################################################################### -logger.info("Loading HuggingFaceEmbeddings model...") -embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") - -index_name = "drug_docs" -if config.get("useCloud", False): - logger.info("Using cloud Elasticsearch.") - cloud_id = "tt:dXMtZWFzdC0yLmF3cy5lbGFzdGljLWNsb3VkLmNvbTo0NDMkOGM3ODQ0ZWVhZTEyNGY3NmFjNjQyNDFhNjI4NmVhYzMkZTI3YjlkNTQ0ODdhNGViNmEyMTcxMjMxNmJhMWI0ZGU=" - vectorstore = ElasticsearchStore( - es_cloud_id=cloud_id, - index_name=index_name, - embedding=embeddings, - es_user="elastic", - es_password="sSz2BEGv56JRNjGFwoQ191RJ" - ) -else: - logger.info("Using local Elasticsearch.") - vectorstore = ElasticsearchStore( - es_url="http://elasticsearch:9200", - index_name=index_name, - embedding=embeddings, - ) - -logger.info("Connected to Elasticsearch.") - -############################################################################### -# Initialisation of LLM small & large -############################################################################### -llm_small = CustomMistralLLM( - api_key=mistral_api_key, - endpoint_url="https://api.mistral.ai/v1/chat/completions", - model_name="mistral-small-latest" -) -llm_large = CustomMistralLLM( - api_key=mistral_api_key, - endpoint_url="https://api.mistral.ai/v1/chat/completions", - model_name="mistral-large-latest" -) - - -############################################################################### -# Request classification function: vyhladavanie vs. upresnenie -############################################################################### -def classify_query(query: str, chat_history: str = "") -> str: - if not chat_history.strip(): - return "vyhladavanie" - prompt = ( - "Ty si zdravotnícky expert, ktorý analyzuje otázky používateľov. " - "Analyzuj nasledujúci dopyt a urči, či ide o dopyt na vyhľadanie liekov alebo " - "o upresnenie/doplnenie už poskytnutej odpovede.\n" - "Ak dopyt obsahuje výrazy ako 'čo pit', 'aké lieky', 'odporuč liek', 'hľadám liek', " - "odpovedaj slovom 'vyhľadávanie'.\n" - "Ak dopyt slúži na upresnenie, napríklad obsahuje výrazy ako 'a nie na predpis', " - "'upresni', 'este raz', odpovedaj slovom 'upresnenie'.\n" - f"Dopyt: \"{query}\"" - ) - classification = llm_small.generate_text(prompt=prompt, max_tokens=20, temperature=0.3) - classification = classification.strip().lower() - logger.info(f"Klasifikácia dopytu: {classification}") - if "vyhládzanie" in classification or "vyhľadávanie" in classification: - return "vyhladavanie" - elif "upresnenie" in classification: - return "upresnenie" - return "vyhladavanie" - - -############################################################################### -# Template for upresnenie dopytu -############################################################################### -def build_upresnenie_prompt_no_history(chat_history: str, user_query: str) -> str: - prompt = f""" -Ty si zdravotnícky expert. Máš k dispozícii históriu chatu a novú upresňujúcu otázku. - -Ak v histórii chatu už existuje jasná odpoveď na túto upresňujúcu otázku, napíš: -"FOUND_IN_HISTORY: <ľudský vysvetľajúci text>" - -Ak však v histórii chatu nie je dostatok informácií, napíš: -"NO_ANSWER_IN_HISTORY: " - -V časti zahrň kľúčové slová z pôvodnej otázky aj z upresnenia. - -=== ZAČIATOK HISTÓRIE CHatu === -{chat_history} -=== KONIEC HISTÓRIE CHatu === - -Upresňujúca otázka od používateľa: -"{user_query}" -""" - return prompt - - -############################################################################### -# Function for retrieving the last vyhladavacieho dopytu z histórie -############################################################################### -def extract_last_vyhladavacie_query(chat_history: str) -> str: - lines = chat_history.splitlines() - last_query = "" - for line in reversed(lines): - if line.startswith("User:"): - last_query = line[len("User:"):].strip() - break - return last_query - - -############################################################################### -# Agent class for data storage: vek, anamneza, predpis, user_data, search_query -############################################################################### -class ConversationalAgent: - def __init__(self): - self.long_term_memory = { - "vek": None, - "anamneza": None, - "predpis": None, - "user_data": None, - "search_query": None - } - - def update_memory(self, key, value): - self.long_term_memory[key] = value - - def get_memory(self, key): - return self.long_term_memory.get(key, None) - - def load_memory_from_history(self, chat_history: str): - memory_match = re.search(r"\[MEMORY\](.*?)\[/MEMORY\]", chat_history, re.DOTALL) - if memory_match: - try: - memory_data = json.loads(memory_match.group(1)) - self.long_term_memory.update(memory_data) - logger.info(f"Nahraná pamäť z histórie: {self.long_term_memory}") - except Exception as e: - logger.error(f"Chyba pri načítaní pamäte: {e}") - - def parse_user_info(self, query: str): - text_lower = query.lower() - if re.search(r"\d+\s*(rok(ov|y)?|years?)", text_lower): - self.update_memory("user_data", query) - age_match = re.search(r"(\d{1,3})\s*(rok(ov|y)?|years?)", text_lower) - if age_match: - self.update_memory("vek", age_match.group(1)) - if ("nemá" in text_lower or "nema" in text_lower) and ("chronické" in text_lower or "alerg" in text_lower): - self.update_memory("anamneza", "Žiadne chronické ochorenia ani alergie") - elif (("chronické" in text_lower or "alerg" in text_lower) and ("má" in text_lower or "ma" in text_lower)): - self.update_memory("anamneza", "Má chronické ochorenie alebo alergie (nespecifikované)") - if "voľnopredajný" in text_lower: - self.update_memory("predpis", "volnopredajny") - elif "na predpis" in text_lower: - self.update_memory("predpis", "na predpis") - - def analyze_input(self, query: str) -> dict: - self.parse_user_info(query) - missing_info = {} - if not self.get_memory("vek"): - missing_info["vek"] = "Prosím, uveďte vek pacienta." - if not self.get_memory("anamneza"): - missing_info["anamnéza"] = "Má pacient nejaké chronické ochorenia alebo alergie?" - if not self.get_memory("predpis"): - missing_info["predpis"] = "Ide o liek na predpis alebo voľnopredajný liek?" - return missing_info - - def ask_follow_up(self, missing_info: dict) -> str: - return " ".join(missing_info.values()) - - -############################################################################### -# Main function process_query_with_mistral with updated logic -############################################################################### -CHAT_HISTORY_ENDPOINT = "http://localhost:5000/api/chat_history_detail" - - -def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10): - logger.info("Processing query started.") - - chat_history = "" - if chat_context: - chat_history = chat_context - elif chat_id: - try: - params = {"id": chat_id} - r = requests.get(CHAT_HISTORY_ENDPOINT, params=params) - if r.status_code == 200: - data = r.json() - chat_data = data.get("chat", "") - if isinstance(chat_data, dict): - chat_history = chat_data.get("chat", "") - else: - chat_history = chat_data or "" - logger.info(f"História chatu načítaná pre chatId: {chat_id}") - else: - logger.warning(f"Nepodarilo sa načítať históriu (status {r.status_code}) pre chatId: {chat_id}") - except Exception as e: - logger.error(f"Chyba pri načítaní histórie: {e}") - - agent = ConversationalAgent() - if chat_history: - agent.load_memory_from_history(chat_history) - - existing_user_data = "" - if chat_id: - existing_user_data = get_user_data_from_db(chat_id) - - agent.parse_user_info(query) - missing_info = agent.analyze_input(query) - - if not existing_user_data: - if "Prosím, uveďte vek pacienta" in chat_history: - if chat_id: - update_payload = {"chatId": chat_id, "userData": query} - try: - update_response = requests.post("http://localhost:5000/api/save_user_data", json=update_payload) - if update_response.status_code == 200: - logger.info("User data was successfully updated via endpoint /api/save_user_data (data question flag).") - else: - logger.warning(f"Failed to update data (data question flag): {update_response.text}") - except Exception as e: - logger.error(f"Error when updating user_data via endpoint (data question flag): {e}") - - if missing_info: - logger.info(f"Chýbajúce informácie: {missing_info}") - combined_missing_text = " ".join(missing_info.values()) - if query.strip() not in combined_missing_text: - if chat_id: - update_payload = {"chatId": chat_id, "userData": query} - try: - update_response = requests.post("http://localhost:5000/api/save_user_data", json=update_payload) - if update_response.status_code == 200: - logger.info("User data was successfully updated via endpoint /api/save_user_data.") - else: - logger.warning(f"Failed to update the data: {update_response.text}") - except Exception as e: - logger.error(f"Error when updating user_data via endpoint: {e}") - return { - "best_answer": combined_missing_text, - "model": "FollowUp (new chat)", - "rating": 0, - "explanation": "Additional data pre pokračovanie is required.", - "patient_data": query - } - - qtype = classify_query(query, chat_history) - logger.info(f"Typ dopytu: {qtype}") - logger.info(f"Chat context (snippet): {chat_history[:200]}...") - - if qtype == "vyhladavanie": - user_data_db = get_user_data_from_db(chat_id) - if user_data_db: - query = query + " Udaje cloveka: " + user_data_db - agent.long_term_memory["search_query"] = query - - if qtype == "upresnenie": - original_search = agent.long_term_memory.get("search_query") - if not original_search: - original_search = extract_last_vyhladavacie_query(chat_history) - if original_search is None: - original_search = "" - combined_query = (original_search + " " + query).strip() - user_data_db = get_user_data_from_db(chat_id) - if user_data_db: - combined_query += " Udaje cloveka: " + user_data_db - logger.info(f"Combined query for search: {combined_query}") - - upres_prompt = build_upresnenie_prompt_no_history(chat_history, combined_query) - response_str = llm_small.generate_text(upres_prompt, max_tokens=1200, temperature=0.5) - normalized = response_str.strip() - logger.info(f"Upresnenie prompt response: {normalized}") - - if re.match(r"(?i)^found_in_history:\s*", normalized): - logger.info("Zistený FOUND_IN_HISTORY – vykonávame vyhľadávanie s kombinovaným dopytom.") - elif re.match(r"(?i)^no_answer_in_history:\s*", normalized): - parts = re.split(r"(?i)^no_answer_in_history:\s*", normalized, maxsplit=1) - if len(parts) >= 2: - combined_query = parts[1].strip() - logger.info(f"Upravený vyhľadávací dopyт z NO_ANSWER_IN_HISTORY: {combined_query}") - - vector_results = vectorstore.similarity_search(combined_query, k=k) - max_docs = 5 - max_len = 1000 - vector_docs = [hit.metadata.get("text", "")[:max_len] for hit in vector_results[:max_docs]] - if not vector_docs: - return { - "best_answer": "Ľutujem, nenašli sa žiadne relevantné informácie.", - "model": "Upresnenie-NoResults", - "rating": 0, - "explanation": "No results from search." - } - joined_docs = "\n".join(vector_docs) - final_prompt = ( - f"Otázka: {combined_query}\n\n" - "Na základe týchto informácií:\n" - f"{joined_docs}\n\n" - "Vygeneruj odporúčanie liekov alebo vysvetlenie, ak je to relevantné.\n" - "Prosím, odpovedaj stručne a dostatočne, bez nadmernej dĺžky." - ) - ans_small = llm_small.generate_text(final_prompt, max_tokens=1200, temperature=0.7) - ans_large = llm_large.generate_text(final_prompt, max_tokens=1200, temperature=0.7) - val_small = validate_answer_logic(combined_query, ans_small) - val_large = validate_answer_logic(combined_query, ans_large) - eval_small = evaluate_complete_answer(combined_query, val_small) - eval_large = evaluate_complete_answer(combined_query, val_large) - candidates = [ - {"summary": val_small, "eval": eval_small, "model": "Mistral Small"}, - {"summary": val_large, "eval": eval_large, "model": "Mistral Large"}, - ] - - # - # for candidate in candidates: - # detailed_desc = detailed_evaluation_description(combined_query, candidate["summary"], candidate["eval"]["rating"]) - # candidate["eval"]["detailed_description"] = detailed_desc - # - - - best = max(candidates, key=lambda x: x["eval"]["rating"]) - logger.info(f"Odpoveď od modelu {best['model']} má rating: {best['eval']['rating']}/10") - - - evaluation_table = "=== Výsledky hodnotenia odpovedí ===\n" - evaluation_table += "{:<15} | {:<6} | {:<60}\n".format("Model", "Rating", "Evaluated Text") - evaluation_table += "-" * 100 + "\n" - for candidate in candidates: - model_name = candidate["model"] - rating = candidate["eval"]["rating"] - evaluated_text = candidate["summary"].replace("\n", " ") - evaluation_table += "{:<15} | {:<6} | {:<60}\n".format(model_name, rating, evaluated_text) - evaluation_table += "=" * 100 + "\n" - - # with open("evaluation.txt", "w", encoding="utf-8") as f: - # f.write(evaluation_table) - # logger.info("Evaluation table записана в evaluation.txt") - - final_answer = translate_preserving_medicine_names(best["summary"]) - memory_json = json.dumps(agent.long_term_memory) - memory_block = f"[MEMORY]{memory_json}[/MEMORY]" - final_answer_with_memory = final_answer + "\n\n" - return { - "best_answer": final_answer_with_memory, - "model": best["model"], - "rating": best["eval"]["rating"], - "explanation": best["eval"]["explanation"] - } - - vector_results = vectorstore.similarity_search(query, k=k) - max_docs = 5 - max_len = 1000 - vector_docs = [hit.metadata.get("text", "")[:max_len] for hit in vector_results[:max_docs]] - if not vector_docs: - return { - "best_answer": "Ľutujem, nenašli sa žiadne relevantné informácie.", - "model": "Vyhladavanie-NoDocs", - "rating": 0, - "explanation": "No results" - } - joined_docs = "\n".join(vector_docs) - final_prompt = ( - f"Otázka: {query}\n\n" - "Na základe týchto informácií:\n" - f"{joined_docs}\n\n" - "Vygeneruj odporúčanie liekov alebo vysvetlenie, ak je to relevantné.\n" - "Prosím, odpovedaj stručne a dostatočne, bez nadmernej dĺžky." - ) - answer = llm_small.generate_text(final_prompt, max_tokens=1200, temperature=0.7) - memory_json = json.dumps(agent.long_term_memory) - memory_block = f"[MEMORY]{memory_json}[/MEMORY]" - answer_with_memory = answer + "\n\n" - return { - "best_answer": answer_with_memory, - "model": "Vyhladavanie-Final", - "rating": 9, - "explanation": "Vyhľadávacia cesta" - } +import json +import requests +import logging +import time +import re +from requests.exceptions import HTTPError +from elasticsearch import Elasticsearch +from langchain_huggingface import HuggingFaceEmbeddings +from langchain_elasticsearch import ElasticsearchStore +from langchain.text_splitter import RecursiveCharacterTextSplitter +import psycopg2 +from psycopg2.extras import RealDictCursor + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +config_file_path = "config.json" +with open(config_file_path, 'r') as config_file: + config = json.load(config_file) + +mistral_api_key = "hXDC4RBJk1qy5pOlrgr01GtOlmyCBaNs" +if not mistral_api_key: + raise ValueError("Mistral API key not found in configuration.") + + +############################################################################### +# Simple functions for translation (stub) +############################################################################### +def translate_to_slovak(text: str) -> str: + return text + + +def translate_preserving_medicine_names(text: str) -> str: + return text + + +############################################################################### +# Function for evaluating the completeness of the answer +############################################################################### +def evaluate_complete_answer(query: str, answer: str) -> dict: + evaluation_prompt = ( + f"Vyhodnoť nasledujúcu odpoveď na základe týchto kritérií:\n" + f"1. Odpoveď obsahuje odporúčania liekov vrátane názvu, stručného vysvetlenia a, ak bolo žiadané, aj dávkovanie alebo čas užívania.\n" + f"2. Ak otázka obsahovala dodatočné požiadavky, odpoveď má samostatnú časť, ktorá tieto požiadavky rieši.\n\n" + f"Otázka: '{query}'\n" + f"Odpoveď: '{answer}'\n\n" + "Na základe týchto kritérií daj odpovedi hodnotenie od 0 do 10, kde 10 znamená, že odpoveď je úplne logická a obsahuje všetky požadované informácie. " + "Vráť len číslo." + ) + score_str = llm_small.generate_text(prompt=evaluation_prompt, max_tokens=50, temperature=0.3) + try: + score = float(score_str.strip()) + except Exception as e: + logger.error(f"Error parsing evaluation score: {e}") + score = 0.0 + return {"rating": round(score, 2), "explanation": "Evaluation based on required criteria."} + + +############################################################################### +# Function for validating the response logic +############################################################################### +def validate_answer_logic(query: str, answer: str) -> str: + validation_prompt = ( + f"Otázka: '{query}'\n" + f"Odpoveď: '{answer}'\n\n" + "Analyzuj prosím túto odpoveď. Ak odpoveď neobsahuje všetky dodatočné informácie, na ktoré sa pýtal používateľ, " + "alebo ak odporúčania liekov nie sú úplné (napr. chýba dávkovanie alebo čas užívania, ak boli takéto požiadavky v otázke), " + "vytvor opravenú odpoveď, ktorá je logicky konzistentná s otázkou. " + "Odpovedz v slovenčine a iba čistou, konečnou odpoveďou bez ďalších komentárov." + ) + try: + validated_answer = llm_small.generate_text(prompt=validation_prompt, max_tokens=800, temperature=0.5) + logger.info(f"Validated answer: {validated_answer}") + return validated_answer + except Exception as e: + logger.error(f"Error during answer validation: {e}") + return answer + + +############################################################################### +# Function for creating a dynamic prompt with information from documents +############################################################################### +def build_dynamic_prompt(query: str, documents: list) -> str: + documents_str = "\n".join(documents) + prompt = ( + f"Otázka: '{query}'.