Nahrát soubory do „website/backend“

website/backend/app.py

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+
+from flask import Flask, request, jsonify, Response, send_from_directory
+from flask_cors import CORS
+from flask_caching import Cache
+import json
+import os
+import hashlib
+import re
+import psycopg2
+from datetime import datetime
+import pytz
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+app = Flask(__name__)
+CORS(app)
+app.config['CACHE_TYPE'] = 'SimpleCache'
+cache = Cache(app)
+
+# Database connection
+DATABASE_URL = os.environ.get('DATABASE_URL')
+conn = psycopg2.connect(DATABASE_URL)
+cursor = conn.cursor()
+
+# Initialize DB
+cursor.execute('''
+    CREATE TABLE IF NOT EXISTS history (
+        id SERIAL PRIMARY KEY,
+        text TEXT NOT NULL,
+        prediction TEXT NOT NULL,
+        timestamp TIMESTAMP NOT NULL
+    )
+''')
+conn.commit()
+
+model_path = "tetianamohorian/hate_speech_model"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+model.eval()
+
+HISTORY_FILE = "history.json"
+
+def generate_text_hash(text):
+    return hashlib.md5(text.encode('utf-8')).hexdigest()
+
+def get_current_time():
+    tz = pytz.timezone('Europe/Bratislava')
+    return datetime.now(tz)
+
+def sync_history_file():
+    # Synchronizácia history.json so všetkými záznamami z databázy
+    cursor.execute("SELECT text, prediction, timestamp FROM history ORDER BY timestamp DESC")
+    rows = cursor.fetchall()
+    history = [
+        {
+            "text": r[0],
+            "prediction": r[1],
+            "timestamp": r[2].strftime("%d.%m.%Y %H:%M:%S")
+        } for r in rows
+    ]
+    with open(HISTORY_FILE, "w", encoding="utf-8") as f:
+        json.dump(history, f, ensure_ascii=False, indent=2)
+
+def save_to_history(text, prediction_label):
+    timestamp = get_current_time()
+    try:
+        # Проверка на дубликат
+        cursor.execute(
+            "SELECT 1 FROM history WHERE text = %s AND prediction = %s",
+            (text, prediction_label)
+        )
+        if cursor.fetchone() is None:
+            cursor.execute(
+                "INSERT INTO history (text, prediction, timestamp) VALUES (%s, %s, %s)",
+                (text, prediction_label, timestamp)
+            )
+            conn.commit()
+            sync_history_file()
+    except Exception as e:
+        print("Nepodarilo sa uložiť do databázy alebo prepísať history.json:", e)
+
+@app.route("/")
+def serve_frontend():
+    return send_from_directory("static", "index.html")
+
+@app.route("/<path:path>")
+def serve_static_files(path):
+    return send_from_directory("static", path)
+
+@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:
+        with open(HISTORY_FILE, "r", encoding="utf-8") as f:
+            content = json.load(f)
+        return Response(
+            json.dumps(content, ensure_ascii=False, indent=2),
+            mimetype="application/json"
+        )
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+@app.route("/api/history/reset", methods=["POST"])
+def reset_history():
+    try:
+        cursor.execute("DELETE FROM history")
+        conn.commit()
+        with open(HISTORY_FILE, "w", encoding="utf-8") as f:
+            json.dump([], f, ensure_ascii=False, indent=2)
+        return jsonify({"message": "História bola úspešne vymazaná."}), 200
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+@app.route("/api/history/db", methods=["GET"])
+def get_history_from_db():
+    try:
+        cursor.execute("SELECT text, prediction, timestamp FROM history ORDER BY timestamp DESC")
+        rows = cursor.fetchall()
+        history = [
+            {
+                "text": r[0],
+                "prediction": r[1],
+                "timestamp": r[2].strftime("%d.%m.%Y %H:%M:%S")
+            } for r in rows
+        ]
+        return Response(
+            json.dumps(history, ensure_ascii=False, indent=2),
+            mimetype="application/json"
+        )
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+print("✅ Flask is starting...")
+sync_history_file()
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 8080))
+    app.run(host="0.0.0.0", port=port)

website/backend/requirements.txt

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+torch
+transformers
+datasets
+scikit-learn
+peft
+flask
+flask-cors
+flask_caching
+psycopg2-binary

website/backend/requirements.txt.save

@@ -0,0 +1,9 @@
+torch
+transformers
+datasets
+scikit-learn
+peft
+flask
+flask-cors
+flask_caching
+ requirements.txt