Nahrát soubory do „sk1/backend“

sk1/backend/Dockerfile

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+FROM python:3.9
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["python", "app.py"]

sk1/backend/app.py

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+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
+
+from datetime import datetime
+import os
+from flask import Response
+
+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 save_to_history(text, prediction_label):
+    entry = {
+        "text": text,
+        "prediction": prediction_label,
+        "timestamp": datetime.now().strftime("%d.%m.%Y %H:%M:%S")
+    }
+
+    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", "")
+
+        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
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 5000))
+    app.run(host="0.0.0.0", port=port)

sk1/backend/requirements.txt

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