\n" + "Na základe nasledujúcich informácií o liekoch:\n" + f"{documents_str}\n\n" + "Analyzuj uvedenú otázku a zisti, či obsahuje dodatočné požiadavky okrem odporúčania liekov. " + "Ak áno, v odpovedi najprv uveď odporúčané lieky – pre každý liek uveď jeho názov, stručné vysvetlenie a, ak je to relevantné, " + "odporúčané dávkovanie alebo čas užívania, a potom v ďalšej časti poskytn ú odpoveď na dodatočné požiadavky. " + "Odpovedaj priamo a ľudským, priateľským tónom v číslovanom zozname, bez zbytočných úvodných fráz. " + "Odpoveď musí byť v slovenčine. " + "Prosím, odpovedaj v priateľskom, zdvorilom a profesionálnom tóne, bez akýchkoľvek agresívnych či drzých výrazov." + ) + return prompt + + +############################################################################### +# Function to get user data from the database via endpoint /api/get_user_data +############################################################################### +def get_user_data_from_db(chat_id: str) -> str: + try: + response = requests.get("http://localhost:5000/api/get_user_data", params={"chatId": chat_id}) + if response.status_code == 200: + data = response.json() + return data.get("user_data", "") + else: + logger.warning(f"Nezískané user_data, status: {response.status_code}") + except Exception as e: + logger.error(f"Error retrieving user_data from DB: {e}", exc_info=True) + return "" + + +############################################################################### +# Class for calling Mistral LLM +############################################################################### +class CustomMistralLLM: + def __init__(self, api_key: str, endpoint_url: str, model_name: str): + self.api_key = api_key + self.endpoint_url = endpoint_url + self.model_name = model_name + + def generate_text(self, prompt: str, max_tokens=812, temperature=0.7, retries=3, delay=2): + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + payload = { + "model": self.model_name, + "messages": [{"role": "user", "content": prompt}], + "max_tokens": max_tokens, + "temperature": temperature + } + attempt = 0 + while attempt < retries: + try: + response = requests.post(self.endpoint_url, headers=headers, json=payload) + response.raise_for_status() + result = response.json() + logger.info(f"Full response from model {self.model_name}: {result}") + return result.get("choices", [{}])[0].get("message", {}).get("content", "No response") + except HTTPError as e: + if response.status_code == 429: + logger.warning(f"Rate limit exceeded. Waiting {delay} seconds before retry.") + time.sleep(delay) + attempt += 1 + else: + logger.error(f"HTTP Error: {e}") + raise e + except Exception as ex: + logger.error(f"Error: {str(ex)}") + raise ex + raise Exception("Reached maximum number of retries for API request") + + +############################################################################### +# Function for generating a detailed evaluation description +############################################################################### +# def detailed_evaluation_description(query: str, answer: str, rating: float) -> str: +# prompt = ( +# f"Podrobne opíš, prečo odpoveď: '{answer}' na otázku: '{query}' dosiahla hodnotenie {rating} zo 10. " +# "Uveď relevantné aspekty, ktoré ovplyvnili toto hodnotenie, vrátane úplnosti, presnosti a kvality vysvetlenia." +# ) +# description = llm_small.generate_text(prompt=prompt, max_tokens=150, temperature=0.5) +# return description.strip() +# +# Ak chcete vidieť podrobné hodnotenie, odkomentujte funkciu detailed_evaluation_description a príslušné časti kódu. + + +############################################################################### +# Initialisation of Embeddings and Elasticsearch +############################################################################### +logger.info("Loading HuggingFaceEmbeddings model...") +embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") + +index_name = "drug_docs" +if config.get("useCloud", False): + logger.info("Using cloud Elasticsearch.") + cloud_id = "tt:dXMtZWFzdC0yLmF3cy5lbGFzdGljLWNsb3VkLmNvbTo0NDMkOGM3ODQ0ZWVhZTEyNGY3NmFjNjQyNDFhNjI4NmVhYzMkZTI3YjlkNTQ0ODdhNGViNmEyMTcxMjMxNmJhMWI0ZGU=" + vectorstore = ElasticsearchStore( + es_cloud_id=cloud_id, + index_name=index_name, + embedding=embeddings, + es_user="elastic", + es_password="sSz2BEGv56JRNjGFwoQ191RJ" + ) +else: + logger.info("Using local Elasticsearch.") + vectorstore = ElasticsearchStore( + es_url="http://elasticsearch:9200", + index_name=index_name, + embedding=embeddings, + ) + +logger.info("Connected to Elasticsearch.") + +############################################################################### +# Initialisation of LLM small & large +############################################################################### +llm_small = CustomMistralLLM( + api_key=mistral_api_key, + endpoint_url="https://api.mistral.ai/v1/chat/completions", + model_name="mistral-small-latest" +) +llm_large = CustomMistralLLM( + api_key=mistral_api_key, + endpoint_url="https://api.mistral.ai/v1/chat/completions", + model_name="mistral-large-latest" +) + + +############################################################################### +# Request classification function: vyhladavanie vs. upresnenie +############################################################################### +def classify_query(query: str, chat_history: str = "") -> str: + if not chat_history.strip(): + return "vyhladavanie" + prompt = ( + "Ty si zdravotnícky expert, ktorý analyzuje otázky používateľov. " + "Analyzuj nasledujúci dopyt a urči, či ide o dopyt na vyhľadanie liekov alebo " + "o upresnenie/doplnenie už poskytnutej odpovede.\n" + "Ak dopyt obsahuje výrazy ako 'čo pit', 'aké lieky', 'odporuč liek', 'hľadám liek', " + "odpovedaj slovom 'vyhľadávanie'.\n" + "Ak dopyt slúži na upresnenie, napríklad obsahuje výrazy ako 'a nie na predpis', " + "'upresni', 'este raz', odpovedaj slovom 'upresnenie'.\n" + f"Dopyt: \"{query}\"" + ) + classification = llm_small.generate_text(prompt=prompt, max_tokens=20, temperature=0.3) + classification = classification.strip().lower() + logger.info(f"Klasifikácia dopytu: {classification}") + if "vyhládzanie" in classification or "vyhľadávanie" in classification: + return "vyhladavanie" + elif "upresnenie" in classification: + return "upresnenie" + return "vyhladavanie" + + +############################################################################### +# Template for upresnenie dopytu +############################################################################### +def build_upresnenie_prompt_no_history(chat_history: str, user_query: str) -> str: + prompt = f""" +Ty si zdravotnícky expert. Máš k dispozícii históriu chatu a novú upresňujúcu otázku. + +Ak v histórii chatu už existuje jasná odpoveď na túto upresňujúcu otázku, napíš: +"FOUND_IN_HISTORY: <ľudský vysvetľajúci text>" + +Ak však v histórii chatu nie je dostatok informácií, napíš: +"NO_ANSWER_IN_HISTORY: " + +V časti zahrň kľúčové slová z pôvodnej otázky aj z upresnenia. + +=== ZAČIATOK HISTÓRIE CHatu === +{chat_history} +=== KONIEC HISTÓRIE CHatu === + +Upresňujúca otázka od používateľa: +"{user_query}" +""" + return prompt + + +############################################################################### +# Function for retrieving the last vyhladavacieho dopytu z histórie +############################################################################### +def extract_last_vyhladavacie_query(chat_history: str) -> str: + lines = chat_history.splitlines() + last_query = "" + for line in reversed(lines): + if line.startswith("User:"): + last_query = line[len("User:"):].strip() + break + return last_query + + +############################################################################### +# Agent class for data storage: vek, anamneza, predpis, user_data, search_query +############################################################################### +class ConversationalAgent: + def __init__(self): + self.long_term_memory = { + "vek": None, + "anamneza": None, + "predpis": None, + "user_data": None, + "search_query": None + } + + def update_memory(self, key, value): + self.long_term_memory[key] = value + + def get_memory(self, key): + return self.long_term_memory.get(key, None) + + def load_memory_from_history(self, chat_history: str): + memory_match = re.search(r"\[MEMORY\](.*?)\[/MEMORY\]", chat_history, re.DOTALL) + if memory_match: + try: + memory_data = json.loads(memory_match.group(1)) + self.long_term_memory.update(memory_data) + logger.info(f"Nahraná pamäť z histórie: {self.long_term_memory}") + except Exception as e: + logger.error(f"Chyba pri načítaní pamäte: {e}") + + def parse_user_info(self, query: str): + text_lower = query.lower() + if re.search(r"\d+\s*(rok(ov|y)?|years?)", text_lower): + self.update_memory("user_data", query) + age_match = re.search(r"(\d{1,3})\s*(rok(ov|y)?|years?)", text_lower) + if age_match: + self.update_memory("vek", age_match.group(1)) + if ("nemá" in text_lower or "nema" in text_lower) and ("chronické" in text_lower or "alerg" in text_lower): + self.update_memory("anamneza", "Žiadne chronické ochorenia ani alergie") + elif (("chronické" in text_lower or "alerg" in text_lower) and ("má" in text_lower or "ma" in text_lower)): + self.update_memory("anamneza", "Má chronické ochorenie alebo alergie (nespecifikované)") + if "voľnopredajný" in text_lower: + self.update_memory("predpis", "volnopredajny") + elif "na predpis" in text_lower: + self.update_memory("predpis", "na predpis") + + def analyze_input(self, query: str) -> dict: + self.parse_user_info(query) + missing_info = {} + if not self.get_memory("vek"): + missing_info["vek"] = "Prosím, uveďte vek pacienta." + if not self.get_memory("anamneza"): + missing_info["anamnéza"] = "Má pacient nejaké chronické ochorenia alebo alergie?" + if not self.get_memory("predpis"): + missing_info["predpis"] = "Ide o liek na predpis alebo voľnopredajný liek?" + return missing_info + + def ask_follow_up(self, missing_info: dict) -> str: + return " ".join(missing_info.values()) + + +############################################################################### +# Main function process_query_with_mistral with updated logic +############################################################################### +CHAT_HISTORY_ENDPOINT = "http://localhost:5000/api/chat_history_detail" + + +def process_query_with_mistral(query: str, chat_id: str, chat_context: str, k=10): + logger.info("Processing query started.") + + chat_history = "" + if chat_context: + chat_history = chat_context + elif chat_id: + try: + params = {"id": chat_id} + r = requests.get(CHAT_HISTORY_ENDPOINT, params=params) + if r.status_code == 200: + data = r.json() + chat_data = data.get("chat", "") + if isinstance(chat_data, dict): + chat_history = chat_data.get("chat", "") + else: + chat_history = chat_data or "" + logger.info(f"História chatu načítaná pre chatId: {chat_id}") + else: + logger.warning(f"Nepodarilo sa načítať históriu (status {r.status_code}) pre chatId: {chat_id}") + except Exception as e: + logger.error(f"Chyba pri načítaní histórie: {e}") + + agent = ConversationalAgent() + if chat_history: + agent.load_memory_from_history(chat_history) + + existing_user_data = "" + if chat_id: + existing_user_data = get_user_data_from_db(chat_id) + + agent.parse_user_info(query) + missing_info = agent.analyze_input(query) + + if not existing_user_data: + if "Prosím, uveďte vek pacienta" in chat_history: + if chat_id: + update_payload = {"chatId": chat_id, "userData": query} + try: + update_response = requests.post("http://localhost:5000/api/save_user_data", json=update_payload) + if update_response.status_code == 200: + logger.info("User data was successfully updated via endpoint /api/save_user_data (data question flag).") + else: + logger.warning(f"Failed to update data (data question flag): {update_response.text}") + except Exception as e: + logger.error(f"Error when updating user_data via endpoint (data question flag): {e}") + + if missing_info: + logger.info(f"Chýbajúce informácie: {missing_info}") + combined_missing_text = " ".join(missing_info.values()) + if query.strip() not in combined_missing_text: + if chat_id: + update_payload = {"chatId": chat_id, "userData": query} + try: + update_response = requests.post("http://localhost:5000/api/save_user_data", json=update_payload) + if update_response.status_code == 200: + logger.info("User data was successfully updated via endpoint /api/save_user_data.") + else: + logger.warning(f"Failed to update the data: {update_response.text}") + except Exception as e: + logger.error(f"Error when updating user_data via endpoint: {e}") + return { + "best_answer": combined_missing_text, + "model": "FollowUp (new chat)", + "rating": 0, + "explanation": "Additional data pre pokračovanie is required.", + "patient_data": query + } + + qtype = classify_query(query, chat_history) + logger.info(f"Typ dopytu: {qtype}") + logger.info(f"Chat context (snippet): {chat_history[:200]}...") + + if qtype == "vyhladavanie": + user_data_db = get_user_data_from_db(chat_id) + if user_data_db: + query = query + " Udaje cloveka: " + user_data_db + agent.long_term_memory["search_query"] = query + + if qtype == "upresnenie": + original_search = agent.long_term_memory.get("search_query") + if not original_search: + original_search = extract_last_vyhladavacie_query(chat_history) + if original_search is None: + original_search = "" + combined_query = (original_search + " " + query).strip() + user_data_db = get_user_data_from_db(chat_id) + if user_data_db: + combined_query += " Udaje cloveka: " + user_data_db + logger.info(f"Combined query for search: {combined_query}") + + upres_prompt = build_upresnenie_prompt_no_history(chat_history, combined_query) + response_str = llm_small.generate_text(upres_prompt, max_tokens=1200, temperature=0.5) + normalized = response_str.strip() + logger.info(f"Upresnenie prompt response: {normalized}") + + if re.match(r"(?i)^found_in_history:\s*", normalized): + logger.info("Zistený FOUND_IN_HISTORY – vykonávame vyhľadávanie s kombinovaným dopytom.") + elif re.match(r"(?i)^no_answer_in_history:\s*", normalized): + parts = re.split(r"(?i)^no_answer_in_history:\s*", normalized, maxsplit=1) + if len(parts) >= 2: + combined_query = parts[1].strip() + logger.info(f"Upravený vyhľadávací dopyт z NO_ANSWER_IN_HISTORY: {combined_query}") + + vector_results = vectorstore.similarity_search(combined_query, k=k) + max_docs = 5 + max_len = 1000 + vector_docs = [hit.metadata.get("text", "")[:max_len] for hit in vector_results[:max_docs]] + if not vector_docs: + return { + "best_answer": "Ľutujem, nenašli sa žiadne relevantné informácie.", + "model": "Upresnenie-NoResults", + "rating": 0, + "explanation": "No results from search." + } + joined_docs = "\n".join(vector_docs) + final_prompt = ( + f"Otázka: {combined_query}\n\n" + "Na základe týchto informácií:\n" + f"{joined_docs}\n\n" + "Vygeneruj odporúčanie liekov alebo vysvetlenie, ak je to relevantné.\n" + "Prosím, odpovedaj stručne a dostatočne, bez nadmernej dĺžky." + ) + ans_small = llm_small.generate_text(final_prompt, max_tokens=1200, temperature=0.7) + ans_large = llm_large.generate_text(final_prompt, max_tokens=1200, temperature=0.7) + val_small = validate_answer_logic(combined_query, ans_small) + val_large = validate_answer_logic(combined_query, ans_large) + eval_small = evaluate_complete_answer(combined_query, val_small) + eval_large = evaluate_complete_answer(combined_query, val_large) + candidates = [ + {"summary": val_small, "eval": eval_small, "model": "Mistral Small"}, + {"summary": val_large, "eval": eval_large, "model": "Mistral Large"}, + ] + + # + # for candidate in candidates: + # detailed_desc = detailed_evaluation_description(combined_query, candidate["summary"], candidate["eval"]["rating"]) + # candidate["eval"]["detailed_description"] = detailed_desc + # + + + best = max(candidates, key=lambda x: x["eval"]["rating"]) + logger.info(f"Odpoveď od modelu {best['model']} má rating: {best['eval']['rating']}/10") + + + evaluation_table = "=== Výsledky hodnotenia odpovedí ===\n" + evaluation_table += "{:<15} | {:<6} | {:<60}\n".format("Model", "Rating", "Evaluated Text") + evaluation_table += "-" * 100 + "\n" + for candidate in candidates: + model_name = candidate["model"] + rating = candidate["eval"]["rating"] + evaluated_text = candidate["summary"].replace("\n", " ") + evaluation_table += "{:<15} | {:<6} | {:<60}\n".format(model_name, rating, evaluated_text) + evaluation_table += "=" * 100 + "\n" + + # with open("evaluation.txt", "w", encoding="utf-8") as f: + # f.write(evaluation_table) + # logger.info("Evaluation table записана в evaluation.txt") + + final_answer = translate_preserving_medicine_names(best["summary"]) + memory_json = json.dumps(agent.long_term_memory) + memory_block = f"[MEMORY]{memory_json}[/MEMORY]" + final_answer_with_memory = final_answer + "\n\n" + return { + "best_answer": final_answer_with_memory, + "model": best["model"], + "rating": best["eval"]["rating"], + "explanation": best["eval"]["explanation"] + } + + vector_results = vectorstore.similarity_search(query, k=k) + max_docs = 5 + max_len = 1000 + vector_docs = [hit.metadata.get("text", "")[:max_len] for hit in vector_results[:max_docs]] + if not vector_docs: + return { + "best_answer": "Ľutujem, nenašli sa žiadne relevantné informácie.", + "model": "Vyhladavanie-NoDocs", + "rating": 0, + "explanation": "No results" + } + joined_docs = "\n".join(vector_docs) + final_prompt = ( + f"Otázka: {query}\n\n" + "Na základe týchto informácií:\n" + f"{joined_docs}\n\n" + "Vygeneruj odporúčanie liekov alebo vysvetlenie, ak je to relevantné.