Update app.py

app.py

@@ -1,18 +1,59 @@
 import gradio as gr
 from transformers import pipeline
+import pandas as pd
+import os
 
+# Initialize detector
 detector = pipeline("text-classification", model="debojit01/fake-review-detector")
 
+# CSV file setup
+FEEDBACK_FILE = "feedback_data.csv"
+if not os.path.exists(FEEDBACK_FILE):
+    pd.DataFrame(columns=["text", "predicted_label", "user_feedback"]).to_csv(FEEDBACK_FILE, index=False)
+
 def predict(text):
     result = detector(text)[0]
-    return {
-        "Fake": result["score"] if result["label"] == "LABEL_0" else 1 - result["score"],
-        "Genuine": 1 - result["score"] if result["label"] == "LABEL_0" else result["score"]
+    if result["label"] == "LABEL_0":  # Real
+        return {"Real": result["score"], "Fake": 1 - result["score"]}
+    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
     }
+    
+    # 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!"
+
+with gr.Blocks() as app:
+    gr.Markdown("## Fake Review Detector (with Feedback)")
+    
+    with gr.Row():
+        review = gr.Textbox(label="Enter Review")
+        output = gr.Label(label="Prediction")
+    
+    with gr.Row():
+        feedback = gr.Radio(
+            choices=["Correct", "Incorrect"],
+            label="Is this prediction correct?"
+        )
+        submit_btn = gr.Button("Submit Feedback")
+    
+    # Prediction flow
+    review.change(predict, inputs=review, outputs=output)
+    
+    # Feedback flow
+    submit_btn.click(
+        save_feedback,
+        inputs=[review, output, feedback],
+        outputs=gr.Textbox(label="Feedback Status")
+    )
 
-gr.Interface(
-    fn=predict,
-    inputs="text",
-    outputs="label",
-    title="Fake Review Detector"
-).launch()
+app.launch()