From edec51f193a9b7dd50140d5c68942cc65d22b404 Mon Sep 17 00:00:00 2001
From: Tetiana Mohorian <tetiana.mohorian@student.tuke.sk>
Date: Thu, 20 Mar 2025 00:30:37 +0000
Subject: [PATCH] Aktualizovat z1/backend/app.py

---
 z1/backend/app.py | 83 +++++++++++++++++++++++++++++++----------------
 1 file changed, 55 insertions(+), 28 deletions(-)

diff --git a/z1/backend/app.py b/z1/backend/app.py
index dd7eac5..1053f34 100644
--- a/z1/backend/app.py
+++ b/z1/backend/app.py
@@ -1,28 +1,55 @@
-from flask import Flask, request, jsonify
-from flask_cors import CORS
-import json
-
-app = Flask(__name__)
-CORS(app)  # Разрешаем CORS для фронтенда
-
-@app.route("/api/predict", methods=["POST"])
-def predict():
-    try:
-        data = request.json
-        text = data.get("text", "")
-
-        # Простая логика анализа текста
-        prediction = "Neutrálny text" if "dobry" in text else "Pravdepodobne toxický"
-
-        # ✅ Правильный способ вернуть JSON в UTF-8
-        response = app.response_class(
-            response=json.dumps({"prediction": prediction}, ensure_ascii=False),
-            status=200,
-            mimetype="application/json"
-        )
-        return response
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=5000)
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import json
+
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+
+app = Flask(__name__)
+CORS(app)
+
+
+
+
+model_path = "./hate_speech_model/final_model"
+
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+model.eval()
+
+@app.route("/api/predict", methods=["POST"])
+def predict():
+    try:
+        data = request.json
+        text = data.get("text", "")
+
+
+        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"
+
+
+
+        
+        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
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000)