\n" + "Prosím, odpovedaj stručne a dostatočne, bez nadmernej dĺžky." + ) + answer = llm_small.generate_text(final_prompt, max_tokens=1200, temperature=0.7) + memory_json = json.dumps(agent.long_term_memory) + memory_block = f"[MEMORY]{memory_json}[/MEMORY]" + answer_with_memory = answer + "\n\n" + return { + "best_answer": answer_with_memory, + "model": "Vyhladavanie-Final", + "rating": 9, + "explanation": "Vyhľadávacia cesta" + } diff --git a/Backend/requirements.txt b/Backend/requirements.txt index 510632f..4489e8b 100644 --- a/Backend/requirements.txt +++ b/Backend/requirements.txt @@ -1,12 +1,12 @@ -Flask -flask-cors -requests -elasticsearch -langchain -langchain-huggingface -langchain-elasticsearch -psycopg2-binary -google-auth -transformers -sentence-transformers>=2.2.2 -accelerate +Flask +flask-cors +requests +elasticsearch +langchain +langchain-huggingface +langchain-elasticsearch +psycopg2-binary +google-auth +transformers +sentence-transformers>=2.2.2 +accelerate diff --git a/Backend/server.py b/Backend/server.py index 09b7d89..d1fa11f 100644 --- a/Backend/server.py +++ b/Backend/server.py @@ -1,361 +1,366 @@ -import time -import re -from flask import Flask, request, jsonify -from flask_cors import CORS -from google.oauth2 import id_token -from google.auth.transport import requests as google_requests -import logging -import psycopg2 -from psycopg2.extras import RealDictCursor -from model import process_query_with_mistral - -_real_time = time.time -time.time = lambda: _real_time() - 1 - -# Database connection parameters -DATABASE_CONFIG = { - "dbname": "HealthAIDB", - "user": "postgres", - "password": "Oleg2005", - "host": "postgres", - "port": 5432, -} - -import logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -try: - conn = psycopg2.connect(**DATABASE_CONFIG) - logger.info("Database connection established successfully") -except Exception as e: - logger.error(f"Error connecting to database: {e}", exc_info=True) - conn = None - -def init_db(): - create_users_query = """ - CREATE TABLE IF NOT EXISTS users ( - id SERIAL PRIMARY KEY, - name TEXT NOT NULL, - email TEXT UNIQUE NOT NULL, - google_id TEXT, - password TEXT - ); - """ - create_chat_history_query = """ - CREATE TABLE IF NOT EXISTS chat_history ( - id SERIAL PRIMARY KEY, - user_email TEXT NOT NULL, - chat TEXT NOT NULL, - user_data TEXT, - created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP - ); - """ - try: - with conn.cursor() as cur: - cur.execute(create_users_query) - cur.execute(create_chat_history_query) - conn.commit() - logger.info("Database tables initialized successfully") - except Exception as e: - logger.error(f"Error initializing database tables: {e}", exc_info=True) - conn.rollback() - -if conn: - init_db() - -app = Flask(__name__) -CORS(app, resources={r"/api/*": {"origins": "*"}}) - -CLIENT_ID = "532143017111-4eqtlp0oejqaovj6rf5l1ergvhrp4vao.apps.googleusercontent.com" - -def save_user_to_db(name, email, google_id=None, password=None): - logger.info(f"Saving user {name} with email: {email} to the database") - try: - with conn.cursor(cursor_factory=RealDictCursor) as cur: - cur.execute( - """ - INSERT INTO users (name, email, google_id, password) - VALUES (%s, %s, %s, %s) - ON CONFLICT (email) DO NOTHING - """, - (name, email, google_id, password) - ) - conn.commit() - logger.info(f"User {name} ({email}) saved successfully") - except Exception as e: - logger.error(f"Error saving user {name} ({email}) to database: {e}", exc_info=True) - -@app.route('/api/verify', methods=['POST']) -def verify_token(): - logger.info("Received token verification request") - data = request.get_json() - token = data.get('token') - if not token: - logger.warning("Token not provided in request") - return jsonify({'error': 'No token provided'}), 400 - try: - id_info = id_token.verify_oauth2_token(token, google_requests.Request(), CLIENT_ID) - user_email = id_info.get('email') - user_name = id_info.get('name') - google_id = id_info.get('sub') - logger.info(f"Token verified for user: {user_name} ({user_email})") - save_user_to_db(name=user_name, email=user_email, google_id=google_id) - return jsonify({'message': 'Authentication successful', 'user': {'email': user_email, 'name': user_name}}), 200 - except ValueError as e: - logger.error(f"Token verification error: {e}", exc_info=True) - return jsonify({'error': 'Invalid token'}), 400 - -@app.route('/api/register', methods=['POST']) -def register(): - logger.info("Received new user registration request") - data = request.get_json() - name = data.get('name') - email = data.get('email') - password = data.get('password') - if not all([name, email, password]): - logger.warning("Not all required fields provided for registration") - return jsonify({'error': 'All fields are required'}), 400 - try: - with conn.cursor(cursor_factory=RealDictCursor) as cur: - cur.execute("SELECT * FROM users WHERE email = %s", (email,)) - existing_user = cur.fetchone() - if existing_user: - logger.warning(f"User with email {email} already exists") - return jsonify({'error': 'User already exists'}), 409 - save_user_to_db(name=name, email=email, password=password) - logger.info(f"User {name} ({email}) registered successfully") - return jsonify({'message': 'User registered successfully'}), 201 - except Exception as e: - logger.error(f"Error during user registration: {e}", exc_info=True) - return jsonify({'error': str(e)}), 500 - -@app.route('/api/login', methods=['POST']) -def login(): - logger.info("Received login request") - data = request.get_json() - email = data.get('email') - password = data.get('password') - if not all([email, password]): - logger.warning("Email or password not provided") - return jsonify({'error': 'Email and password are required'}), 400 - try: - with conn.cursor(cursor_factory=RealDictCursor) as cur: - cur.execute("SELECT * FROM users WHERE email = %s", (email,)) - user = cur.fetchone() - if not user or user.get('password') != password: - logger.warning(f"Invalid credentials for email: {email}") - return jsonify({'error': 'Invalid credentials'}), 401 - logger.info(f"User {user.get('name')} ({email}) logged in successfully") - return jsonify({'message': 'Login successful', 'user': {'name': user.get('name'), 'email': user.get('email')}}), 200 - except Exception as e: - logger.error(f"Error during user login: {e}", exc_info=True) - return jsonify({'error': str(e)}), 500 - - -@app.route('/api/update_profile', methods=['PUT']) -def update_profile(): - data = request.get_json() - email = data.get('email') - if not email: - return jsonify({'error': 'Email is required'}), 400 - - # Fields to update; if not provided, the current value remains. - name = data.get('name') - phone = data.get('phone') - role = data.get('role') - bio = data.get('bio') - picture = data.get('picture') - - try: - with conn.cursor(cursor_factory=RealDictCursor) as cur: - cur.execute( - """ - UPDATE users - SET name = COALESCE(%s, name), - phone = COALESCE(%s, phone), - role = COALESCE(%s, role), - bio = COALESCE(%s, bio), - picture = COALESCE(%s, picture) - WHERE email = %s - """, - (name, phone, role, bio, picture, email) - ) - conn.commit() - logger.info(f"Profile updated for {email}") - return jsonify({'message': 'Profile updated successfully'}), 200 - except Exception as e: - logger.error(f"Error updating profile: {e}") - return jsonify({'error': str(e)}), 500 - -@app.route('/api/chat', methods=['POST']) -def chat(): - logger.info("Received chat request") - data = request.get_json() - query = data.get('query', '') - user_email = data.get('email') - chat_id = data.get('chatId') - - if not query: - logger.warning("No query provided") - return jsonify({'error': 'No query provided'}), 400 - - logger.info(f"Processing request for chatId: {chat_id if chat_id else 'new chat'} | Query: {query}") - - # Retrieve chat context from the database - chat_context = "" - if chat_id: - try: - with conn.cursor(cursor_factory=RealDictCursor) as cur: - cur.execute("SELECT chat, user_data FROM chat_history WHERE id = %s", (chat_id,)) - result = cur.fetchone() - if result: - chat_context = result.get("chat", "") - logger.info(f"Loaded chat context for chatId {chat_id}: {chat_context}") - else: - logger.info(f"No chat context found for chatId {chat_id}") - except Exception as e: - logger.error(f"Error loading chat context from DB: {e}", exc_info=True) - - logger.info("Calling process_query_with_mistral function") - response_obj = process_query_with_mistral(query, chat_id=chat_id, chat_context=chat_context) - best_answer = response_obj.get("best_answer", "") if isinstance(response_obj, dict) else str(response_obj) - logger.info(f"Response from process_query_with_mistral: {best_answer}") - - best_answer = re.sub(r'[*#]', '', best_answer) - best_answer = re.sub(r'(\d\.\s)', r'\n\n\1', best_answer) - best_answer = re.sub(r':\s-', r':\n-', best_answer) - - # Update or create chat_history record including user_data if available - if chat_id: - try: - with conn.cursor(cursor_factory=RealDictCursor) as cur: - cur.execute("SELECT chat FROM chat_history WHERE id = %s", (chat_id,)) - existing_chat = cur.fetchone() - if existing_chat: - updated_chat = existing_chat['chat'] + f"\nUser: {query}\nBot: {best_answer}" - if "patient_data" in response_obj: - cur.execute("UPDATE chat_history SET chat = %s, user_data = %s WHERE id = %s", - (updated_chat, response_obj["patient_data"], chat_id)) - else: - cur.execute("UPDATE chat_history SET chat = %s WHERE id = %s", (updated_chat, chat_id)) - conn.commit() - logger.info(f"Chat history for chatId {chat_id} updated successfully") - else: - with conn.cursor(cursor_factory=RealDictCursor) as cur2: - cur2.execute( - "INSERT INTO chat_history (user_email, chat) VALUES (%s, %s) RETURNING id", - (user_email, f"User: {query}\nBot: {best_answer}") - ) - new_chat_id = cur2.fetchone()['id'] - conn.commit() - chat_id = new_chat_id - logger.info(f"New chat created with chatId: {chat_id}") - except Exception as e: - logger.error(f"Error updating/creating chat history: {e}", exc_info=True) - return jsonify({'error': str(e)}), 500 - else: - try: - with conn.cursor(cursor_factory=RealDictCursor) as cur: - cur.execute( - "INSERT INTO chat_history (user_email, chat) VALUES (%s, %s) RETURNING id", - (user_email, f"User: {query}\nBot: {best_answer}") - ) - new_chat_id = cur.fetchone()['id'] - conn.commit() - chat_id = new_chat_id - logger.info(f"New chat created with chatId: {chat_id}") - except Exception as e: - logger.error(f"Error creating new chat: {e}", exc_info=True) - return jsonify({'error': str(e)}), 500 - - return jsonify({'response': {'best_answer': best_answer, 'model': response_obj.get("model", ""), 'chatId': chat_id}}), 200 - -@app.route('/api/save_user_data', methods=['POST']) -def save_user_data(): - logger.info("Received request to save user data") - data = request.get_json() - chat_id = data.get('chatId') - user_data = data.get('userData') - if not chat_id or not user_data: - return jsonify({'error': 'chatId and userData are required'}), 400 - try: - with conn.cursor(cursor_factory=RealDictCursor) as cur: - cur.execute("UPDATE chat_history SET user_data = %s WHERE id = %s", (user_data, chat_id)) - conn.commit() - logger.info(f"User data for chatId {chat_id} updated successfully") - return jsonify({'message': 'User data updated successfully'}), 200 - except Exception as e: - logger.error(f"Error updating user data: {e}", exc_info=True) - return jsonify({'error': str(e)}), 500 - -@app.route('/api/chat_history', methods=['GET']) -def get_chat_history(): - logger.info("Received request to get chat history") - user_email = request.args.get('email') - if not user_email: - return jsonify({'error': 'User email is required'}), 400 - try: - with conn.cursor(cursor_factory=RealDictCursor) as cur: - cur.execute( - "SELECT id, chat, user_data, created_at FROM chat_history WHERE user_email = %s ORDER BY created_at DESC", - (user_email,) - ) - history = cur.fetchall() - return jsonify({'history': history}), 200 - except Exception as e: - logger.error(f"Error getting chat history for {user_email}: {e}", exc_info=True) - return jsonify({'error': str(e)}), 500 - -@app.route('/api/chat_history', methods=['DELETE']) -def delete_chat(): - logger.info("Received request to delete chat") - chat_id = request.args.get('id') - if not chat_id: - return jsonify({'error': 'Chat id is required'}), 400 - try: - with conn.cursor() as cur: - cur.execute("DELETE FROM chat_history WHERE id = %s", (chat_id,)) - conn.commit() - return jsonify({'message': 'Chat deleted successfully'}), 200 - except Exception as e: - logger.error(f"Error deleting chat with chatId {chat_id}: {e}", exc_info=True) - return jsonify({'error': str(e)}), 500 - -@app.route('/api/get_user_data', methods=['GET']) -def get_user_data(): - chat_id = request.args.get('chatId') - if not chat_id: - return jsonify({'error': 'Chat id is required'}), 400 - try: - with conn.cursor(cursor_factory=RealDictCursor) as cur: - cur.execute("SELECT user_data FROM chat_history WHERE id = %s", (chat_id,)) - result = cur.fetchone() - if result and result.get("user_data"): - return jsonify({'user_data': result.get("user_data")}), 200 - else: - return jsonify({'user_data': None}), 200 - except Exception as e: - logger.error(f"Error retrieving user data: {e}", exc_info=True) - return jsonify({'error': str(e)}), 500 - -@app.route('/api/chat_history_detail', methods=['GET']) -def chat_history_detail(): - logger.info("Received request to get chat details") - chat_id = request.args.get('id') - if not chat_id: - return jsonify({'error': 'Chat id is required'}), 400 - try: - with conn.cursor(cursor_factory=RealDictCursor) as cur: - cur.execute("SELECT id, chat, user_data, created_at FROM chat_history WHERE id = %s", (chat_id,)) - chat = cur.fetchone() - if not chat: - return jsonify({'error': 'Chat not found'}), 404 - return jsonify({'chat': chat}), 200 - except Exception as e: - logger.error(f"Error getting chat details for chatId {chat_id}: {e}", exc_info=True) - return jsonify({'error': str(e)}), 500 - -if __name__ == '__main__': - logger.info("Starting Flask application") - app.run(host='0.0.0.0', port=5000, debug=True) +import time +import re +from flask import Flask, request, jsonify +from flask_cors import CORS +from google.oauth2 import id_token +from google.auth.transport import requests as google_requests +import logging +import psycopg2 +from psycopg2.extras import RealDictCursor +from model import process_query_with_mistral + +_real_time = time.time +time.time = lambda: _real_time() - 1 + +# Database connection parameters +DATABASE_CONFIG = { + "dbname": "HealthAIDB", + "user": "postgres", + "password": "Oleg2005", + "host": "postgres", + "port": 5432, +} + +import logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +try: + conn = psycopg2.connect(**DATABASE_CONFIG) + logger.info("Database connection established successfully") +except Exception as e: + logger.error(f"Error connecting to database: {e}", exc_info=True) + conn = None + +def init_db(): + create_users_query = """ + CREATE TABLE IF NOT EXISTS users ( + id SERIAL PRIMARY KEY, + name TEXT NOT NULL, + email TEXT UNIQUE NOT NULL, + google_id TEXT, + password TEXT, + phone TEXT, + role TEXT, + bio TEXT, + picture TEXT +); + + """ + create_chat_history_query = """ + CREATE TABLE IF NOT EXISTS chat_history ( + id SERIAL PRIMARY KEY, + user_email TEXT NOT NULL, + chat TEXT NOT NULL, + user_data TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ); + """ + try: + with conn.cursor() as cur: + cur.execute(create_users_query) + cur.execute(create_chat_history_query) + conn.commit() + logger.info("Database tables initialized successfully") + except Exception as e: + logger.error(f"Error initializing database tables: {e}", exc_info=True) + conn.rollback() + +if conn: + init_db() + +app = Flask(__name__) +CORS(app, resources={r"/api/*": {"origins": "*"}}) + +CLIENT_ID = "532143017111-4eqtlp0oejqaovj6rf5l1ergvhrp4vao.apps.googleusercontent.com" + +def save_user_to_db(name, email, google_id=None, password=None): + logger.info(f"Saving user {name} with email: {email} to the database") + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute( + """ + INSERT INTO users (name, email, google_id, password) + VALUES (%s, %s, %s, %s) + ON CONFLICT (email) DO NOTHING + """, + (name, email, google_id, password) + ) + conn.commit() + logger.info(f"User {name} ({email}) saved successfully") + except Exception as e: + logger.error(f"Error saving user {name} ({email}) to database: {e}", exc_info=True) + +@app.route('/api/verify', methods=['POST']) +def verify_token(): + logger.info("Received token verification request") + data = request.get_json() + token = data.get('token') + if not token: + logger.warning("Token not provided in request") + return jsonify({'error': 'No token provided'}), 400 + try: + id_info = id_token.verify_oauth2_token(token, google_requests.Request(), CLIENT_ID) + user_email = id_info.get('email') + user_name = id_info.get('name') + google_id = id_info.get('sub') + logger.info(f"Token verified for user: {user_name} ({user_email})") + save_user_to_db(name=user_name, email=user_email, google_id=google_id) + return jsonify({'message': 'Authentication successful', 'user': {'email': user_email, 'name': user_name}}), 200 + except ValueError as e: + logger.error(f"Token verification error: {e}", exc_info=True) + return jsonify({'error': 'Invalid token'}), 400 + +@app.route('/api/register', methods=['POST']) +def register(): + logger.info("Received new user registration request") + data = request.get_json() + name = data.get('name') + email = data.get('email') + password = data.get('password') + if not all([name, email, password]): + logger.warning("Not all required fields provided for registration") + return jsonify({'error': 'All fields are required'}), 400 + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute("SELECT * FROM users WHERE email = %s", (email,)) + existing_user = cur.fetchone() + if existing_user: + logger.warning(f"User with email {email} already exists") + return jsonify({'error': 'User already exists'}), 409 + save_user_to_db(name=name, email=email, password=password) + logger.info(f"User {name} ({email}) registered successfully") + return jsonify({'message': 'User registered successfully'}), 201 + except Exception as e: + logger.error(f"Error during user registration: {e}", exc_info=True) + return jsonify({'error': str(e)}), 500 + +@app.route('/api/login', methods=['POST']) +def login(): + logger.info("Received login request") + data = request.get_json() + email = data.get('email') + password = data.get('password') + if not all([email, password]): + logger.warning("Email or password not provided") + return jsonify({'error': 'Email and password are required'}), 400 + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute("SELECT * FROM users WHERE email = %s", (email,)) + user = cur.fetchone() + if not user or user.get('password') != password: + logger.warning(f"Invalid credentials for email: {email}") + return jsonify({'error': 'Invalid credentials'}), 401 + logger.info(f"User {user.get('name')} ({email}) logged in successfully") + return jsonify({'message': 'Login successful', 'user': {'name': user.get('name'), 'email': user.get('email')}}), 200 + except Exception as e: + logger.error(f"Error during user login: {e}", exc_info=True) + return jsonify({'error': str(e)}), 500 + + +@app.route('/api/update_profile', methods=['PUT']) +def update_profile(): + data = request.get_json() + email = data.get('email') + if not email: + return jsonify({'error': 'Email is required'}), 400 + + # Fields to update; if not provided, the current value remains. + name = data.get('name') + phone = data.get('phone') + role = data.get('role') + bio = data.get('bio') + picture = data.get('picture') + + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute( + """ + UPDATE users + SET name = COALESCE(%s, name), + phone = COALESCE(%s, phone), + role = COALESCE(%s, role), + bio = COALESCE(%s, bio), + picture = COALESCE(%s, picture) + WHERE email = %s + """, + (name, phone, role, bio, picture, email) + ) + conn.commit() + logger.info(f"Profile updated for {email}") + return jsonify({'message': 'Profile updated successfully'}), 200 + except Exception as e: + logger.error(f"Error updating profile: {e}") + return jsonify({'error': str(e)}), 500 + +@app.route('/api/chat', methods=['POST']) +def chat(): + logger.info("Received chat request") + data = request.get_json() + query = data.get('query', '') + user_email = data.get('email') + chat_id = data.get('chatId') + + if not query: + logger.warning("No query provided") + return jsonify({'error': 'No query provided'}), 400 + + logger.info(f"Processing request for chatId: {chat_id if chat_id else 'new chat'} | Query: {query}") + + # Retrieve chat context from the database + chat_context = "" + if chat_id: + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute("SELECT chat, user_data FROM chat_history WHERE id = %s", (chat_id,)) + result = cur.fetchone() + if result: + chat_context = result.get("chat", "") + logger.info(f"Loaded chat context for chatId {chat_id}: {chat_context}") + else: + logger.info(f"No chat context found for chatId {chat_id}") + except Exception as e: + logger.error(f"Error loading chat context from DB: {e}", exc_info=True) + + logger.info("Calling process_query_with_mistral function") + response_obj = process_query_with_mistral(query, chat_id=chat_id, chat_context=chat_context) + best_answer = response_obj.get("best_answer", "") if isinstance(response_obj, dict) else str(response_obj) + logger.info(f"Response from process_query_with_mistral: {best_answer}") + + best_answer = re.sub(r'[*#]', '', best_answer) + best_answer = re.sub(r'(\d\.