Update app.py

app.py

@@ -15,11 +15,11 @@ tokenizer = tokenizer_from_json(tokenizer_data)
 max_sequence_length = 500  
 languages = ["C", "C++", "JAVA", "Python"]
 
-try:
-    with open("feedback.json", "r") as f:
-        feedback_data = json.load(f)
-except FileNotFoundError:
-    feedback_data = []
+# try:
+#     with open("feedback.json", "r") as f:
+#         feedback_data = json.load(f)
+# except FileNotFoundError:
+#     feedback_data = []
 
 def predict_language(code_snippet):
     seq = tokenizer.texts_to_sequences([code_snippet])
@@ -29,48 +29,48 @@ def predict_language(code_snippet):
     predicted_language = languages[np.argmax(predictions)]
     return predicted_language, confidence_scores
 
-def provide_feedback(code_snippet, predicted_language, feedback, correct_language=None):
-    global feedback_data
-
-    feedback_entry = {
-        "code": code_snippet,
-        "predicted_language": predicted_language,
-        "feedback": feedback,
-        "correct_language": correct_language if feedback == "Incorrect" else predicted_language
-    }
-    feedback_data.append(feedback_entry)
-
-    # Save feedback to file
-    with open("feedback.json", "w") as f:
-        json.dump(feedback_data, f, indent=4)
-
-    if feedback == "Incorrect":
-        retrain_model()
-
-    return "Thank you for your feedback!"
-
-def retrain_model():
-    global model
-    # Prepare the feedback data (new training data)
-    if feedback_data.count("Incorrect") < 10: # Minimum 10 incorrect feedbacks required to retrain
-        return
-    feedback_texts = [entry["code"] for entry in feedback_data]
-    feedback_labels = [entry["correct_language"] for entry in feedback_data]
-
-    # Tokenize and pad the new data
-    seq = tokenizer.texts_to_sequences(feedback_texts)
-    padded_seq = pad_sequences(seq, maxlen=max_sequence_length, padding='post', truncating='post')
-
-    # Convert labels to categorical (one-hot encoding)
-    labels = [languages.index(lang) for lang in feedback_labels]
-    labels = to_categorical(labels, num_classes=len(languages))
-
-    # Retrain the model
-    model.fit(padded_seq, labels, epochs=2, batch_size=32, verbose=1)
-    feedback_data = []  # Clear the feedback data after retraining
-    # Save the retrained model
-    # model.save("code_language_cnn_retrained.keras")
-    print("Model retrained")
+# def provide_feedback(code_snippet, predicted_language, feedback, correct_language=None):
+#     global feedback_data
+
+#     feedback_entry = {
+#         "code": code_snippet,
+#         "predicted_language": predicted_language,
+#         "feedback": feedback,
+#         "correct_language": correct_language if feedback == "Incorrect" else predicted_language
+#     }
+#     feedback_data.append(feedback_entry)
+
+#     # Save feedback to file
+#     with open("feedback.json", "w") as f:
+#         json.dump(feedback_data, f, indent=4)
+
+#     if feedback == "Incorrect":
+#         retrain_model()
+
+#     return "Thank you for your feedback!"
+
+# def retrain_model():
+#     global model
+#     # Prepare the feedback data (new training data)
+#     if feedback_data.count("Incorrect") < 10: # Minimum 10 incorrect feedbacks required to retrain
+#         return
+#     feedback_texts = [entry["code"] for entry in feedback_data]
+#     feedback_labels = [entry["correct_language"] for entry in feedback_data]
+
+#     # Tokenize and pad the new data
+#     seq = tokenizer.texts_to_sequences(feedback_texts)
+#     padded_seq = pad_sequences(seq, maxlen=max_sequence_length, padding='post', truncating='post')
+
+#     # Convert labels to categorical (one-hot encoding)
+#     labels = [languages.index(lang) for lang in feedback_labels]
+#     labels = to_categorical(labels, num_classes=len(languages))
+
+#     # Retrain the model
+#     model.fit(padded_seq, labels, epochs=2, batch_size=32, verbose=1)
+#     feedback_data = []  # Clear the feedback data after retraining
+#     # Save the retrained model
+#     # model.save("code_language_cnn_retrained.keras")
+#     print("Model retrained")
 
 # Define Gradio components
 def interface_func(code_snippet):
@@ -85,10 +85,10 @@ with gr.Blocks() as demo:
     predicted_label = gr.Label(label="Predicted Language")
     confidence_output = gr.JSON(label="Confidence Scores")
 
-    feedback_dropdown = gr.Radio(["Correct", "Incorrect"], label="Was the prediction correct?")
-    correct_language_dropdown = gr.Dropdown(languages, label="If incorrect, select the correct language (optional)")
-    feedback_button = gr.Button("Submit Feedback")
-    feedback_message = gr.Label(label="Feedback Status")
+    # feedback_dropdown = gr.Radio(["Correct", "Incorrect"], label="Was the prediction correct?")
+    # correct_language_dropdown = gr.Dropdown(languages, label="If incorrect, select the correct language (optional)")
+    # feedback_button = gr.Button("Submit Feedback")
+    # feedback_message = gr.Label(label="Feedback Status")
 
     # Prediction workflow
     predict_button.click(
@@ -98,11 +98,11 @@ with gr.Blocks() as demo:
     )
 
     # Feedback workflow
-    feedback_button.click(
-        provide_feedback,
-        inputs=[code_input, predicted_label, feedback_dropdown, correct_language_dropdown],
-        outputs=[feedback_message]
-    )
+    # feedback_button.click(
+    #     provide_feedback,
+    #     inputs=[code_input, predicted_label, feedback_dropdown, correct_language_dropdown],
+    #     outputs=[feedback_message]
+    # )
 
 # Launch the interface
-demo.launch(share=True)
+demo.launch()