added the feedback option for future training data

app.py

@@ -7,9 +7,9 @@ import os
 detector = pipeline("text-classification", model="debojit01/fake-review-detector")
 
 # CSV file setup
-FEEDBACK_FILE = "feedback_data.csv"
+FEEDBACK_FILE = "training_data.csv"
 if not os.path.exists(FEEDBACK_FILE):
-    pd.DataFrame(columns=["text", "predicted_label", "user_feedback"]).to_csv(FEEDBACK_FILE, index=False)
+    pd.DataFrame(columns=["text", "label"]).to_csv(FEEDBACK_FILE, index=False)
 
 def predict(text):
     result = detector(text)[0]
@@ -18,42 +18,50 @@ def predict(text):
     else:  # Fake (LABEL_1)
         return {"Real": 1 - result["score"], "Fake": result["score"]}
 
-def save_feedback(text, prediction, feedback):
-    """Save user feedback to CSV"""
-    new_row = {
-        "text": text,
-        "predicted_label": "Real" if prediction["Real"] > 0.5 else "Fake",
-        "user_feedback": feedback
-    }
+def save_feedback(text, prediction, is_correct):
+    """Save feedback only when user submits"""
+    if is_correct is None:  # No feedback provided
+        return "Prediction shown"
     
-    # Append to CSV
-    df = pd.read_csv(FEEDBACK_FILE)
-    df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
-    df.to_csv(FEEDBACK_FILE, index=False)
-    return "Feedback saved! Thank you!"
+    predicted_label = "Real" if prediction["Real"] > 0.5 else "Fake"
+    true_label = predicted_label if is_correct else ("Fake" if predicted_label == "Real" else "Real")
+    
+    new_data = pd.DataFrame({"text": [text], "label": [true_label]})
+    new_data.to_csv(FEEDBACK_FILE, mode='a', header=not os.path.exists(FEEDBACK_FILE), index=False)
+    return "Thank you for your feedback!"
 
 with gr.Blocks() as app:
-    gr.Markdown("## Fake Review Detector (with Feedback)")
+    gr.Markdown("## Fake Review Detector")
     
     with gr.Row():
-        review = gr.Textbox(label="Enter Review")
-        output = gr.Label(label="Prediction")
+        review_input = gr.Textbox(label="Enter Review")
+        predict_btn = gr.Button("Predict")
     
-    with gr.Row():
-        feedback = gr.Radio(
-            choices=["Correct", "Incorrect"],
-            label="Is this prediction correct?"
-        )
-        submit_btn = gr.Button("Submit Feedback")
+    output_label = gr.Label(label="Prediction")
     
-    # Prediction flow
-    review.change(predict, inputs=review, outputs=output)
+    with gr.Row(visible=False) as feedback_row:
+        feedback_radio = gr.Radio(
+            ["Correct", "Incorrect"],
+            label="Is this prediction accurate?"
+        )
+        feedback_btn = gr.Button("Submit Feedback")
     
-    # Feedback flow
-    submit_btn.click(
+    status_text = gr.Textbox(label="Status", interactive=False)
+
+    def show_prediction(text):
+        prediction = predict(text)
+        return prediction, gr.Row(visible=True), ""
+
+    predict_btn.click(
+        show_prediction,
+        inputs=review_input,
+        outputs=[output_label, feedback_row, status_text]
+    )
+
+    feedback_btn.click(
         save_feedback,
-        inputs=[review, output, feedback],
-        outputs=gr.Textbox(label="Feedback Status")
+        inputs=[review_input, output_label, feedback_radio],
+        outputs=status_text
     )
 
 app.launch()