\s)', r'\n\n\1', best_answer) + best_answer = re.sub(r':\s-', r':\n-', best_answer) + + # Update or create chat_history record including user_data if available + if chat_id: + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute("SELECT chat FROM chat_history WHERE id = %s", (chat_id,)) + existing_chat = cur.fetchone() + if existing_chat: + updated_chat = existing_chat['chat'] + f"\nUser: {query}\nBot: {best_answer}" + if "patient_data" in response_obj: + cur.execute("UPDATE chat_history SET chat = %s, user_data = %s WHERE id = %s", + (updated_chat, response_obj["patient_data"], chat_id)) + else: + cur.execute("UPDATE chat_history SET chat = %s WHERE id = %s", (updated_chat, chat_id)) + conn.commit() + logger.info(f"Chat history for chatId {chat_id} updated successfully") + else: + with conn.cursor(cursor_factory=RealDictCursor) as cur2: + cur2.execute( + "INSERT INTO chat_history (user_email, chat) VALUES (%s, %s) RETURNING id", + (user_email, f"User: {query}\nBot: {best_answer}") + ) + new_chat_id = cur2.fetchone()['id'] + conn.commit() + chat_id = new_chat_id + logger.info(f"New chat created with chatId: {chat_id}") + except Exception as e: + logger.error(f"Error updating/creating chat history: {e}", exc_info=True) + return jsonify({'error': str(e)}), 500 + else: + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute( + "INSERT INTO chat_history (user_email, chat) VALUES (%s, %s) RETURNING id", + (user_email, f"User: {query}\nBot: {best_answer}") + ) + new_chat_id = cur.fetchone()['id'] + conn.commit() + chat_id = new_chat_id + logger.info(f"New chat created with chatId: {chat_id}") + except Exception as e: + logger.error(f"Error creating new chat: {e}", exc_info=True) + return jsonify({'error': str(e)}), 500 + + return jsonify({'response': {'best_answer': best_answer, 'model': response_obj.get("model", ""), 'chatId': chat_id}}), 200 + +@app.route('/api/save_user_data', methods=['POST']) +def save_user_data(): + logger.info("Received request to save user data") + data = request.get_json() + chat_id = data.get('chatId') + user_data = data.get('userData') + if not chat_id or not user_data: + return jsonify({'error': 'chatId and userData are required'}), 400 + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute("UPDATE chat_history SET user_data = %s WHERE id = %s", (user_data, chat_id)) + conn.commit() + logger.info(f"User data for chatId {chat_id} updated successfully") + return jsonify({'message': 'User data updated successfully'}), 200 + except Exception as e: + logger.error(f"Error updating user data: {e}", exc_info=True) + return jsonify({'error': str(e)}), 500 + +@app.route('/api/chat_history', methods=['GET']) +def get_chat_history(): + logger.info("Received request to get chat history") + user_email = request.args.get('email') + if not user_email: + return jsonify({'error': 'User email is required'}), 400 + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute( + "SELECT id, chat, user_data, created_at FROM chat_history WHERE user_email = %s ORDER BY created_at DESC", + (user_email,) + ) + history = cur.fetchall() + return jsonify({'history': history}), 200 + except Exception as e: + logger.error(f"Error getting chat history for {user_email}: {e}", exc_info=True) + return jsonify({'error': str(e)}), 500 + +@app.route('/api/chat_history', methods=['DELETE']) +def delete_chat(): + logger.info("Received request to delete chat") + chat_id = request.args.get('id') + if not chat_id: + return jsonify({'error': 'Chat id is required'}), 400 + try: + with conn.cursor() as cur: + cur.execute("DELETE FROM chat_history WHERE id = %s", (chat_id,)) + conn.commit() + return jsonify({'message': 'Chat deleted successfully'}), 200 + except Exception as e: + logger.error(f"Error deleting chat with chatId {chat_id}: {e}", exc_info=True) + return jsonify({'error': str(e)}), 500 + +@app.route('/api/get_user_data', methods=['GET']) +def get_user_data(): + chat_id = request.args.get('chatId') + if not chat_id: + return jsonify({'error': 'Chat id is required'}), 400 + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute("SELECT user_data FROM chat_history WHERE id = %s", (chat_id,)) + result = cur.fetchone() + if result and result.get("user_data"): + return jsonify({'user_data': result.get("user_data")}), 200 + else: + return jsonify({'user_data': None}), 200 + except Exception as e: + logger.error(f"Error retrieving user data: {e}", exc_info=True) + return jsonify({'error': str(e)}), 500 + +@app.route('/api/chat_history_detail', methods=['GET']) +def chat_history_detail(): + logger.info("Received request to get chat details") + chat_id = request.args.get('id') + if not chat_id: + return jsonify({'error': 'Chat id is required'}), 400 + try: + with conn.cursor(cursor_factory=RealDictCursor) as cur: + cur.execute("SELECT id, chat, user_data, created_at FROM chat_history WHERE id = %s", (chat_id,)) + chat = cur.fetchone() + if not chat: + return jsonify({'error': 'Chat not found'}), 404 + return jsonify({'chat': chat}), 200 + except Exception as e: + logger.error(f"Error getting chat details for chatId {chat_id}: {e}", exc_info=True) + return jsonify({'error': str(e)}), 500 + +if __name__ == '__main__': + logger.info("Starting Flask application") + app.run(host='0.0.0.0', port=5000, debug=True) diff --git a/Backend/tablepresent.py b/Backend/tablepresent.py index 425a0d2..36c6787 100644 --- a/Backend/tablepresent.py +++ b/Backend/tablepresent.py @@ -1,56 +1,56 @@ -from pptx import Presentation -from pptx.util import Inches - -# Создание новой презентации -prs = Presentation() - -# Добавляем пустой слайд (layout с индексом 5 обычно является пустым) -slide_layout = prs.slide_layouts[5] -slide = prs.slides.add_slide(slide_layout) - -# Определяем позицию и размер таблицы -left = Inches(0.5) -top = Inches(1.5) -width = Inches(9) -height = Inches(3) - -# Количество строк: 1 заголовок + 2 строки с данными -rows = 3 -cols = 4 - -# Добавляем таблицу на слайд -table = slide.shapes.add_table(rows, cols, left, top, width, height).table - -# Устанавливаем ширину столбцов (при необходимости можно настроить отдельно) -table.columns[0].width = Inches(1.5) # Модель -table.columns[1].width = Inches(1) # Оценка -table.columns[2].width = Inches(4) # Текст -table.columns[3].width = Inches(2.5) # Описание - -# Заполняем заголовки -table.cell(0, 0).text = "Модель" -table.cell(0, 1).text = "Оценка" -table.cell(0, 2).text = "Текст" -table.cell(0, 3).text = "Описание" - -# Данные для первого кандидата -table.cell(1, 0).text = "Mistral Small" -table.cell(1, 1).text = "9.0" -table.cell(1, 2).text = ( - "Nevolnosť môže byť spôsobená rôznymi príčinami, ako sú napríklad gastrointestinálne problémy, infekcie, alebo vedľajšie účinky liekov. " - "Pre ľudí, ktorí hľadajú voľnopredajný liek na nevolnosť, sú dostupné niekoľko možností: 1. Dimedrol (Dramin) – Antihistaminikum; " - "2. Bismut subsalicylát (Pepto-Bismol); 3. Ginger (Zázvor); 4. Meclizin (Bonine). Pred použitím lieku je dôležité konzultovať s lekárom." -) -table.cell(1, 3).text = "Evaluation based on required criteria." - -# Данные для второго кандидата -table.cell(2, 0).text = "Mistral Large" -table.cell(2, 1).text = "8.0" -table.cell(2, 2).text = ( - "Pre nevolnosť sa dajú použiť niektoré voľne predávané lieky, ale dôležité je poradiť sa s lekárom, najmä ak má pacient 20 rokov. " - "Medzi bežné voľne predávané lieky patria: 1. Dimenhydrinát; 2. Meclozín. Tieto lieky môžu spôsobiť spánkovosť a dávkovanie je nutné konzultovať." -) -table.cell(2, 3).text = "Evaluation based on required criteria." - -# Сохраняем презентацию в файл -prs.save("evaluation_table.pptx") +from pptx import Presentation +from pptx.util import Inches + +# Создание новой презентации +prs = Presentation() + +# Добавляем пустой слайд (layout с индексом 5 обычно является пустым) +slide_layout = prs.slide_layouts[5] +slide = prs.slides.add_slide(slide_layout) + +# Определяем позицию и размер таблицы +left = Inches(0.5) +top = Inches(1.5) +width = Inches(9) +height = Inches(3) + +# Количество строк: 1 заголовок + 2 строки с данными +rows = 3 +cols = 4 + +# Добавляем таблицу на слайд +table = slide.shapes.add_table(rows, cols, left, top, width, height).table + +# Устанавливаем ширину столбцов (при необходимости можно настроить отдельно) +table.columns[0].width = Inches(1.5) # Модель +table.columns[1].width = Inches(1) # Оценка +table.columns[2].width = Inches(4) # Текст +table.columns[3].width = Inches(2.5) # Описание + +# Заполняем заголовки +table.cell(0, 0).text = "Модель" +table.cell(0, 1).text = "Оценка" +table.cell(0, 2).text = "Текст" +table.cell(0, 3).text = "Описание" + +# Данные для первого кандидата +table.cell(1, 0).text = "Mistral Small" +table.cell(1, 1).text = "9.0" +table.cell(1, 2).text = ( + "Nevolnosť môže byť spôsobená rôznymi príčinami, ako sú napríklad gastrointestinálne problémy, infekcie, alebo vedľajšie účinky liekov. " + "Pre ľudí, ktorí hľadajú voľnopredajný liek na nevolnosť, sú dostupné niekoľko možností: 1. Dimedrol (Dramin) – Antihistaminikum; " + "2. Bismut subsalicylát (Pepto-Bismol); 3. Ginger (Zázvor); 4. Meclizin (Bonine). Pred použitím lieku je dôležité konzultovať s lekárom." +) +table.cell(1, 3).text = "Evaluation based on required criteria." + +# Данные для второго кандидата +table.cell(2, 0).text = "Mistral Large" +table.cell(2, 1).text = "8.0" +table.cell(2, 2).text = ( + "Pre nevolnosť sa dajú použiť niektoré voľne predávané lieky, ale dôležité je poradiť sa s lekárom, najmä ak má pacient 20 rokov. " + "Medzi bežné voľne predávané lieky patria: 1. Dimenhydrinát; 2. Meclozín. Tieto lieky môžu spôsobiť spánkovosť a dávkovanie je nutné konzultovať." +) +table.cell(2, 3).text = "Evaluation based on required criteria." + +# Сохраняем презентацию в файл +prs.save("evaluation_table.pptx") diff --git a/Backend/test_flant5.py b/Backend/test_flant5.py index bdd728d..753566d 100644 --- a/Backend/test_flant5.py +++ b/Backend/test_flant5.py @@ -1,34 +1,34 @@ -import requests - - -API_TOKEN = "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" -API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-large" - - -headers = { - "Authorization": f"Bearer {API_TOKEN}", - "Content-Type": "application/json" -} - -def query_flan_t5(prompt): - payload = { - "inputs": prompt, - "parameters": { - "max_length": 250, - "do_sample": True, - "temperature": 0.9, - "top_p": 0.95, - "top_k": 50 - } - } - response = requests.post(API_URL, headers=headers, json=payload) - return response.json() - - -prompt = "Ako sa máš? Daj odpoved v slovencine" -result = query_flan_t5(prompt) - -if isinstance(result, list) and len(result) > 0: - print("Ответ от Flan-T5:", result[0]['generated_text']) -else: +import requests + + +API_TOKEN = "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" +API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-large" + + +headers = { + "Authorization": f"Bearer {API_TOKEN}", + "Content-Type": "application/json" +} + +def query_flan_t5(prompt): + payload = { + "inputs": prompt, + "parameters": { + "max_length": 250, + "do_sample": True, + "temperature": 0.9, + "top_p": 0.95, + "top_k": 50 + } + } + response = requests.post(API_URL, headers=headers, json=payload) + return response.json() + + +prompt = "Ako sa máš? Daj odpoved v slovencine" +result = query_flan_t5(prompt) + +if isinstance(result, list) and len(result) > 0: + print("Ответ от Flan-T5:", result[0]['generated_text']) +else: print("Ошибка при получении ответа:", result) \ No newline at end of file diff --git a/Backend/test_mt5_base.py b/Backend/test_mt5_base.py index 5e9a9e6..c70baba 100644 --- a/Backend/test_mt5_base.py +++ b/Backend/test_mt5_base.py @@ -1,47 +1,47 @@ -# import requests -# -# API_TOKEN = "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" -# API_URL = "https://api-inference.huggingface.co/models/google/mt5-base" -# -# headers = { -# "Authorization": f"Bearer {API_TOKEN}", -# "Content-Type": "application/json" -# } -# -# def query_mT5(prompt): -# payload = { -# "inputs": prompt, -# "parameters": { -# "max_length": 100, -# "do_sample": True, -# "temperature": 0.7 -# } -# } -# response = requests.post(API_URL, headers=headers, json=payload) -# return response.json() -# -# # Пример использования -# result = query_mT5("Aké sú účinné lieky na horúčku?") -# print("Ответ от mT5:", result) - -from transformers import AutoTokenizer, MT5ForConditionalGeneration - -tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") -model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small") - -# training -input_ids = tokenizer("The walks in park", return_tensors="pt").input_ids -labels = tokenizer(" cute dog the ", return_tensors="pt").input_ids -outputs = model(input_ids=input_ids, labels=labels) -loss = outputs.loss -logits = outputs.logits - -# inference - -input_ids = tokenizer( - "summarize: studies have shown that owning a dog is good for you", return_tensors="pt" -).input_ids # Batch size 1 -outputs = model.generate(input_ids, max_new_tokens=50) -print(tokenizer.decode(outputs[0], skip_special_tokens=True)) - -# studies have shown that owning a dog is good for you. +# import requests +# +# API_TOKEN = "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" +# API_URL = "https://api-inference.huggingface.co/models/google/mt5-base" +# +# headers = { +# "Authorization": f"Bearer {API_TOKEN}", +# "Content-Type": "application/json" +# } +# +# def query_mT5(prompt): +# payload = { +# "inputs": prompt, +# "parameters": { +# "max_length": 100, +# "do_sample": True, +# "temperature": 0.7 +# } +# } +# response = requests.post(API_URL, headers=headers, json=payload) +# return response.json() +# +# # Пример использования +# result = query_mT5("Aké sú účinné lieky na horúčku?") +# print("Ответ от mT5:", result) + +from transformers import AutoTokenizer, MT5ForConditionalGeneration + +tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") +model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small") + +# training +input_ids = tokenizer("The walks in park", return_tensors="pt").input_ids +labels = tokenizer(" cute dog the ", return_tensors="pt").input_ids +outputs = model(input_ids=input_ids, labels=labels) +loss = outputs.loss +logits = outputs.logits + +# inference + +input_ids = tokenizer( + "summarize: studies have shown that owning a dog is good for you", return_tensors="pt" +).input_ids # Batch size 1 +outputs = model.generate(input_ids, max_new_tokens=50) +print(tokenizer.decode(outputs[0], skip_special_tokens=True)) + +# studies have shown that owning a dog is good for you. diff --git a/Backend/test_slovakbert-skquad.py b/Backend/test_slovakbert-skquad.py index 08c7db2..2f65804 100644 --- a/Backend/test_slovakbert-skquad.py +++ b/Backend/test_slovakbert-skquad.py @@ -1,18 +1,18 @@ -from sentence_transformers import SentenceTransformer, util - - -model = SentenceTransformer("TUKE-DeutscheTelekom/slovakbert-skquad-mnlr") - - -sentences = [ - "Prvý most cez Zlatý roh nechal vybudovať cisár Justinián I. V roku 1502 vypísal sultán Bajezid II. súťaž na nový most.", - "V ktorom roku vznikol druhý drevený most cez záliv Zlatý roh?", - "Aká je priemerná dĺžka života v Eritrei?" -] - -embeddings = model.encode(sentences) -print("Shape of embeddings:", embeddings.shape) - - -similarities = util.cos_sim(embeddings, embeddings) -print("Similarity matrix:\n", similarities) +from sentence_transformers import SentenceTransformer, util + + +model = SentenceTransformer("TUKE-DeutscheTelekom/slovakbert-skquad-mnlr") + + +sentences = [ + "Prvý most cez Zlatý roh nechal vybudovať cisár Justinián I. V roku 1502 vypísal sultán Bajezid II. súťaž na nový most.", + "V ktorom roku vznikol druhý drevený most cez záliv Zlatý roh?", + "Aká je priemerná dĺžka života v Eritrei?" +] + +embeddings = model.encode(sentences) +print("Shape of embeddings:", embeddings.shape) + + +similarities = util.cos_sim(embeddings, embeddings) +print("Similarity matrix:\n", similarities) diff --git a/docker-compose.yml b/docker-compose.yml index f62629a..49895a9 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,73 +1,73 @@ -version: '3.8' - -services: - elasticsearch: - build: - context: ./elasticsearch - dockerfile: Dockerfile - container_name: