Odstranit hatespeechapp/app.py

hatespeechapp/app.py

@@ -1,84 +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 time
-
-
-
-app = Flask(__name__)
-CORS(app)
-
-app.config['CACHE_TYPE'] = 'SimpleCache' 
-cache = Cache(app)
-
-
-
-
-
-
-
-
-
-
-model_path = "./hate_speech_model/final_model"
-
-
-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()
-
-@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:
-            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)
-        
-        
-
-
-        response = app.response_class(
-            response=json.dumps({"prediction": prediction_label}, ensure_ascii=False),
-            status=200,
-            mimetype="application/json"
-        )
-        
-        
-
-
-        return response
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-def run_flask():
-    app.run(host="127.0.0.1", port=5000, threaded=True)