Update app.py

app.py

@@ -36,9 +36,9 @@ def analyze_sentiment(user_input: str):
         result = sentiment_pipeline(user_input)[0]
         sentiment = result['label']
         confidence = result['score']
-        return sentiment, confidence
+        return f"Sentiment: {sentiment}, Confidence: {confidence:.2f}", sentiment, confidence
     except Exception as e:
-        return f"Error analyzing sentiment: {e}", None
+        return f"Error analyzing sentiment: {e}", None, None
 
 # Function to save data
 def save_feedback(user_input: str, sentiment: str, confidence: float, user_feedback: str) -> str:
@@ -58,35 +58,48 @@ def save_feedback(user_input: str, sentiment: str, confidence: float, user_feedb
                 f.write("\n")
         # Push the changes to Hugging Face Hub
         scheduler.push_to_hub()
-        return "Data saved successfully"
+        return "Feedback saved successfully!"
     except Exception as e:
-        return f"Error saving data: {e}"
+        return f"Error saving feedback: {e}"
 
 # Gradio Interface
-def handle_feedback(user_input):
-    sentiment, confidence = analyze_sentiment(user_input)
-    if confidence is None:  # Check for sentiment analysis errors
-        return sentiment, "", ""
-    result_message = f"Sentiment: {sentiment}, Confidence: {confidence:.2f}"
-    return result_message, sentiment, confidence
+with gr.Blocks() as demo:
+    with gr.Column():
+        gr.Markdown("### Sentiment Analysis with User Feedback")
+        
+        with gr.Row():
+            user_input = gr.Textbox(label="Enter Your Review", placeholder="Type your review here...")
+            analyze_button = gr.Button("Analyze")
+        
+        analysis_output = gr.Textbox(label="Analysis Result", interactive=False)
+        
+        feedback_section = gr.Column(visible=False)  # Initially hidden
+        with feedback_section:
+            feedback_input = gr.Textbox(label="Your Feedback on the Model's Response", placeholder="Type your feedback here...")
+            feedback_button = gr.Button("Submit Feedback")
+        
+        feedback_status = gr.Textbox(label="Feedback Status", interactive=False)
 
-def submit_feedback(user_input, sentiment, confidence, user_feedback):
-    if not user_feedback:
-        return "Please provide feedback before submitting."
-    save_status = save_feedback(user_input, sentiment, confidence, user_feedback)
-    return save_status
+    # Button Logic
+    def analyze_and_show_feedback(user_input):
+        result_message, sentiment, confidence = analyze_sentiment(user_input)
+        return result_message, gr.update(visible=True), user_input, sentiment, confidence
 
-with gr.Blocks() as demo:
-    with gr.Row():
-        user_input = gr.Textbox(label="Enter Your Review", placeholder="Type your review here...")
-        output = gr.Textbox(label="Analysis Result", interactive=False)
-    with gr.Row():
-        user_feedback = gr.Textbox(label="Your Feedback on the Model's Response", placeholder="Type your feedback here...")
-    submit_button = gr.Button("Analyze")
-    feedback_submit_button = gr.Button("Submit Feedback")
+    def submit_feedback(user_input, sentiment, confidence, user_feedback):
+        if not user_feedback:
+            return "Please provide feedback before submitting."
+        return save_feedback(user_input, sentiment, confidence, user_feedback)
 
-    # Connect functions to buttons
-    submit_button.click(fn=handle_feedback, inputs=user_input, outputs=[output, user_feedback, output])
-    feedback_submit_button.click(fn=submit_feedback, inputs=[user_input, user_feedback, output, user_feedback], outputs=output)
+    # Connect button actions
+    analyze_button.click(
+        fn=analyze_and_show_feedback,
+        inputs=[user_input],
+        outputs=[analysis_output, feedback_section, user_input, feedback_section, feedback_section]
+    )
+    feedback_button.click(
+        fn=submit_feedback,
+        inputs=[user_input, analysis_output, feedback_status, user_input],
+        outputs=feedback_status
+    )
 
-demo.launch()
+demo.launch()