elasticsearch - environment: - - discovery.type=single-node - - xpack.security.enabled=false - ports: - - "9200:9200" - - "9300:9300" - networks: - - app-network - healthcheck: - test: [ "CMD", "curl", "-f", "http://localhost:9200/_cluster/health" ] - interval: 10s - timeout: 5s - retries: 5 - start_period: 40s - - postgres: - image: postgres:14 - container_name: postgres_db - environment: - - POSTGRES_DB=HealthAIDB - - POSTGRES_USER=postgres - - POSTGRES_PASSWORD=Oleg2005 - ports: - - "5432:5432" - volumes: - - pgdata:/var/lib/postgresql/data - networks: - - app-network - - backend: - container_name: backend_container - build: - context: ./Backend - dockerfile: Dockerfile - ports: - - "5000:5000" - environment: - - ELASTICSEARCH_HOST=http://elasticsearch:9200 - - DATABASE_HOST=postgres - depends_on: - elasticsearch: - condition: service_healthy - postgres: - condition: service_started - networks: - - app-network - - frontend: - container_name: frontend_container - build: - context: ./frontend - dockerfile: Dockerfile - ports: - - "5173:5173" - depends_on: - - backend - networks: - - app-network - -networks: - app-network: - driver: bridge - -volumes: - pgdata: +version: '3.8' + +services: + elasticsearch: + build: + context: ./elasticsearch + dockerfile: Dockerfile + container_name: elasticsearch + environment: + - discovery.type=single-node + - xpack.security.enabled=false + ports: + - "9200:9200" + - "9300:9300" + networks: + - app-network + healthcheck: + test: [ "CMD", "curl", "-f", "http://localhost:9200/_cluster/health" ] + interval: 10s + timeout: 5s + retries: 5 + start_period: 40s + + postgres: + image: postgres:14 + container_name: postgres_db + environment: + - POSTGRES_DB=HealthAIDB + - POSTGRES_USER=postgres + - POSTGRES_PASSWORD=Oleg2005 + ports: + - "5432:5432" + volumes: + - pgdata:/var/lib/postgresql/data + networks: + - app-network + + backend: + container_name: backend_container + build: + context: ./Backend + dockerfile: Dockerfile + ports: + - "5000:5000" + environment: + - ELASTICSEARCH_HOST=http://elasticsearch:9200 + - DATABASE_HOST=postgres + depends_on: + elasticsearch: + condition: service_healthy + postgres: + condition: service_started + networks: + - app-network + + frontend: + container_name: frontend_container + build: + context: ./frontend + dockerfile: Dockerfile + ports: + - "5173:5173" + depends_on: + - backend + networks: + - app-network + +networks: + app-network: + driver: bridge + +volumes: + pgdata: diff --git a/elasticsearch/Dockerfile b/elasticsearch/Dockerfile index 756ad21..ab8103d 100644 --- a/elasticsearch/Dockerfile +++ b/elasticsearch/Dockerfile @@ -1,15 +1,15 @@ -FROM docker.elastic.co/elasticsearch/elasticsearch:8.14.3 - - -ENV discovery.type=single-node -ENV xpack.security.enabled=false - - -COPY --chown=elasticsearch:elasticsearch data/ /usr/share/elasticsearch/data - - -RUN chmod -R 0775 /usr/share/elasticsearch/data - - -RUN find /usr/share/elasticsearch/data -type f -name "*.lock" -delete -RUN rm -f /usr/share/elasticsearch/data/nodes/0/node.lock +FROM docker.elastic.co/elasticsearch/elasticsearch:8.14.3 + + +ENV discovery.type=single-node +ENV xpack.security.enabled=false + + +COPY --chown=elasticsearch:elasticsearch data/ /usr/share/elasticsearch/data + + +RUN chmod -R 0775 /usr/share/elasticsearch/data + + +RUN find /usr/share/elasticsearch/data -type f -name "*.lock" -delete +RUN rm -f /usr/share/elasticsearch/data/nodes/0/node.lock diff --git a/frontend/.gitignore b/frontend/.gitignore index a547bf3..4108b33 100644 --- a/frontend/.gitignore +++ b/frontend/.gitignore @@ -1,24 +1,24 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* - -node_modules -dist -dist-ssr -*.local - -# Editor directories and files -.vscode/* -!.vscode/extensions.json -.idea -.DS_Store -*.suo -*.ntvs* -*.njsproj -*.sln -*.sw? +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/frontend/.vite/deps/_metadata.json b/frontend/.vite/deps/_metadata.json index 2c88dae..3f87636 100644 --- a/frontend/.vite/deps/_metadata.json +++ b/frontend/.vite/deps/_metadata.json @@ -1,8 +1,8 @@ -{ - "hash": "c4d2d06d", - "configHash": "9db50785", - "lockfileHash": "e3b0c442", - "browserHash": "efbdb5f3", - "optimized": {}, - "chunks": {} +{ + "hash": "c4d2d06d", + "configHash": "9db50785", + "lockfileHash": "e3b0c442", + "browserHash": "efbdb5f3", + "optimized": {}, + "chunks": {} } \ No newline at end of file diff --git a/frontend/.vite/deps/package.json b/frontend/.vite/deps/package.json index 3dbc1ca..9168335 100644 --- a/frontend/.vite/deps/package.json +++ b/frontend/.vite/deps/package.json @@ -1,3 +1,3 @@ -{ - "type": "module" -} +{ + "type": "module" +} diff --git a/frontend/.vite/deps_temp_530095be/package.json b/frontend/.vite/deps_temp_530095be/package.json index 3dbc1ca..9168335 100644 --- a/frontend/.vite/deps_temp_530095be/package.json +++ b/frontend/.vite/deps_temp_530095be/package.json @@ -1,3 +1,3 @@ -{ - "type": "module" -} +{ + "type": "module" +} diff --git a/frontend/Dockerfile b/frontend/Dockerfile index 5b4a0bd..eb7ecde 100644 --- a/frontend/Dockerfile +++ b/frontend/Dockerfile @@ -1,26 +1,26 @@ - -FROM node:18-alpine - - -WORKDIR /app - - -COPY package*.json ./ - - -RUN npm install - - -COPY . . - - -RUN npm run build - - -RUN npm install -g serve - - -EXPOSE 5173 - - -CMD ["serve", "-s", "dist", "-l", "5173"] + +FROM node:18-alpine + + +WORKDIR /app + + +COPY package*.json ./ + + +RUN npm install + + +COPY . . + + +RUN npm run build + + +RUN npm install -g serve + + +EXPOSE 5173 + + +CMD ["serve", "-s", "dist", "-l", "5173"] diff --git a/frontend/eslint.config.js b/frontend/eslint.config.js index 092408a..cc5c897 100644 --- a/frontend/eslint.config.js +++ b/frontend/eslint.config.js @@ -1,28 +1,28 @@ -import js from '@eslint/js' -import globals from 'globals' -import reactHooks from 'eslint-plugin-react-hooks' -import reactRefresh from 'eslint-plugin-react-refresh' -import tseslint from 'typescript-eslint' - -export default tseslint.config( - { ignores: ['dist'] }, - { - extends: [js.configs.recommended, ...tseslint.configs.recommended], - files: ['**/*.{ts,tsx}'], - languageOptions: { - ecmaVersion: 2020, - globals: globals.browser, - }, - plugins: { - 'react-hooks': reactHooks, - 'react-refresh': reactRefresh, - }, - rules: { - ...reactHooks.configs.recommended.rules, - 'react-refresh/only-export-components': [ - 'warn', - { allowConstantExport: true }, - ], - }, - }, -) +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' + +export default tseslint.config( + { ignores: ['dist'] }, + { + extends: [js.configs.recommended, ...tseslint.configs.recommended], + files: ['**/*.{ts,tsx}'], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + plugins: { + 'react-hooks': reactHooks, + 'react-refresh': reactRefresh, + }, + rules: { + ...reactHooks.configs.recommended.rules, + 'react-refresh/only-export-components': [ + 'warn', + { allowConstantExport: true }, + ], + }, + }, +) diff --git a/frontend/index.html b/frontend/index.html index ce85df7..15eab09 100644 --- a/frontend/index.html +++ b/frontend/index.html @@ -1,12 +1,12 @@ - - - - - - Health AI - - -
- - - + + + + + + Health AI + + +
+ + + diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 3e11ca0..89b222e 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -1,4976 +1,4976 @@ -{ - "name": "coconuts", - "version": "0.0.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "coconuts", - "version": "0.0.0", - "dependencies": { - "@emotion/react": "^11.13.3", - "@emotion/styled": "^11.13.0", - "@gsap/react": "^2.1.1", - "@mui/icons-material": "^6.4.3", - "@mui/material": "^6.1.5", - "@react-oauth/google": "^0.12.1", - "@reduxjs/toolkit": "^2.3.0", - "appwrite": "^16.0.2", - "final-form": "^4.20.10", - "gsap": "^3.12.5", - "react": "^18.3.1", - "react-dom": "^18.3.1", - "react-final-form": "^6.5.9", - "react-icons": "^5.3.0", - "react-redux": "^9.1.2", - "react-router-dom": "^6.27.0" - }, - "devDependencies": { - "@eslint/js": "^9.13.0", - "@types/react": "^18.3.11", - "@types/react-dom": "^18.3.1", - "@vitejs/plugin-react": "^4.3.3", - "autoprefixer": "^10.4.20", - 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"@react-oauth/google": "^0.12.1", - "@reduxjs/toolkit": "^2.3.0", - "appwrite": "^16.0.2", - "final-form": "^4.20.10", - "gsap": "^3.12.5", - "react": "^18.3.1", - "react-dom": "^18.3.1", - "react-final-form": "^6.5.9", - "react-icons": "^5.3.0", - "react-redux": "^9.1.2", - "react-router-dom": "^6.27.0" - }, - "devDependencies": { - "@eslint/js": "^9.13.0", - "@types/react": "^18.3.11", - "@types/react-dom": "^18.3.1", - "@vitejs/plugin-react": "^4.3.3", - "autoprefixer": "^10.4.20", - "eslint": "^9.13.0", - "eslint-plugin-react-hooks": "^5.0.0", - "eslint-plugin-react-refresh": "^0.4.13", - "globals": "^15.11.0", - "postcss": "^8.4.47", - "tailwindcss": "^3.4.14", - "typescript": "~5.6.2", - "typescript-eslint": "^8.10.0", - "vite": "^5.4.9" - } -} +{ + "name": "coconuts", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc -b && vite build", + "lint": "eslint .", + "preview": "vite preview" + }, + "dependencies": { + "@emotion/react": "^11.13.3", + "@emotion/styled": "^11.13.0", + "@gsap/react": "^2.1.1", + "@mui/icons-material": "^6.4.3", + "@mui/material": "^6.1.5", + "@react-oauth/google": "^0.12.1", + "@reduxjs/toolkit": "^2.3.0", + "appwrite": "^16.0.2", + "final-form": "^4.20.10", + "gsap": "^3.12.5", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-final-form": "^6.5.9", + "react-icons": "^5.3.0", + "react-redux": "^9.1.2", + "react-router-dom": "^6.27.0" + }, + "devDependencies": { + "@eslint/js": "^9.13.0", + "@types/react": "^18.3.11", + "@types/react-dom": "^18.3.1", + "@vitejs/plugin-react": "^4.3.3", + "autoprefixer": "^10.4.20", + "eslint": "^9.13.0", + "eslint-plugin-react-hooks": "^5.0.0", + "eslint-plugin-react-refresh": "^0.4.13", + "globals": "^15.11.0", + "postcss": "^8.4.47", + "tailwindcss": "^3.4.14", + "typescript": "~5.6.2", + "typescript-eslint": "^8.10.0", + "vite": "^5.4.9" + } +} diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js index 2e7af2b..5eec88d 100644 --- a/frontend/postcss.config.js +++ b/frontend/postcss.config.js @@ -1,6 +1,6 @@ -export default { - plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -} +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index e53c710..2232af8 100644 --- a/frontend/src/App.tsx +++ b/frontend/src/App.tsx @@ -1,45 +1,45 @@ -import { BrowserRouter as Router, Route, Routes, Outlet } from 'react-router-dom'; -import Navigation from './Components/Navigation'; -import {Home} from './pages/LandingPage'; -import RegistrationForm from "./Components/RegistrationForm"; -import LoginForm from "./Components/LoginForm"; -import ChatHistory from "./Components/ChatHistory"; -import HomePage from './pages/HomePage'; -import NewChatPage from "./Components/NewChatPage"; -import About from "./Components/About.tsx"; -import Contact from "./Components/Contact.tsx"; -import Profile from "./Components/Profile.tsx"; - -const Layout = () => ( -
- -
-
- -
-
-
-); - -function App() { - return ( - - - } /> - } /> - } /> - } /> - } /> - } /> - }> - } /> - } /> - } /> - } /> - - - - ); -} - -export default App; +import { BrowserRouter as Router, Route, Routes, Outlet } from 'react-router-dom'; +import Navigation from './Components/Navigation'; +import {Home} from './pages/LandingPage'; +import RegistrationForm from "./Components/RegistrationForm"; +import LoginForm from "./Components/LoginForm"; +import ChatHistory from "./Components/ChatHistory"; +import HomePage from './pages/HomePage'; +import NewChatPage from "./Components/NewChatPage"; +import About from "./Components/About.tsx"; +import Contact from "./Components/Contact.tsx"; +import Profile from "./Components/Profile.tsx"; + +const Layout = () => ( +
+ +
+
+ +
+
+
+); + +function App() { + return ( + + + } /> + } /> + } /> + } /> + } /> + } /> + }> + } /> + } /> + } /> + } /> + + + + ); +} + +export default App; diff --git a/frontend/src/Components/About.tsx b/frontend/src/Components/About.tsx index 1605479..83fc057 100644 --- a/frontend/src/Components/About.tsx +++ b/frontend/src/Components/About.tsx @@ -1,109 +1,109 @@ -import React, { useState, useEffect } from 'react'; -import { Box, Typography, Grid, Paper } from '@mui/material'; -import {Navbar} from '../pages/LandingPage'; -import MedicalServicesIcon from '@mui/icons-material/MedicalServices'; -import LocalHospitalIcon from '@mui/icons-material/LocalHospital'; -import CodeIcon from '@mui/icons-material/Code'; - -const About: React.FC = () => { - const [user, setUser] = useState(null); - - useEffect(() => { - const storedUser = localStorage.getItem('user'); - if (storedUser) { - setUser(JSON.parse(storedUser)); - } - }, []); - - return ( - - {/* Navigation bar */} - - - {/* Main content with top padding to account for fixed Navbar */} - - - About Health AI - - - Your Personal AI Assistant for Tailored Drug Recommendations - - - {/* Project Information Card */} - - - - - - About the Project - - - - Health AI is a cutting-edge application specializing in providing personalized drug recommendations and medication advice. - Leveraging advanced AI models like Mistral and powerful search technologies such as Elasticsearch, our platform delivers accurate, - context-aware suggestions for both over-the-counter and prescription medications. - - - Our backend utilizes modern technologies including Flask, PostgreSQL, and Google OAuth, ensuring robust security and reliable performance. - We also use long-term conversational memory to continuously enhance our responses. - - - - {/* How It Works Card */} - - - - - - How It Works - - - - Our system uses natural language processing to understand user queries and extract key details such as age, medical history, - and medication type. It then employs vector search techniques to fetch the most relevant information from a comprehensive drug database, - ensuring precise recommendations. - - - Health AI validates its responses to guarantee consistency and reliability, making it an innovative solution for personalized healthcare guidance. - - - - {/* Future Enhancements Card */} - - - - - - What's Next? - - - - We are continuously improving Health AI by integrating additional data sources and refining our AI algorithms. - Future enhancements include real-time drug interaction checks, comprehensive patient monitoring, - and seamless integration with healthcare providers. Stay tuned for more exciting updates and features as we strive to make healthcare more accessible and efficient. - - - - - {/* Footer */} - - - © {new Date().getFullYear()} Health AI. All rights reserved. - - - - - ); -}; - -export default About; +import React, { useState, useEffect } from 'react'; +import { Box, Typography, Grid, Paper } from '@mui/material'; +import {Navbar} from '../pages/LandingPage'; +import MedicalServicesIcon from '@mui/icons-material/MedicalServices'; +import LocalHospitalIcon from '@mui/icons-material/LocalHospital'; +import CodeIcon from '@mui/icons-material/Code'; + +const About: React.FC = () => { + const [user, setUser] = useState(null); + + useEffect(() => { + const storedUser = localStorage.getItem('user'); + if (storedUser) { + setUser(JSON.parse(storedUser)); + } + }, []); + + return ( + + {/* Navigation bar */} + + + {/* Main content with top padding to account for fixed Navbar */} + + + About Health AI + + + Your Personal AI Assistant for Tailored Drug Recommendations + + + {/* Project Information Card */} + + + + + + About the Project + + + + Health AI is a cutting-edge application specializing in providing personalized drug recommendations and medication advice. + Leveraging advanced AI models like Mistral and powerful search technologies such as Elasticsearch, our platform delivers accurate, + context-aware suggestions for both over-the-counter and prescription medications. + + + Our backend utilizes modern technologies including Flask, PostgreSQL, and Google OAuth, ensuring robust security and reliable performance. + We also use long-term conversational memory to continuously enhance our responses. + + + + {/* How It Works Card */} + + + + + + How It Works + + + + Our system uses natural language processing to understand user queries and extract key details such as age, medical history, + and medication type. It then employs vector search techniques to fetch the most relevant information from a comprehensive drug database, + ensuring precise recommendations. + + + Health AI validates its responses to guarantee consistency and reliability, making it an innovative solution for personalized healthcare guidance. + + + + {/* Future Enhancements Card */} + + + + + + What's Next? + + + + We are continuously improving Health AI by integrating additional data sources and refining our AI algorithms. + Future enhancements include real-time drug interaction checks, comprehensive patient monitoring, + and seamless integration with healthcare providers. Stay tuned for more exciting updates and features as we strive to make healthcare more accessible and efficient. + + + + + {/* Footer */} + + + © {new Date().getFullYear()} Health AI. All rights reserved. + + + + + ); +}; + +export default About; diff --git a/frontend/src/Components/ChatDetails.tsx b/frontend/src/Components/ChatDetails.tsx index 252b4cd..31612ee 100644 --- a/frontend/src/Components/ChatDetails.tsx +++ b/frontend/src/Components/ChatDetails.tsx @@ -1,57 +1,57 @@ -import React, { useEffect, useState } from 'react'; -import { useParams, useLocation } from 'react-router-dom'; - -interface ChatHistoryItem { - id: number; - chat: string; - created_at: string; -} - -const ChatDetails: React.FC = () => { - const { id } = useParams<{ id: string }>(); - const location = useLocation(); - const [chat, setChat] = useState(location.state?.chat || null); - const [error, setError] = useState(null); - - useEffect(() => { - if (!chat && id) { - fetch(`http://localhost:5000/api/chat_history_detail?id=${encodeURIComponent(id)}`) - .then((res) => { - if (!res.ok) { - throw new Error('Chat not found'); - } - return res.json(); - }) - .then((data) => { - if (data.error) { - setError(data.error); - } else { - setChat(data.chat); - } - }) - .catch((err) => setError(err.message)); - } - }, [id, chat]); - - if (error) { - return
Error: {error}
; - } - - if (!chat) { - return
Loading chat details...
; - } - - return ( -
-

