Aktualizovat sk1/backend/app.py
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from flask import Flask, request, jsonify
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from flask import Flask, request, jsonify
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from flask_cors import CORS
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from flask_cors import CORS
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import json
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import json
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import torch
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import torch
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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from flask_caching import Cache
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from flask_caching import Cache
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import hashlib
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import hashlib
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import re
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from datetime import datetime
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from datetime import datetime
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import os
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import os
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from flask import Response
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from flask import Response
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import pytz
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app = Flask(__name__)
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CORS(app)
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app = Flask(__name__)
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CORS(app)
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app.config['CACHE_TYPE'] = 'SimpleCache'
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cache = Cache(app)
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app.config['CACHE_TYPE'] = 'SimpleCache'
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cache = Cache(app)
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model_path = "tetianamohorian/hate_speech_model"
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HISTORY_FILE = "history.json"
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model_path = "tetianamohorian/hate_speech_model"
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HISTORY_FILE = "history.json"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForSequenceClassification.from_pretrained(model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model.eval()
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model = AutoModelForSequenceClassification.from_pretrained(model_path)
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model.eval()
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def generate_text_hash(text):
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return hashlib.md5(text.encode('utf-8')).hexdigest()
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def generate_text_hash(text):
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return hashlib.md5(text.encode('utf-8')).hexdigest()
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def save_to_history(text, prediction_label):
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entry = {
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def get_current_time():
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"text": text,
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tz = pytz.timezone('Europe/Bratislava')
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"prediction": prediction_label,
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now = datetime.now(tz)
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"timestamp": datetime.now().strftime("%d.%m.%Y %H:%M:%S")
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return now.strftime("%d.%m.%Y %H:%M:%S")
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}
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def save_to_history(text, prediction_label):
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if os.path.exists(HISTORY_FILE):
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entry = {
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with open(HISTORY_FILE, "r", encoding="utf-8") as f:
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"text": text,
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history = json.load(f)
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"prediction": prediction_label,
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else:
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"timestamp": get_current_time()
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history = []
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}
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history.append(entry)
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if os.path.exists(HISTORY_FILE):
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with open(HISTORY_FILE, "w", encoding="utf-8") as f:
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with open(HISTORY_FILE, "r", encoding="utf-8") as f:
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json.dump(history, f, ensure_ascii=False, indent=2)
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history = json.load(f)
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else:
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@app.route("/api/predict", methods=["POST"])
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history = []
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def predict():
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try:
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history.append(entry)
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data = request.json
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with open(HISTORY_FILE, "w", encoding="utf-8") as f:
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text = data.get("text", "")
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json.dump(history, f, ensure_ascii=False, indent=2)
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text_hash = generate_text_hash(text)
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@app.route("/api/predict", methods=["POST"])
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cached_result = cache.get(text_hash)
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def predict():
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if cached_result:
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try:
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save_to_history(text, cached_result)
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data = request.json
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return jsonify({"prediction": cached_result}), 200
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text = data.get("text", "")
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
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if not text:
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return jsonify({"error": "Text nesmie byť prázdny."}), 400
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with torch.no_grad():
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if len(text) > 512:
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outputs = model(**inputs)
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return jsonify({"error": "Text je príliš dlhý. Maximálne 512 znakov."}), 400
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predictions = torch.argmax(outputs.logits, dim=1).item()
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if re.search(r"[а-яА-ЯёЁ]", text):
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return jsonify({"error": "Text nesmie obsahovať azbuku (cyriliku)."}), 400
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prediction_label = "Pravdepodobne toxický" if predictions == 1 else "Neutrálny text"
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cache.set(text_hash, prediction_label)
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text_hash = generate_text_hash(text)
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save_to_history(text, prediction_label)
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cached_result = cache.get(text_hash)
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if cached_result:
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return jsonify({"prediction": prediction_label}), 200
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save_to_history(text, cached_result)
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return jsonify({"prediction": cached_result}), 200
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
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with torch.no_grad():
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outputs = model(**inputs)
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@app.route("/api/history", methods=["GET"])
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predictions = torch.argmax(outputs.logits, dim=1).item()
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def get_history():
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try:
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prediction_label = "Pravdepodobne toxický" if predictions == 1 else "Neutrálny text"
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if os.path.exists(HISTORY_FILE):
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cache.set(text_hash, prediction_label)
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with open(HISTORY_FILE, "r", encoding="utf-8") as f:
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history = json.load(f)
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save_to_history(text, prediction_label)
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return Response(
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json.dumps(history, ensure_ascii=False, indent=2),
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return jsonify({"prediction": prediction_label}), 200
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mimetype="application/json"
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)
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except Exception as e:
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else:
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return jsonify({"error": str(e)}), 500
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return jsonify([]), 200
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@app.route("/api/history", methods=["GET"])
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if __name__ == "__main__":
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def get_history():
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port = int(os.environ.get("PORT", 5000))
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try:
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app.run(host="0.0.0.0", port=port)
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if os.path.exists(HISTORY_FILE):
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with open(HISTORY_FILE, "r", encoding="utf-8") as f:
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history = json.load(f)
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return Response(
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json.dumps(history, ensure_ascii=False, indent=2),
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mimetype="application/json"
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)
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else:
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return jsonify([]), 200
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@app.route("/api/history/raw", methods=["GET"])
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def get_raw_history():
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try:
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if os.path.exists(HISTORY_FILE):
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with open(HISTORY_FILE, "r", encoding="utf-8") as f:
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content = f.read()
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return Response(content, mimetype="application/json")
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else:
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return jsonify({"error": "history.json not found"}), 404
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@app.route("/api/history/reset", methods=["POST"])
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def reset_history():
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try:
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with open(HISTORY_FILE, "w", encoding="utf-8") as f:
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json.dump([], f, ensure_ascii=False, indent=2)
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return jsonify({"message": "History reset successful."}), 200
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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if __name__ == "__main__":
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port = int(os.environ.get("PORT", 5000))
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app.run(host="0.0.0.0", port=port)
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