Update main.py

main.py

@@ -22,38 +22,40 @@ def home():
     return request.url
 
 
-@app.route("/predict", methods=["POST"])
+# @app.route("/predict", methods=["POST"])
+@app.route("/predict")
 def predict():
-    try:
-        # Debugging: print input code to check if the request is received correctly
-        print("Received code:", request.get_json()["code"])
+    return "predicted brooo"
+    # try:
+    #     # Debugging: print input code to check if the request is received correctly
+    #     print("Received code:", request.get_json()["code"])
 
-        data = request.get_json()
-        if "code" not in data:
-            return jsonify({"error": "Missing 'code' parameter"}), 400
+    #     data = request.get_json()
+    #     if "code" not in data:
+    #         return jsonify({"error": "Missing 'code' parameter"}), 400
 
-        code_input = data["code"]
+    #     code_input = data["code"]
 
-        # Tokenize the input code using the CodeBERT tokenizer
-        inputs = tokenizer(
-            code_input,
-            return_tensors='pt',
-            truncation=True,
-            padding='max_length',
-            max_length=512
-        )
+    #     # Tokenize the input code using the CodeBERT tokenizer
+    #     inputs = tokenizer(
+    #         code_input,
+    #         return_tensors='pt',
+    #         truncation=True,
+    #         padding='max_length',
+    #         max_length=512
+    #     )
 
-        # Make prediction using the model
-        with torch.no_grad():
-            outputs = model(**inputs)
-            prediction = outputs.logits.squeeze().item()  # Extract the predicted score (single float)
+    #     # Make prediction using the model
+    #     with torch.no_grad():
+    #         outputs = model(**inputs)
+    #         prediction = outputs.logits.squeeze().item()  # Extract the predicted score (single float)
 
-        print(f"Predicted score: {prediction}")  # Debugging: Print prediction
+    #     print(f"Predicted score: {prediction}")  # Debugging: Print prediction
 
-        return jsonify({"predicted_score": prediction})
+    #     return jsonify({"predicted_score": prediction})
 
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
+    # except Exception as e:
+    #     return jsonify({"error": str(e)}), 500
 
 
 # Run the Flask app