Chat Details

-
- {chat.chat.split('\n').map((line, index) => ( -

{line}

- ))} -
- {new Date(chat.created_at).toLocaleString()} -
- ); -}; - -export default ChatDetails; +import React, { useEffect, useState } from 'react'; +import { useParams, useLocation } from 'react-router-dom'; + +interface ChatHistoryItem { + id: number; + chat: string; + created_at: string; +} + +const ChatDetails: React.FC = () => { + const { id } = useParams<{ id: string }>(); + const location = useLocation(); + const [chat, setChat] = useState(location.state?.chat || null); + const [error, setError] = useState(null); + + useEffect(() => { + if (!chat && id) { + fetch(`http://localhost:5000/api/chat_history_detail?id=${encodeURIComponent(id)}`) + .then((res) => { + if (!res.ok) { + throw new Error('Chat not found'); + } + return res.json(); + }) + .then((data) => { + if (data.error) { + setError(data.error); + } else { + setChat(data.chat); + } + }) + .catch((err) => setError(err.message)); + } + }, [id, chat]); + + if (error) { + return
Error: {error}
; + } + + if (!chat) { + return
Loading chat details...
; + } + + return ( +
+

Chat Details

+
+ {chat.chat.split('\n').map((line, index) => ( +

{line}

+ ))} +
+ {new Date(chat.created_at).toLocaleString()} +
+ ); +}; + +export default ChatDetails; diff --git a/frontend/src/Components/ChatHistory.tsx b/frontend/src/Components/ChatHistory.tsx index 6708563..5dc4dee 100644 --- a/frontend/src/Components/ChatHistory.tsx +++ b/frontend/src/Components/ChatHistory.tsx @@ -1,158 +1,158 @@ -import React, { useEffect, useState } from 'react'; -import { useNavigate } from 'react-router-dom'; -import { Box, Typography, Paper, IconButton } from '@mui/material'; -import DeleteIcon from '@mui/icons-material/Delete'; - -interface ChatHistoryItem { - id: number; - chat: string; - created_at: string; -} - -const ChatHistory: React.FC = () => { - const [history, setHistory] = useState([]); - const [error, setError] = useState(null); - const navigate = useNavigate(); - - useEffect(() => { - const storedUser = localStorage.getItem('user'); - if (storedUser) { - const user = JSON.parse(storedUser); - const email = user.email; - fetch(`http://localhost:5000/api/chat_history?email=${encodeURIComponent(email)}`) - .then((res) => res.json()) - .then((data) => { - if (data.error) { - setError(data.error); - } else { - setHistory(data.history); - } - }) - .catch(() => setError('Error fetching chat history')); - } else { - setError('User not logged in'); - } - }, []); - - const handleClick = (item: ChatHistoryItem) => { - navigate(`/dashboard/chat/${item.id}`, { state: { selectedChat: item } }); - }; - - const handleDelete = (chatId: number) => { - if (window.confirm('Are you sure that you want to delete that chat?')) { - fetch(`http://localhost:5000/api/chat_history?id=${chatId}`, { - method: 'DELETE' - }) - .then((res) => res.json()) - .then((data) => { - if (data.error) { - setError(data.error); - } else { - setHistory(history.filter((chat) => chat.id !== chatId)); - } - }) - .catch(() => setError('Error deleting chat')); - } - }; - - return ( - - - Chat History - - {error ? ( - - {error} - - ) : ( - - {history.length === 0 ? ( - - No chat history found. - - ) : ( - history.map((item) => { - const lines = item.chat.split("\n"); - let firstUserMessage = lines[0]; - if (firstUserMessage.startsWith("User:")) { - firstUserMessage = firstUserMessage.replace("User:", "").trim(); - } - return ( - - handleClick(item)} - > - - {firstUserMessage} - - - {new Date(item.created_at).toLocaleString()} - - - { - e.stopPropagation(); - handleDelete(item.id); - }} - sx={{ color: '#d32f2f' }} - > - - - - ); - }) - )} - - )} - - ); -}; - -export default ChatHistory; +import React, { useEffect, useState } from 'react'; +import { useNavigate } from 'react-router-dom'; +import { Box, Typography, Paper, IconButton } from '@mui/material'; +import DeleteIcon from '@mui/icons-material/Delete'; + +interface ChatHistoryItem { + id: number; + chat: string; + created_at: string; +} + +const ChatHistory: React.FC = () => { + const [history, setHistory] = useState([]); + const [error, setError] = useState(null); + const navigate = useNavigate(); + + useEffect(() => { + const storedUser = localStorage.getItem('user'); + if (storedUser) { + const user = JSON.parse(storedUser); + const email = user.email; + fetch(`http://localhost:5000/api/chat_history?email=${encodeURIComponent(email)}`) + .then((res) => res.json()) + .then((data) => { + if (data.error) { + setError(data.error); + } else { + setHistory(data.history); + } + }) + .catch(() => setError('Error fetching chat history')); + } else { + setError('User not logged in'); + } + }, []); + + const handleClick = (item: ChatHistoryItem) => { + navigate(`/dashboard/chat/${item.id}`, { state: { selectedChat: item } }); + }; + + const handleDelete = (chatId: number) => { + if (window.confirm('Are you sure that you want to delete that chat?')) { + fetch(`http://localhost:5000/api/chat_history?id=${chatId}`, { + method: 'DELETE' + }) + .then((res) => res.json()) + .then((data) => { + if (data.error) { + setError(data.error); + } else { + setHistory(history.filter((chat) => chat.id !== chatId)); + } + }) + .catch(() => setError('Error deleting chat')); + } + }; + + return ( + + + Chat History + + {error ? ( + + {error} + + ) : ( + + {history.length === 0 ? ( + + No chat history found. + + ) : ( + history.map((item) => { + const lines = item.chat.split("\n"); + let firstUserMessage = lines[0]; + if (firstUserMessage.startsWith("User:")) { + firstUserMessage = firstUserMessage.replace("User:", "").trim(); + } + return ( + + handleClick(item)} + > + + {firstUserMessage} + + + {new Date(item.created_at).toLocaleString()} + + + { + e.stopPropagation(); + handleDelete(item.id); + }} + sx={{ color: '#d32f2f' }} + > + + + + ); + }) + )} + + )} + + ); +}; + +export default ChatHistory; diff --git a/frontend/src/Components/Contact.tsx b/frontend/src/Components/Contact.tsx index 4ac3622..8313ca1 100644 --- a/frontend/src/Components/Contact.tsx +++ b/frontend/src/Components/Contact.tsx @@ -1,129 +1,129 @@ -import React, { useState, useEffect } from 'react'; -import { Box, Typography, Paper, Grid } from '@mui/material'; -import {Navbar} from '../pages/LandingPage'; -import SchoolIcon from '@mui/icons-material/School'; -import DeveloperModeIcon from '@mui/icons-material/DeveloperMode'; -import EmailIcon from '@mui/icons-material/Email'; - -const Contact: React.FC = () => { - const [user, setUser] = useState(null); - - useEffect(() => { - const storedUser = localStorage.getItem('user'); - if (storedUser) { - setUser(JSON.parse(storedUser)); - } - }, []); - - return ( - - {/* Navbar with navigation links */} - - - {/* Main content with spacing for fixed Navbar */} - - - - Contact - - - {/* University Info */} - - - - - - Technical University of Košice - - - KEMT Department - - - - - {/* Developer Info */} - - - - - - Developer - - - oleh.poiasnik@student.tuke.sk - - - - - {/* Additional Contact Option */} - - - - - - Email Us - - - For any inquiries or further information about Health AI, please get in touch! - - - - - - - - © {new Date().getFullYear()} Health AI. All rights reserved. - - - - - - ); -}; - -export default Contact; +import React, { useState, useEffect } from 'react'; +import { Box, Typography, Paper, Grid } from '@mui/material'; +import {Navbar} from '../pages/LandingPage'; +import SchoolIcon from '@mui/icons-material/School'; +import DeveloperModeIcon from '@mui/icons-material/DeveloperMode'; +import EmailIcon from '@mui/icons-material/Email'; + +const Contact: React.FC = () => { + const [user, setUser] = useState(null); + + useEffect(() => { + const storedUser = localStorage.getItem('user'); + if (storedUser) { + setUser(JSON.parse(storedUser)); + } + }, []); + + return ( + + {/* Navbar with navigation links */} + + + {/* Main content with spacing for fixed Navbar */} + + + + Contact + + + {/* University Info */} + + + + + + Technical University of Košice + + + KEMT Department + + + + + {/* Developer Info */} + + + + + + Developer + + + oleh.poiasnik@student.tuke.sk + + + + + {/* Additional Contact Option */} + + + + + + Email Us + + + For any inquiries or further information about Health AI, please get in touch! + + + + + + + + © {new Date().getFullYear()} Health AI. All rights reserved. + + + + + + ); +}; + +export default Contact; diff --git a/frontend/src/Components/LoginForm.tsx b/frontend/src/Components/LoginForm.tsx index 63c8423..34ba033 100644 --- a/frontend/src/Components/LoginForm.tsx +++ b/frontend/src/Components/LoginForm.tsx @@ -1,190 +1,190 @@ -import React from 'react'; -import { GoogleLogin, GoogleOAuthProvider } from '@react-oauth/google'; -import { Link, useNavigate } from 'react-router-dom'; - -const CLIENT_ID = "532143017111-4eqtlp0oejqaovj6rf5l1ergvhrp4vao.apps.googleusercontent.com"; - -const LoginFormContent: React.FC = () => { - const navigate = useNavigate(); - - const handleSubmit = async (e: React.FormEvent) => { - e.preventDefault(); - - const emailElement = document.getElementById('email') as HTMLInputElement | null; - const passwordElement = document.getElementById('password') as HTMLInputElement | null; - - if (!emailElement || !passwordElement) { - console.error('One or many inputs are missing'); - return; - } - - const email = emailElement.value; - const password = passwordElement.value; - - try { - const response = await fetch('http://localhost:5000/api/login', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ email, password }), - }); - - const data = await response.json(); - - if (response.ok) { - console.log('Login successful:', data.message); - const loggedInUser = { - name: data.user.name, - email: data.user.email, - picture: data.user.picture || 'https://via.placeholder.com/150', - }; - localStorage.setItem('user', JSON.stringify(loggedInUser)); - navigate('/dashboard'); - } else { - console.error('Error:', data.error); - } - } catch (error) { - console.error('Error loginning:', error); - } - }; - - const handleGoogleLoginSuccess = async (response: any) => { - try { - const res = await fetch('http://localhost:5000/api/verify', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ token: response.credential }), - }); - const data = await res.json(); - const loggedInUser = { - name: data.user.name, - email: data.user.email, - picture: data.user.picture || 'https://via.placeholder.com/150', - }; - localStorage.setItem('user', JSON.stringify(loggedInUser)); - navigate('/dashboard'); - } catch (error) { - console.error('Error token verification:', error); - } - }; - - const handleGoogleLoginError = () => { - console.error('Error auth through Google'); - }; - - - return ( -
-
-

- Sign In -

-
-
- - -
-
- - -
- -
-
- OR -
-
- -
-

- Don't have an account?{' '} - - Sign Up - -

-
-
- ); -}; - -const LoginForm: React.FC = () => { - return ( - - - - ); -}; - -export default LoginForm; +import React from 'react'; +import { GoogleLogin, GoogleOAuthProvider } from '@react-oauth/google'; +import { Link, useNavigate } from 'react-router-dom'; + +const CLIENT_ID = "532143017111-4eqtlp0oejqaovj6rf5l1ergvhrp4vao.apps.googleusercontent.com"; + +const LoginFormContent: React.FC = () => { + const navigate = useNavigate(); + + const handleSubmit = async (e: React.FormEvent) => { + e.preventDefault(); + + const emailElement = document.getElementById('email') as HTMLInputElement | null; + const passwordElement = document.getElementById('password') as HTMLInputElement | null; + + if (!emailElement || !passwordElement) { + console.error('One or many inputs are missing'); + return; + } + + const email = emailElement.value; + const password = passwordElement.value; + + try { + const response = await fetch('http://localhost:5000/api/login', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ email, password }), + }); + + const data = await response.json(); + + if (response.ok) { + console.log('Login successful:', data.message); + const loggedInUser = { + name: data.user.name, + email: data.user.email, + picture: data.user.picture || 'https://via.placeholder.com/150', + }; + localStorage.setItem('user', JSON.stringify(loggedInUser)); + navigate('/dashboard'); + } else { + console.error('Error:', data.error); + } + } catch (error) { + console.error('Error loginning:', error); + } + }; + + const handleGoogleLoginSuccess = async (response: any) => { + try { + const res = await fetch('http://localhost:5000/api/verify', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ token: response.credential }), + }); + const data = await res.json(); + const loggedInUser = { + name: data.user.name, + email: data.user.email, + picture: data.user.picture || 'https://via.placeholder.com/150', + }; + localStorage.setItem('user', JSON.stringify(loggedInUser)); + navigate('/dashboard'); + } catch (error) { + console.error('Error token verification:', error); + } + }; + + const handleGoogleLoginError = () => { + console.error('Error auth through Google'); + }; + + + return ( +
+
+

+ Sign In +

+
+
+ + +
+
+ + +
+ +
+
+ OR +
+
+ +
+

+ Don't have an account?{' '} + + Sign Up + +

+
+
+ ); +}; + +const LoginForm: React.FC = () => { + return ( + + + + ); +}; + +export default LoginForm; diff --git a/frontend/src/Components/Navigation.tsx b/frontend/src/Components/Navigation.tsx index 1db872c..86dea2b 100644 --- a/frontend/src/Components/Navigation.tsx +++ b/frontend/src/Components/Navigation.tsx @@ -1,146 +1,146 @@ -import React, { useEffect, useState } from 'react'; -import { Link } from 'react-router-dom'; -import IconButton from '@mui/material/IconButton'; -import Avatar from '@mui/material/Avatar'; -import { MdAddCircleOutline } from "react-icons/md"; -import { GoHistory } from "react-icons/go"; -import { CgLogIn } from "react-icons/cg"; -import BackImage from '../assets/smallheadicon.png'; - -export interface NavigationItem { - icon: React.ReactNode; - title: string; - link: string; -} - -const NavigationItems: NavigationItem[] = [ - { - title: 'New Chat', - link: '/dashboard/new-chat', - icon: - }, - { - title: 'History', - link: '/dashboard/history', - icon: - } -]; - -interface NavigationProps { - isExpanded: boolean; -} - -const Navigation = ({ isExpanded = false }: NavigationProps) => { - const [theme, setTheme] = useState<'dark' | 'light'>('light'); - - useEffect(() => { - if (window.matchMedia('(prefers-color-scheme:dark)').matches) { - setTheme('dark'); - } else { - setTheme('light'); - } - }, []); - - useEffect(() => { - if (theme === "dark") { - document.documentElement.classList.add("dark"); - } else { - document.documentElement.classList.remove("dark"); - } - }, [theme]); - - - - const [user, setUser] = useState(null); - useEffect(() => { - const storedUser = localStorage.getItem('user'); - if (storedUser) { - setUser(JSON.parse(storedUser)); - } - }, []); - - return ( -
-
-
- - - Back - - {isExpanded && ( -

- Health AI -

- )} - -
- {NavigationItems.map((item) => ( - - - {item.icon} - - {isExpanded && item.title} - - ))} -
-
-
- - - {user ? ( - - ) : ( - - )} - - - {/**/} -
-
-
- ); -}; - -export default Navigation; +import React, { useEffect, useState } from 'react'; +import { Link } from 'react-router-dom'; +import IconButton from '@mui/material/IconButton'; +import Avatar from '@mui/material/Avatar'; +import { MdAddCircleOutline } from "react-icons/md"; +import { GoHistory } from "react-icons/go"; +import { CgLogIn } from "react-icons/cg"; +import BackImage from '../assets/smallheadicon.png'; + +export interface NavigationItem { + icon: React.ReactNode; + title: string; + link: string; +} + +const NavigationItems: NavigationItem[] = [ + { + title: 'New Chat', + link: '/dashboard/new-chat', + icon: + }, + { + title: 'History', + link: '/dashboard/history', + icon: + } +]; + +interface NavigationProps { + isExpanded: boolean; +} + +const Navigation = ({ isExpanded = false }: NavigationProps) => { + const [theme, setTheme] = useState<'dark' | 'light'>('light'); + + useEffect(() => { + if (window.matchMedia('(prefers-color-scheme:dark)').matches) { + setTheme('dark'); + } else { + setTheme('light'); + } + }, []); + + useEffect(() => { + if (theme === "dark") { + document.documentElement.classList.add("dark"); + } else { + document.documentElement.classList.remove("dark"); + } + }, [theme]); + + + + const [user, setUser] = useState(null); + useEffect(() => { + const storedUser = localStorage.getItem('user'); + if (storedUser) { + setUser(JSON.parse(storedUser)); + } + }, []); + + return ( +
+
+
+ + + Back + + {isExpanded && ( +

+ Health AI +

+ )} + +
+ {NavigationItems.map((item) => ( + + + {item.icon} + + {isExpanded && item.title} + + ))} +
+
+
+ + + {user ? ( + + ) : ( + + )} + + + {/**/} +
+
+
+ ); +}; + +export default Navigation; diff --git a/frontend/src/Components/NewChatPage.tsx b/frontend/src/Components/NewChatPage.tsx index 39b1422..51e45be 100644 --- a/frontend/src/Components/NewChatPage.tsx +++ b/frontend/src/Components/NewChatPage.tsx @@ -1,186 +1,186 @@ -import React, { useEffect, useState, useRef } from 'react'; -import { useNavigate } from 'react-router-dom'; -import gsap from 'gsap'; -import { useGSAP } from '@gsap/react'; -import { useLazySendChatQuestionQuery } from '../store/api/chatApi'; -import callCenterIcon from '../assets/call-center.png'; - -interface ChatMessage { - sender: string; - text: string; -} - -const NewChatPage: React.FC = () => { - const [sendChatQuestion, { isLoading, isFetching }] = useLazySendChatQuestionQuery(); - const [message, setMessage] = useState(''); - const [chatHistory, setChatHistory] = useState([]); - const navigate = useNavigate(); - const messagesEndRef = useRef(null); - - useEffect(() => { - messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' }); - }, [chatHistory, isLoading, isFetching]); - - useEffect(() => { - setChatHistory([]); - }, []); - - async function onSubmit() { - if (!message.trim()) return; - - const newUserMessage: ChatMessage = { sender: 'User', text: message }; - const updatedHistory = [...chatHistory, newUserMessage]; - setChatHistory(updatedHistory); - const userMessage = message; - setMessage(''); - - const storedUser = localStorage.getItem('user'); - const email = storedUser ? JSON.parse(storedUser).email : ''; - - const question = { query: userMessage, email }; - - try { - const res = await sendChatQuestion(question).unwrap(); - console.log('Response from server:', res); - - let bestAnswer = res.response.best_answer; - if (typeof bestAnswer !== 'string') { - bestAnswer = String(bestAnswer); - } - bestAnswer = bestAnswer.trim(); - - const newAssistantMessage: ChatMessage = { sender: 'Assistant', text: bestAnswer }; - const newUpdatedHistory = [...updatedHistory, newAssistantMessage]; - setChatHistory(newUpdatedHistory); - - if (res.response.chatId) { - const chatString = newUpdatedHistory - .map(msg => (msg.sender === 'User' ? 'User: ' : 'Bot: ') + msg.text) - .join('\n'); - navigate(`/dashboard/chat/${res.response.chatId}`, { - replace: true, - state: { - selectedChat: { - id: res.response.chatId, - chat: chatString, - created_at: new Date().toISOString() - } - } - }); - } - } catch (error) { - console.error('Error:', error); - setChatHistory(prev => [ - ...prev, - { sender: 'Assistant', text: 'Something went wrong' } - ]); - } - } - - useGSAP(() => { - gsap.from('#input', { opacity: 0, y: 5, ease: 'power2.inOut', duration: 0.5 }); - }, []); - - return ( -
-
- {chatHistory.length > 0 ? ( - <> - {chatHistory.map((msg, index) => ( -
- {msg.sender === 'Assistant' && ( - Call Center Icon - )} -
- {msg.text.split('\n').map((line, i) => ( -

{line}

- ))} -
-
- ))} - - {(isLoading || isFetching) && ( -
- Call Center Icon -
-
- - - - - Assistant is typing... -
-
-
- )} - - ) : ( -
-

- Start a New Chat -

-
- )} -
-
- -
-
- setMessage(e.target.value)} - className="w-full px-5 py-2 rounded-l-xl outline-none border border-gray-300" - /> - -
-
-
- ); -}; - -export default NewChatPage; +import React, { useEffect, useState, useRef } from 'react'; +import { useNavigate } from 'react-router-dom'; +import gsap from 'gsap'; +import { useGSAP } from '@gsap/react'; +import { useLazySendChatQuestionQuery } from '../store/api/chatApi'; +import callCenterIcon from '../assets/call-center.png'; + +interface ChatMessage { + sender: string; + text: string; +} + +const NewChatPage: React.FC = () => { + const [sendChatQuestion, { isLoading, isFetching }] = useLazySendChatQuestionQuery(); + const [message, setMessage] = useState(''); + const [chatHistory, setChatHistory] = useState([]); + const navigate = useNavigate(); + const messagesEndRef = useRef(null); + + useEffect(() => { + messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' }); + }, [chatHistory, isLoading, isFetching]); + + useEffect(() => { + setChatHistory([]); + }, []); + + async function onSubmit() { + if (!message.trim()) return; + + const newUserMessage: ChatMessage = { sender: 'User', text: message }; + const updatedHistory = [...chatHistory, newUserMessage]; + setChatHistory(updatedHistory); + const userMessage = message; + setMessage(''); + + const storedUser = localStorage.getItem('user'); + const email = storedUser ? JSON.parse(storedUser).email : ''; + + const question = { query: userMessage, email }; + + try { + const res = await sendChatQuestion(question).unwrap(); + console.log('Response from server:', res); + + let bestAnswer = res.response.best_answer; + if (typeof bestAnswer !== 'string') { + bestAnswer = String(bestAnswer); + } + bestAnswer = bestAnswer.trim(); + + const newAssistantMessage: ChatMessage = { sender: 'Assistant', text: bestAnswer }; + const newUpdatedHistory = [...updatedHistory, newAssistantMessage]; + setChatHistory(newUpdatedHistory); + + if (res.response.chatId) { + const chatString = newUpdatedHistory + .map(msg => (msg.sender === 'User' ? 'User: ' : 'Bot: ') + msg.text) + .join('\n'); + navigate(`/dashboard/chat/${res.response.chatId}`, { + replace: true, + state: { + selectedChat: { + id: res.response.chatId, + chat: chatString, + created_at: new Date().toISOString() + } + } + }); + } + } catch (error) { + console.error('Error:', error); + setChatHistory(prev => [ + ...prev, + { sender: 'Assistant', text: 'Something went wrong' } + ]); + } + } + + useGSAP(() => { + gsap.from('#input', { opacity: 0, y: 5, ease: 'power2.inOut', duration: 0.5 }); + }, []); + + return ( +
+
+ {chatHistory.length > 0 ? ( + <> + {chatHistory.map((msg, index) => ( +
+ {msg.sender === 'Assistant' && ( + Call Center Icon + )} +
+ {msg.text.split('\n').map((line, i) => ( +

