diff --git a/sk1/backend/app.py b/sk1/backend/app.py deleted file mode 100644 index 7b260c5..0000000 --- a/sk1/backend/app.py +++ /dev/null @@ -1,132 +0,0 @@ -from flask import Flask, request, jsonify -from flask_cors import CORS -import json - -import torch -from transformers import AutoModelForSequenceClassification, AutoTokenizer -from flask_caching import Cache -import hashlib -import re -from datetime import datetime -import os -from flask import Response -import pytz - -app = Flask(__name__) -CORS(app) - -app.config['CACHE_TYPE'] = 'SimpleCache' -cache = Cache(app) - -model_path = "tetianamohorian/hate_speech_model" -HISTORY_FILE = "history.json" - -tokenizer = AutoTokenizer.from_pretrained(model_path) -model = AutoModelForSequenceClassification.from_pretrained(model_path) -model.eval() - - -def generate_text_hash(text): - return hashlib.md5(text.encode('utf-8')).hexdigest() - -def get_current_time(): - tz = pytz.timezone('Europe/Bratislava') - now = datetime.now(tz) - return now.strftime("%d.%m.%Y %H:%M:%S") - -def save_to_history(text, prediction_label): - entry = { - "text": text, - "prediction": prediction_label, - "timestamp": get_current_time() - } - - if os.path.exists(HISTORY_FILE): - with open(HISTORY_FILE, "r", encoding="utf-8") as f: - history = json.load(f) - else: - history = [] - - history.append(entry) - with open(HISTORY_FILE, "w", encoding="utf-8") as f: - json.dump(history, f, ensure_ascii=False, indent=2) - -@app.route("/api/predict", methods=["POST"]) -def predict(): - try: - data = request.json - text = data.get("text", "") - - if not text: - return jsonify({"error": "Text nesmie byť prázdny."}), 400 - if len(text) > 512: - return jsonify({"error": "Text je príliš dlhý. Maximálne 512 znakov."}), 400 - if re.search(r"[а-яА-ЯёЁ]", text): - return jsonify({"error": "Text nesmie obsahovať azbuku (cyriliku)."}), 400 - - - text_hash = generate_text_hash(text) - cached_result = cache.get(text_hash) - if cached_result: - save_to_history(text, cached_result) - return jsonify({"prediction": cached_result}), 200 - - inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) - - with torch.no_grad(): - outputs = model(**inputs) - predictions = torch.argmax(outputs.logits, dim=1).item() - - prediction_label = "Pravdepodobne toxický" if predictions == 1 else "Neutrálny text" - cache.set(text_hash, prediction_label) - - save_to_history(text, prediction_label) - - return jsonify({"prediction": prediction_label}), 200 - - except Exception as e: - return jsonify({"error": str(e)}), 500 - - - -@app.route("/api/history", methods=["GET"]) -def get_history(): - try: - if os.path.exists(HISTORY_FILE): - with open(HISTORY_FILE, "r", encoding="utf-8") as f: - history = json.load(f) - return Response( - json.dumps(history, ensure_ascii=False, indent=2), - mimetype="application/json" - ) - else: - return jsonify([]), 200 - except Exception as e: - return jsonify({"error": str(e)}), 500 - - -@app.route("/api/history/raw", methods=["GET"]) -def get_raw_history(): - try: - if os.path.exists(HISTORY_FILE): - with open(HISTORY_FILE, "r", encoding="utf-8") as f: - content = f.read() - return Response(content, mimetype="application/json") - else: - return jsonify({"error": "history.json not found"}), 404 - except Exception as e: - return jsonify({"error": str(e)}), 500 - - -@app.route("/api/history/reset", methods=["POST"]) -def reset_history(): - try: - with open(HISTORY_FILE, "w", encoding="utf-8") as f: - json.dump([], f, ensure_ascii=False, indent=2) - return jsonify({"message": "History reset successful."}), 200 - except Exception as e: - return jsonify({"error": str(e)}), 500 - -if __name__ == "__main__": - port = int(os.environ.get("PORT", 5000)) - app.run(host="0.0.0.0", port=port) \ No newline at end of file