{line}

+ ))} +
+
+ ))} + + {(isLoading || isFetching) && ( +
+ Call Center Icon +
+
+ + + + + Assistant is typing... +
+
+
+ )} + + ) : ( +
+

+ Start a New Chat +

+
+ )} +
+
+ +
+
+ setMessage(e.target.value)} + className="w-full px-5 py-2 rounded-l-xl outline-none border border-gray-300" + /> + +
+
+
+ ); +}; + +export default NewChatPage; diff --git a/frontend/src/Components/Profile.tsx b/frontend/src/Components/Profile.tsx index 9f75b06..24c3f42 100644 --- a/frontend/src/Components/Profile.tsx +++ b/frontend/src/Components/Profile.tsx @@ -1,233 +1,233 @@ -import React, { useEffect, useState } from 'react'; -import { - Box, - Typography, - Avatar, - Paper, - Button, - TextField, - IconButton, - Divider -} from '@mui/material'; -import { useNavigate } from 'react-router-dom'; -import { Navbar } from '../pages/LandingPage'; -import CheckIcon from '@mui/icons-material/Check'; -import CloseIcon from '@mui/icons-material/Close'; - -const Profile: React.FC = () => { - const [user, setUser] = useState(null); - const [editing, setEditing] = useState(false); - const [formData, setFormData] = useState({ - name: '', - email: '', - phone: '', - role: '', - bio: '', - picture: '', - }); - const navigate = useNavigate(); - - useEffect(() => { - const storedUser = localStorage.getItem('user'); - if (storedUser) { - const parsedUser = JSON.parse(storedUser); - setUser(parsedUser); - setFormData({ - name: parsedUser.name || '', - email: parsedUser.email || '', - phone: parsedUser.phone || '', - role: parsedUser.role || '', - bio: parsedUser.bio || '', - picture: parsedUser.picture || '', - }); - } else { - navigate('/login'); - } - }, [navigate]); - - const handleChange = (e: React.ChangeEvent) => { - setFormData(prev => ({ - ...prev, - [e.target.name]: e.target.value, - })); - }; - - const handleCancelEdit = () => { - setFormData({ - name: user.name || '', - email: user.email || '', - phone: user.phone || '', - role: user.role || '', - bio: user.bio || '', - picture: user.picture || '', - }); - setEditing(false); - }; - - const handleSaveEdit = async () => { - try { - const response = await fetch('http://localhost:5000/api/update_profile', { - method: 'PUT', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify(formData), - }); - const data = await response.json(); - if (response.ok) { - const updatedUser = { ...user, ...formData }; - setUser(updatedUser); - localStorage.setItem('user', JSON.stringify(updatedUser)); - setEditing(false); - } else { - alert(data.error || 'Error updating profile'); - } - } catch (err) { - console.error(err); - alert('Error updating profile'); - } - }; - - if (!user) { - return null; - } - - return ( - - - - - - {editing ? ( - <> - - - - - - - - - - - - - - - - ) : ( - <> - - {user.name} - - - {user.email} - - - - Phone: {user.phone || 'Not provided'} - - - Role: {user.role || 'User'} - - - Bio: {user.bio || 'No bio available'} - - - - )} - - - - ); -}; - -export default Profile; +import React, { useEffect, useState } from 'react'; +import { + Box, + Typography, + Avatar, + Paper, + Button, + TextField, + IconButton, + Divider +} from '@mui/material'; +import { useNavigate } from 'react-router-dom'; +import { Navbar } from '../pages/LandingPage'; +import CheckIcon from '@mui/icons-material/Check'; +import CloseIcon from '@mui/icons-material/Close'; + +const Profile: React.FC = () => { + const [user, setUser] = useState(null); + const [editing, setEditing] = useState(false); + const [formData, setFormData] = useState({ + name: '', + email: '', + phone: '', + role: '', + bio: '', + picture: '', + }); + const navigate = useNavigate(); + + useEffect(() => { + const storedUser = localStorage.getItem('user'); + if (storedUser) { + const parsedUser = JSON.parse(storedUser); + setUser(parsedUser); + setFormData({ + name: parsedUser.name || '', + email: parsedUser.email || '', + phone: parsedUser.phone || '', + role: parsedUser.role || '', + bio: parsedUser.bio || '', + picture: parsedUser.picture || '', + }); + } else { + navigate('/login'); + } + }, [navigate]); + + const handleChange = (e: React.ChangeEvent) => { + setFormData(prev => ({ + ...prev, + [e.target.name]: e.target.value, + })); + }; + + const handleCancelEdit = () => { + setFormData({ + name: user.name || '', + email: user.email || '', + phone: user.phone || '', + role: user.role || '', + bio: user.bio || '', + picture: user.picture || '', + }); + setEditing(false); + }; + + const handleSaveEdit = async () => { + try { + const response = await fetch('http://localhost:5000/api/update_profile', { + method: 'PUT', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(formData), + }); + const data = await response.json(); + if (response.ok) { + const updatedUser = { ...user, ...formData }; + setUser(updatedUser); + localStorage.setItem('user', JSON.stringify(updatedUser)); + setEditing(false); + } else { + alert(data.error || 'Error updating profile'); + } + } catch (err) { + console.error(err); + alert('Error updating profile'); + } + }; + + if (!user) { + return null; + } + + return ( + + + + + + {editing ? ( + <> + + + + + + + + + + + + + + + + ) : ( + <> + + {user.name} + + + {user.email} + + + + Phone: {user.phone || 'Not provided'} + + + Role: {user.role || 'User'} + + + Bio: {user.bio || 'No bio available'} + + + + )} + + + + ); +}; + +export default Profile; diff --git a/frontend/src/Components/RegistrationForm.tsx b/frontend/src/Components/RegistrationForm.tsx index 657345d..288b4de 100644 --- a/frontend/src/Components/RegistrationForm.tsx +++ b/frontend/src/Components/RegistrationForm.tsx @@ -1,240 +1,240 @@ -import React from 'react'; -import { GoogleLogin, GoogleOAuthProvider } from '@react-oauth/google'; -import { Link, useNavigate } from 'react-router-dom'; - -const CLIENT_ID = "532143017111-4eqtlp0oejqaovj6rf5l1ergvhrp4vao.apps.googleusercontent.com"; - -const RegistrationFormContent: React.FC = () => { - const navigate = useNavigate(); - - const handleSubmit = async (e: React.FormEvent) => { - e.preventDefault(); - - const nameElement = document.getElementById('name') as HTMLInputElement | null; - const emailElement = document.getElementById('email') as HTMLInputElement | null; - const passwordElement = document.getElementById('password') as HTMLInputElement | null; - const confirmPasswordElement = document.getElementById('confirm-password') as HTMLInputElement | null; - - if (!nameElement || !emailElement || !passwordElement || !confirmPasswordElement) { - console.error('One or more input fields are missing'); - return; - } - - const name = nameElement.value; - const email = emailElement.value; - const password = passwordElement.value; - const confirmPassword = confirmPasswordElement.value; - - if (password !== confirmPassword) { - console.error('Passwords do not match'); - alert('Passwords do not match'); - return; - } - - try { - const response = await fetch('http://localhost:5000/api/register', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ name, email, password }), - }); - - const data = await response.json(); - - if (response.ok) { - console.log('User registered successfully:', data.message); - const loggedInUser = { - name, - email, - picture: 'https://via.placeholder.com/150', - }; - localStorage.setItem('user', JSON.stringify(loggedInUser)); - navigate('/dashboard'); - } else { - console.error('Error:', data.error); - alert(data.error); - } - } catch (error) { - console.error('Error registering user:', error); - alert('Error registering user'); - } - }; - - const handleGoogleLoginSuccess = async (response: any) => { - try { - const res = await fetch('http://localhost:5000/api/verify', { - method: 'POST', - headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ token: response.credential }), - }); - const data = await res.json(); - const loggedInUser = { - name: data.user.name, - email: data.user.email, - picture: data.user.picture || 'https://via.placeholder.com/150', - }; - localStorage.setItem('user', JSON.stringify(loggedInUser)); - navigate('/dashboard'); - } catch (error) { - console.error('Error tiken verification:', error); - } - }; - - const handleGoogleLoginError = () => { - console.error('Error auth: Google login failed'); - }; - - - return ( -
-
-

- Create Your Account -

-

- Join us to explore personalized health solutions. -

-
-
- - -
-
- - -
-
- - -
-
- - -
- -
-
- OR -
-
- -
-

- Already have an account?{' '} - - Sign In - -

-
-
- ); -}; - -const RegistrationForm: React.FC = () => { - return ( - - - - ); -}; - -export default RegistrationForm; +import React from 'react'; +import { GoogleLogin, GoogleOAuthProvider } from '@react-oauth/google'; +import { Link, useNavigate } from 'react-router-dom'; + +const CLIENT_ID = "532143017111-4eqtlp0oejqaovj6rf5l1ergvhrp4vao.apps.googleusercontent.com"; + +const RegistrationFormContent: React.FC = () => { + const navigate = useNavigate(); + + const handleSubmit = async (e: React.FormEvent) => { + e.preventDefault(); + + const nameElement = document.getElementById('name') as HTMLInputElement | null; + const emailElement = document.getElementById('email') as HTMLInputElement | null; + const passwordElement = document.getElementById('password') as HTMLInputElement | null; + const confirmPasswordElement = document.getElementById('confirm-password') as HTMLInputElement | null; + + if (!nameElement || !emailElement || !passwordElement || !confirmPasswordElement) { + console.error('One or more input fields are missing'); + return; + } + + const name = nameElement.value; + const email = emailElement.value; + const password = passwordElement.value; + const confirmPassword = confirmPasswordElement.value; + + if (password !== confirmPassword) { + console.error('Passwords do not match'); + alert('Passwords do not match'); + return; + } + + try { + const response = await fetch('http://localhost:5000/api/register', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ name, email, password }), + }); + + const data = await response.json(); + + if (response.ok) { + console.log('User registered successfully:', data.message); + const loggedInUser = { + name, + email, + picture: 'https://via.placeholder.com/150', + }; + localStorage.setItem('user', JSON.stringify(loggedInUser)); + navigate('/dashboard'); + } else { + console.error('Error:', data.error); + alert(data.error); + } + } catch (error) { + console.error('Error registering user:', error); + alert('Error registering user'); + } + }; + + const handleGoogleLoginSuccess = async (response: any) => { + try { + const res = await fetch('http://localhost:5000/api/verify', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ token: response.credential }), + }); + const data = await res.json(); + const loggedInUser = { + name: data.user.name, + email: data.user.email, + picture: data.user.picture || 'https://via.placeholder.com/150', + }; + localStorage.setItem('user', JSON.stringify(loggedInUser)); + navigate('/dashboard'); + } catch (error) { + console.error('Error tiken verification:', error); + } + }; + + const handleGoogleLoginError = () => { + console.error('Error auth: Google login failed'); + }; + + + return ( +
+
+

+ Create Your Account +

+

+ Join us to explore personalized health solutions. +

+
+
+ + +
+
+ + +
+
+ + +
+
+ + +
+ +
+
+ OR +
+
+ +
+

+ Already have an account?{' '} + + Sign In + +

+
+
+ ); +}; + +const RegistrationForm: React.FC = () => { + return ( + + + + ); +}; + +export default RegistrationForm; diff --git a/frontend/src/index.css b/frontend/src/index.css index 8fedf4d..486e5f4 100644 --- a/frontend/src/index.css +++ b/frontend/src/index.css @@ -1,37 +1,37 @@ -@tailwind base; -@tailwind components; -@tailwind utilities; - -/* Приховує скроллбар у браузерах на основі WebKit (Chrome, Safari) */ -.no-scrollbar::-webkit-scrollbar { - display: none; -} - -/* Приховує скроллбар у Firefox */ -.no-scrollbar { - scrollbar-width: none; -} - -/* Приховує скроллбар в Internet Explorer та Edge */ -.no-scrollbar { - -ms-overflow-style: none; -} - -/* HTML:
*/ -.loader { - width: 20px; - aspect-ratio: 2; - --_g: no-repeat radial-gradient(circle closest-side,#000 90%,#0000); - background: - var(--_g) 0% 50%, - var(--_g) 50% 50%, - var(--_g) 100% 50%; - background-size: calc(100%/3) 50%; - animation: l3 1s infinite linear; - } - @keyframes l3 { - 20%{background-position:0% 0%, 50% 50%,100% 50%} - 40%{background-position:0% 100%, 50% 0%,100% 50%} - 60%{background-position:0% 50%, 50% 100%,100% 0%} - 80%{background-position:0% 50%, 50% 50%,100% 100%} +@tailwind base; +@tailwind components; +@tailwind utilities; + +/* Приховує скроллбар у браузерах на основі WebKit (Chrome, Safari) */ +.no-scrollbar::-webkit-scrollbar { + display: none; +} + +/* Приховує скроллбар у Firefox */ +.no-scrollbar { + scrollbar-width: none; +} + +/* Приховує скроллбар в Internet Explorer та Edge */ +.no-scrollbar { + -ms-overflow-style: none; +} + +/* HTML:
*/ +.loader { + width: 20px; + aspect-ratio: 2; + --_g: no-repeat radial-gradient(circle closest-side,#000 90%,#0000); + background: + var(--_g) 0% 50%, + var(--_g) 50% 50%, + var(--_g) 100% 50%; + background-size: calc(100%/3) 50%; + animation: l3 1s infinite linear; + } + @keyframes l3 { + 20%{background-position:0% 0%, 50% 50%,100% 50%} + 40%{background-position:0% 100%, 50% 0%,100% 50%} + 60%{background-position:0% 50%, 50% 100%,100% 0%} + 80%{background-position:0% 50%, 50% 50%,100% 100%} } \ No newline at end of file diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx index ba9ddf8..791008a 100644 --- a/frontend/src/main.tsx +++ b/frontend/src/main.tsx @@ -1,15 +1,15 @@ -import { StrictMode } from 'react' -import { createRoot } from 'react-dom/client' -import './index.css' -import App from './App.tsx' -import { Provider } from 'react-redux' -import store from './store/index.ts' - - -createRoot(document.getElementById('root')!).render( - - - - - , -) +import { StrictMode } from 'react' +import { createRoot } from 'react-dom/client' +import './index.css' +import App from './App.tsx' +import { Provider } from 'react-redux' +import store from './store/index.ts' + + +createRoot(document.getElementById('root')!).render( + + + + + , +) diff --git a/frontend/src/pages/HomePage.tsx b/frontend/src/pages/HomePage.tsx index b134ce8..76fb478 100644 --- a/frontend/src/pages/HomePage.tsx +++ b/frontend/src/pages/HomePage.tsx @@ -1,227 +1,227 @@ -import React, { useEffect, useState, useRef } from 'react'; -import { useLocation, useNavigate } from 'react-router-dom'; -import { useLazySendChatQuestionQuery } from '../store/api/chatApi'; -import callCenterIcon from '../assets/call-center.png'; - -interface ChatMessage { - sender: string; - text: string; -} - -const HomePage: React.FC = () => { - const [sendChatQuestion, { isLoading, isFetching }] = useLazySendChatQuestionQuery(); - const [message, setMessage] = useState(''); - const [chatHistory, setChatHistory] = useState([]); - const messagesEndRef = useRef(null); - - const location = useLocation(); - const navigate = useNavigate(); - const state = (location.state as any) || {}; - - const isNewChat = state.newChat === true; - const selectedChat = state.selectedChat || null; - const selectedChatId = selectedChat ? selectedChat.id : null; - - useEffect(() => { - messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' }); - }, [chatHistory, isLoading, isFetching]); - - useEffect(() => { - if (!isNewChat && selectedChat && selectedChat.chat) { - const messages: ChatMessage[] = selectedChat.chat - .split(/(?=^(User:|Bot:))/m) - .map((msg:any) => { - const trimmed = msg.trim(); - const sender = trimmed.startsWith('User:') ? 'User' : 'Assistant'; - return { - sender, - text: trimmed.replace(/^User:|^Bot:/, '').trim(), - }; - }); - setChatHistory(messages); - } else { - setChatHistory([]); - } - }, [isNewChat, selectedChat]); - - - const formatMessage = (text: string) => { - let lines: string[] = []; - - if (text.includes('\n')) { - lines = text.split('\n'); - } else { - lines = text.split(/(?=\d+\.\s+)/); - } - - lines = lines.map((line) => line.trim()).filter((line) => line !== ''); - if (lines.length === 0) return null; - - return lines.map((line, index) => { - if (/^\d+\.\s*/.test(line)) { - const colonIndex = line.indexOf(':'); - if (colonIndex !== -1) { - const firstPart = line.substring(0, colonIndex); - const rest = line.substring(colonIndex + 1); - return ( -
- {firstPart.trim()}: {rest.trim()} -
- ); - } else { - return ( -
- {line} -
- ); - } - } - return
{line}
; - }); - }; - - const onSubmit = async () => { - if (!message.trim()) return; - const userMessage = message.trim(); - setMessage(''); - setChatHistory((prev) => [...prev, { sender: 'User', text: userMessage }]); - - const storedUser = localStorage.getItem('user'); - const email = storedUser ? JSON.parse(storedUser).email : ''; - const payload = selectedChatId - ? { query: userMessage, chatId: selectedChatId, email } - : { query: userMessage, email }; - - try { - const res = await sendChatQuestion(payload).unwrap(); - let bestAnswer = res.response.best_answer; - if (typeof bestAnswer !== 'string') { - bestAnswer = String(bestAnswer); - } - bestAnswer = bestAnswer.trim(); - - if (bestAnswer) { - setChatHistory((prev) => [...prev, { sender: 'Assistant', text: bestAnswer }]); - } - - if (!selectedChatId && res.response.chatId) { - const updatedChatHistory = [...chatHistory, { sender: 'User', text: userMessage }]; - if (bestAnswer) { - updatedChatHistory.push({ sender: 'Assistant', text: bestAnswer }); - } - const chatString = updatedChatHistory - .map((msg) => (msg.sender === 'User' ? 'User: ' : 'Bot: ') + msg.text) - .join('\n'); - navigate(`/dashboard/chat/${res.response.chatId}`, { - replace: true, - state: { - selectedChat: { - id: res.response.chatId, - chat: chatString, - created_at: new Date().toISOString(), - }, - }, - }); - } - } catch (error) { - console.error('Error:', error); - setChatHistory((prev) => [ - ...prev, - { sender: 'Assistant', text: 'Something went wrong' }, - ]); - } - }; - - return ( -
-
- {chatHistory.length === 0 ? ( -
-

Start a New Chat

-
- ) : ( - chatHistory.map((msg, index) => { - const formattedMessage = formatMessage(msg.text); - if (!formattedMessage) return null; - return ( -
- {msg.sender === 'Assistant' && ( - Call Center Icon - )} -
- {formattedMessage} -
-
- ); - }) - )} - - {(isLoading || isFetching) && ( -
- Call Center Icon -
- - - - - Assistant is typing... -
-
- )} - -
-
- -
-
- setMessage(e.target.value)} - disabled={isLoading || isFetching} - className="w-full px-5 py-2 rounded-l-xl outline-none border border-gray-300" - /> - -
-
-
- ); -}; - -export default HomePage; +import React, { useEffect, useState, useRef } from 'react'; +import { useLocation, useNavigate } from 'react-router-dom'; +import { useLazySendChatQuestionQuery } from '../store/api/chatApi'; +import callCenterIcon from '../assets/call-center.png'; + +interface ChatMessage { + sender: string; + text: string; +} + +const HomePage: React.FC = () => { + const [sendChatQuestion, { isLoading, isFetching }] = useLazySendChatQuestionQuery(); + const [message, setMessage] = useState(''); + const [chatHistory, setChatHistory] = useState([]); + const messagesEndRef = useRef(null); + + const location = useLocation(); + const navigate = useNavigate(); + const state = (location.state as any) || {}; + + const isNewChat = state.newChat === true; + const selectedChat = state.selectedChat || null; + const selectedChatId = selectedChat ? selectedChat.id : null; + + useEffect(() => { + messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' }); + }, [chatHistory, isLoading, isFetching]); + + useEffect(() => { + if (!isNewChat && selectedChat && selectedChat.chat) { + const messages: ChatMessage[] = selectedChat.chat + .split(/(?=^(User:|Bot:))/m) + .map((msg:any) => { + const trimmed = msg.trim(); + const sender = trimmed.startsWith('User:') ? 'User' : 'Assistant'; + return { + sender, + text: trimmed.replace(/^User:|^Bot:/, '').trim(), + }; + }); + setChatHistory(messages); + } else { + setChatHistory([]); + } + }, [isNewChat, selectedChat]); + + + const formatMessage = (text: string) => { + let lines: string[] = []; + + if (text.includes('\n')) { + lines = text.split('\n'); + } else { + lines = text.split(/(?=\d+\.\s+)/); + } + + lines = lines.map((line) => line.trim()).filter((line) => line !== ''); + if (lines.length === 0) return null; + + return lines.map((line, index) => { + if (/^\d+\.\s*/.test(line)) { + const colonIndex = line.indexOf(':'); + if (colonIndex !== -1) { + const firstPart = line.substring(0, colonIndex); + const rest = line.substring(colonIndex + 1); + return ( +
+ {firstPart.trim()}: {rest.trim()} +
+ ); + } else { + return ( +
+ {line} +
+ ); + } + } + return
{line}
; + }); + }; + + const onSubmit = async () => { + if (!message.trim()) return; + const userMessage = message.trim(); + setMessage(''); + setChatHistory((prev) => [...prev, { sender: 'User', text: userMessage }]); + + const storedUser = localStorage.getItem('user'); + const email = storedUser ? JSON.parse(storedUser).email : ''; + const payload = selectedChatId + ? { query: userMessage, chatId: selectedChatId, email } + : { query: userMessage, email }; + + try { + const res = await sendChatQuestion(payload).unwrap(); + let bestAnswer = res.response.best_answer; + if (typeof bestAnswer !== 'string') { + bestAnswer = String(bestAnswer); + } + bestAnswer = bestAnswer.trim(); + + if (bestAnswer) { + setChatHistory((prev) => [...prev, { sender: 'Assistant', text: bestAnswer }]); + } + + if (!selectedChatId && res.response.chatId) { + const updatedChatHistory = [...chatHistory, { sender: 'User', text: userMessage }]; + if (bestAnswer) { + updatedChatHistory.push({ sender: 'Assistant', text: bestAnswer }); + } + const chatString = updatedChatHistory + .map((msg) => (msg.sender === 'User' ? 'User: ' : 'Bot: ') + msg.text) + .join('\n'); + navigate(`/dashboard/chat/${res.response.chatId}`, { + replace: true, + state: { + selectedChat: { + id: res.response.chatId, + chat: chatString, + created_at: new Date().toISOString(), + }, + }, + }); + } + } catch (error) { + console.error('Error:', error); + setChatHistory((prev) => [ + ...prev, + { sender: 'Assistant', text: 'Something went wrong' }, + ]); + } + }; + + return ( +
+
+ {chatHistory.length === 0 ? ( +
+

Start a New Chat

+
+ ) : ( + chatHistory.map((msg, index) => { + const formattedMessage = formatMessage(msg.text); + if (!formattedMessage) return null; + return ( +
+ {msg.sender === 'Assistant' && ( + Call Center Icon + )} +
+ {formattedMessage} +
+
+ ); + }) + )} + + {(isLoading || isFetching) && ( +
+ Call Center Icon +
+ + + + + Assistant is typing... +
+
+ )} + +
+
+ +
+
+ setMessage(e.target.value)} + disabled={isLoading || isFetching} + className="w-full px-5 py-2 rounded-l-xl outline-none border border-gray-300" + /> + +
+
+
+ ); +}; + +export default HomePage; diff --git a/frontend/src/pages/LandingPage.tsx b/frontend/src/pages/LandingPage.tsx index c4bbb90..c3721b8 100644 --- a/frontend/src/pages/LandingPage.tsx +++ b/frontend/src/pages/LandingPage.tsx @@ -1,186 +1,186 @@ -import React, { useState, useEffect } from 'react'; - -import BackImage from '../assets/smallheadicon.png'; -import ArrowDownwardIcon from '@mui/icons-material/ArrowDownward'; -import { Box, Avatar } from '@mui/material'; -import gsap from 'gsap'; -import { useGSAP } from '@gsap/react'; -import LogoutIcon from '@mui/icons-material/Logout'; -import LoginIcon from '@mui/icons-material/Login'; -import { Link, useNavigate } from 'react-router-dom'; - -const BouncingArrow = () => { - return ( - - - - ); -}; - -interface NavbarProps { - user: any; - setUser: (user: any) => void; -} - -const Navbar: React.FC = ({ user, setUser }) => { - const navigate = useNavigate(); - - const handleSignOut = () => { - setUser(null); - localStorage.removeItem('user'); - }; - - return ( - - ); -}; - -const Home: React.FC = () => { - const navigate = useNavigate(); - const [user, setUser] = useState(null); - - useEffect(() => { - const storedUser = localStorage.getItem('user'); - if (storedUser) { - setUser(JSON.parse(storedUser)); - } - }, []); - - useGSAP(() => { - gsap.from('#mainheading', { opacity: 0.3, ease: 'power2.inOut', duration: 0.5 }); - gsap.from('#secondheading', { opacity: 0, y: 5, ease: 'power2.inOut', delay: 0.3, duration: 0.5 }); - gsap.from('#button', { opacity: 0, y: 5, ease: 'power2.inOut', delay: 0.5, duration: 0.5 }); - gsap.from('#features', { opacity: 0, y: 5, ease: 'power2.inOut', delay: 0.7, duration: 0.5 }); - gsap.from('#arrow', { opacity: 0, ease: 'power2.inOut', delay: 2, duration: 0.2 }); - gsap.to('#button', { opacity: 1, ease: 'power2.inOut', delay: 2.5, duration: 0.5 }); - }, []); - - const handleGetStartedClick = () => { - if (!user) { - navigate('/register'); - } else { - navigate('/dashboard'); - } - }; - - return ( -
-
- - -
-

- AI Assistant for Your Health -

- -

- A solution for personalized health support, including tailored medication guidance and wellness - insights. Take care of yourself with the power of modern technology. -

- -
-
-

Personalized Prescription

-

- Receive tailored medication recommendations specifically designed for your needs. -

-
-
-

Health Monitoring

-

- Stay informed about your health with real-time monitoring and AI-driven insights. -

-
-
-

Advanced AI Support

-

- Utilize AI support to ensure you're following the best routines for a healthier lifestyle. -

-
-
- -
-

Try it out

- -
- -
- -
-
-
- -
-

© {new Date().getFullYear()} Health AI. All rights reserved.

-
-
- ); -}; - -export { Home, Navbar }; +import React, { useState, useEffect } from 'react'; + +import BackImage from '../assets/smallheadicon.png'; +import ArrowDownwardIcon from '@mui/icons-material/ArrowDownward'; +import { Box, Avatar } from '@mui/material'; +import gsap from 'gsap'; +import { useGSAP } from '@gsap/react'; +import LogoutIcon from '@mui/icons-material/Logout'; +import LoginIcon from '@mui/icons-material/Login'; +import { Link, useNavigate } from 'react-router-dom'; + +const BouncingArrow = () => { + return ( + + + + ); +}; + +interface NavbarProps { + user: any; + setUser: (user: any) => void; +} + +const Navbar: React.FC = ({ user, setUser }) => { + const navigate = useNavigate(); + + const handleSignOut = () => { + setUser(null); + localStorage.removeItem('user'); + }; + + return ( + + ); +}; + +const Home: React.FC = () => { + const navigate = useNavigate(); + const [user, setUser] = useState(null); + + useEffect(() => { + const storedUser = localStorage.getItem('user'); + if (storedUser) { + setUser(JSON.parse(storedUser)); + } + }, []); + + useGSAP(() => { + gsap.from('#mainheading', { opacity: 0.3, ease: 'power2.inOut', duration: 0.5 }); + gsap.from('#secondheading', { opacity: 0, y: 5, ease: 'power2.inOut', delay: 0.3, duration: 0.5 }); + gsap.from('#button', { opacity: 0, y: 5, ease: 'power2.inOut', delay: 0.5, duration: 0.5 }); + gsap.from('#features', { opacity: 0, y: 5, ease: 'power2.inOut', delay: 0.7, duration: 0.5 }); + gsap.from('#arrow', { opacity: 0, ease: 'power2.inOut', delay: 2, duration: 0.2 }); + gsap.to('#button', { opacity: 1, ease: 'power2.inOut', delay: 2.5, duration: 0.5 }); + }, []); + + const handleGetStartedClick = () => { + if (!user) { + navigate('/register'); + } else { + navigate('/dashboard'); + } + }; + + return ( +
+
+ + +
+

+ AI Assistant for Your Health +

+ +

+ A solution for personalized health support, including tailored medication guidance and wellness + insights. Take care of yourself with the power of modern technology. +

+ +
+
+

Personalized Prescription

+

+ Receive tailored medication recommendations specifically designed for your needs. +

+
+
+

Health Monitoring

+

+ Stay informed about your health with real-time monitoring and AI-driven insights. +

+
+
+

Advanced AI Support

+

+ Utilize AI support to ensure you're following the best routines for a healthier lifestyle. +

+
+
+ +
+

Try it out

+ +
+ +
+ +
+
+
+ +
+

© {new Date().getFullYear()} Health AI. All rights reserved.

+
+
+ ); +}; + +export { Home, Navbar }; diff --git a/frontend/src/store/api/chatApi.ts b/frontend/src/store/api/chatApi.ts index d214b85..e8f8d29 100644 --- a/frontend/src/store/api/chatApi.ts +++ b/frontend/src/store/api/chatApi.ts @@ -1,33 +1,33 @@ -import { createApi, fetchBaseQuery } from "@reduxjs/toolkit/query/react" - - -// type chatQuestion = { - -// } - -const chatApi = createApi({ - reducerPath: 'chat', - baseQuery: fetchBaseQuery({ baseUrl: 'http://localhost:5000' }), - endpoints: (builder) => ({ - sendTestVersion: builder.query({ - query: (body) => ({ - url: '/create-answer', - method: 'POST', - body: body - }), - transformResponse: ({ response }) => response - }), - sendChatQuestion: builder.query({ - query: (body) => ({ - url: '/api/chat', - method: 'POST', - body: body - }) - }) - }) -}) - - - -export default chatApi -export const { useLazySendTestVersionQuery,useLazySendChatQuestionQuery } = chatApi +import { createApi, fetchBaseQuery } from "@reduxjs/toolkit/query/react" + + +// type chatQuestion = { + +// } + +const chatApi = createApi({ + reducerPath: 'chat', + baseQuery: fetchBaseQuery({ baseUrl: 'http://localhost:5000' }), + endpoints: (builder) => ({ + sendTestVersion: builder.query({ + query: (body) => ({ + url: '/create-answer', + method: 'POST', + body: body + }), + transformResponse: ({ response }) => response + }), + sendChatQuestion: builder.query({ + query: (body) => ({ + url: '/api/chat', + method: 'POST', + body: body + }) + }) + }) +}) + + + +export default chatApi +export const { useLazySendTestVersionQuery,useLazySendChatQuestionQuery } = chatApi diff --git a/frontend/src/store/index.ts b/frontend/src/store/index.ts index 89dd8b4..78d9e6b 100644 --- a/frontend/src/store/index.ts +++ b/frontend/src/store/index.ts @@ -1,19 +1,19 @@ -import { configureStore } from "@reduxjs/toolkit"; -import chatApi from "./api/chatApi"; -import { setupListeners } from '@reduxjs/toolkit/query'; - - -const store = configureStore({ - reducer: { - [chatApi.reducerPath]: chatApi.reducer - }, - middleware: (getDefaultMiddleware) => - getDefaultMiddleware().concat(chatApi.middleware), -}) - -setupListeners(store.dispatch); - -export type RootState = ReturnType; -export type AppDispatch = typeof store.dispatch; - +import { configureStore } from "@reduxjs/toolkit"; +import chatApi from "./api/chatApi"; +import { setupListeners } from '@reduxjs/toolkit/query'; + + +const store = configureStore({ + reducer: { + [chatApi.reducerPath]: chatApi.reducer + }, + middleware: (getDefaultMiddleware) => + getDefaultMiddleware().concat(chatApi.middleware), +}) + +setupListeners(store.dispatch); + +export type RootState = ReturnType; +export type AppDispatch = typeof store.dispatch; + export default store; \ No newline at end of file diff --git a/frontend/src/vite-env.d.ts b/frontend/src/vite-env.d.ts index 11f02fe..7d0ff9e 100644 --- a/frontend/src/vite-env.d.ts +++ b/frontend/src/vite-env.d.ts @@ -1 +1 @@ -/// +/// diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js index 6c01292..47ec16a 100644 --- a/frontend/tailwind.config.js +++ b/frontend/tailwind.config.js @@ -1,22 +1,22 @@ -/** @type {import('tailwindcss').Config} */ -export default { - content: [ - "./index.html", - "./src/**/*.{js,ts,jsx,tsx}", - ], - darkMode: "class", - theme: { - extend: { - colors: { - 'light-blue': '#dbeafe', - 'soft-blue': '#bfdbfe', - 'light-cyan': '#e0f7fa', - 'dark-blue': '#1e3a8a', - 'bright-blue': '#2563eb', - 'deep-blue': '#1d4ed8', - }, - }, - }, - plugins: [], -} - +/** @type {import('tailwindcss').Config} */ +export default { + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + darkMode: "class", + theme: { + extend: { + colors: { + 'light-blue': '#dbeafe', + 'soft-blue': '#bfdbfe', + 'light-cyan': '#e0f7fa', + 'dark-blue': '#1e3a8a', + 'bright-blue': '#2563eb', + 'deep-blue': '#1d4ed8', + }, + }, + }, + plugins: [], +} + diff --git a/frontend/tsconfig.app.json b/frontend/tsconfig.app.json index 5a2def4..a622657 100644 --- a/frontend/tsconfig.app.json +++ b/frontend/tsconfig.app.json @@ -1,25 +1,25 @@ -{ - "compilerOptions": { - "target": "ES2020", - "useDefineForClassFields": true, - "lib": ["ES2020", "DOM", "DOM.Iterable"], - "module": "ESNext", - "skipLibCheck": true, - - /* Bundler mode */ - "moduleResolution": "Bundler", - "allowImportingTsExtensions": true, - "isolatedModules": true, - "moduleDetection": "force", - "noEmit": true, - "jsx": "react-jsx", - - /* Linting */ - "strict": true, - "noUnusedLocals": true, - "noUnusedParameters": true, - "noFallthroughCasesInSwitch": true, - "noUncheckedSideEffectImports": true - }, - "include": ["src"] -} +{ + "compilerOptions": { + "target": "ES2020", + "useDefineForClassFields": true, + "lib": ["ES2020", "DOM", "DOM.Iterable"], + "module": "ESNext", + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "Bundler", + "allowImportingTsExtensions": true, + "isolatedModules": true, + "moduleDetection": "force", + "noEmit": true, + "jsx": "react-jsx", + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["src"] +} diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json index 1ffef60..38e8e3b 100644 --- a/frontend/tsconfig.json +++ b/frontend/tsconfig.json @@ -1,7 +1,7 @@ -{ - "files": [], - "references": [ - { "path": "./tsconfig.app.json" }, - { "path": "./tsconfig.node.json" } - ] -} +{ + "files": [], + "references": [ + { "path": "./tsconfig.app.json" }, + { "path": "./tsconfig.node.json" } + ] +} diff --git a/frontend/tsconfig.node.json b/frontend/tsconfig.node.json index 9dad701..b7f7079 100644 --- a/frontend/tsconfig.node.json +++ b/frontend/tsconfig.node.json @@ -1,23 +1,23 @@ -{ - "compilerOptions": { - "target": "ES2022", - "lib": ["ES2023"], - "module": "ESNext", - "skipLibCheck": true, - - /* Bundler mode */ - "moduleResolution": "Bundler", - "allowImportingTsExtensions": true, - "isolatedModules": true, - "moduleDetection": "force", - "noEmit": true, - - /* Linting */ - "strict": true, - "noUnusedLocals": true, - "noUnusedParameters": true, - "noFallthroughCasesInSwitch": true, - "noUncheckedSideEffectImports": true - }, - "include": ["vite.config.ts"] -} +{ + "compilerOptions": { + "target": "ES2022", + "lib": ["ES2023"], + "module": "ESNext", + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "Bundler", + "allowImportingTsExtensions": true, + "isolatedModules": true, + "moduleDetection": "force", + "noEmit": true, + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["vite.config.ts"] +} diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts index 8b0f57b..ea73791 100644 --- a/frontend/vite.config.ts +++ b/frontend/vite.config.ts @@ -1,7 +1,7 @@ -import { defineConfig } from 'vite' -import react from '@vitejs/plugin-react' - -// https://vite.dev/config/ -export default defineConfig({ - plugins: [react()], -}) +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +// https://vite.dev/config/ +export default defineConfig({ + plugins: [react()], +})