file modified

app.py

@@ -296,80 +296,82 @@ def chatbot_ui():
     with gr.Blocks() as demo:
         gr.Markdown("## Dr. SkinCare - Virtual Dermatologist")
 
-    with gr.Tabs():
+        with gr.Tabs():
         # Chatbot Tab
-        with gr.Tab("Chatbot"):
-            chat_history = gr.State([])
-
-            with gr.Row():
-                with gr.Column(scale=3):
-                    chatbot = gr.Chatbot(label="Responses", elem_id="chatbot")
-                    user_input = gr.Textbox(
-                        label="Ask a health-related question",
-                        placeholder="Describe your symptoms...",
-                        elem_id="user-input",
-                        lines=1,
-                    )
-                with gr.Column(scale=1):
-                    uploaded_image = gr.Image(label="Upload an Image", type="pil")
-                    submit_btn = gr.Button("Submit")
-                    clear_btn = gr.Button("Clear")
-                    audio_output = gr.Audio(label="Audio Response")
-
-            def handle_submit(user_query, image, history):
-                logger.info("User submitted a query.")
-                response, audio = customLLMBot(user_query, image, history)
-                return response, audio, None, "", history
-
-            user_input.submit(
-                handle_submit,
-                inputs=[user_input, uploaded_image, chat_history],
-                outputs=[chatbot, audio_output, uploaded_image, user_input, chat_history],
-            )
-
-            submit_btn.click(
-                handle_submit,
-                inputs=[user_input, uploaded_image, chat_history],
-                outputs=[chatbot, audio_output, uploaded_image, user_input, chat_history],
-            )
-
-            clear_btn.click(
-                lambda: ([], "", None, []),
-                inputs=[],
-                outputs=[chatbot, user_input, uploaded_image, chat_history],
-            )
+            with gr.Tab("Chatbot"):
+                chat_history = gr.State([])
+
+                with gr.Row():
+                    with gr.Column(scale=3):
+                        chatbot = gr.Chatbot(label="Responses", elem_id="chatbot")
+                        user_input = gr.Textbox(
+                            label="Ask a health-related question",
+                            placeholder="Describe your symptoms...",
+                            elem_id="user-input",
+                            lines=1,
+                        )
+                    with gr.Column(scale=1):
+                        uploaded_image = gr.Image(label="Upload an Image", type="pil")
+                        submit_btn = gr.Button("Submit")
+                        clear_btn = gr.Button("Clear")
+                        audio_output = gr.Audio(label="Audio Response")
+
+                def handle_submit(user_query, image, history):
+                    logger.info("User submitted a query.")
+                    response, audio = customLLMBot(user_query, image, history)
+                    return response, audio, None, "", history
+
+                user_input.submit(
+                    handle_submit,
+                    inputs=[user_input, uploaded_image, chat_history],
+                    outputs=[chatbot, audio_output, uploaded_image, user_input, chat_history],
+                )
+
+                submit_btn.click(
+                    handle_submit,
+                    inputs=[user_input, uploaded_image, chat_history],
+                    outputs=[chatbot, audio_output, uploaded_image, user_input, chat_history],
+                )
+
+                clear_btn.click(
+                    lambda: ([], "", None, []),
+                    inputs=[],
+                    outputs=[chatbot, user_input, uploaded_image, chat_history],
+                )
 
         # Predictive Modeling Tab
-        with gr.Tab("Predictive Modeling"):
-            gr.Markdown("### Upload Image for Prediction")
-
-            with gr.Row():
-                with gr.Column():
-                    prediction_image = gr.Image(label="Upload Image", type="pil")
-                    predict_btn = gr.Button("Predict")
-
-                with gr.Column():
-                    gr.Markdown("### Prediction Result")
-                    prediction_output = gr.Textbox(label="Result", interactive=False)
-                    gr.Markdown("### Description")
-                    description_output = gr.Textbox(label="Description", interactive=False)
-                    clear_prediction_btn = gr.Button("Clear Prediction")
-
-            def clear_prediction(prediction_image, prediction_output, description_output):
-                logger.info("Clearing prediction results.")
-                return None, "", ""
-
-            predict_btn.click(
-                predict_image,
-                inputs=[prediction_image],
-                outputs=[prediction_output, description_output],
-            )
-
-            clear_prediction_btn.click(
-                clear_prediction,
-                inputs=[prediction_image, prediction_output, description_output],
-                outputs=[prediction_image, prediction_output, description_output],
-            )
+            with gr.Tab("Predictive Modeling"):
+                gr.Markdown("### Upload Image for Prediction")
+
+                with gr.Row():
+                    with gr.Column():
+                        prediction_image = gr.Image(label="Upload Image", type="pil")
+                        predict_btn = gr.Button("Predict")
+
+                    with gr.Column():
+                        gr.Markdown("### Prediction Result")
+                        prediction_output = gr.Textbox(label="Result", interactive=False)
+                        gr.Markdown("### Description")
+                        description_output = gr.Textbox(label="Description", interactive=False)
+                        clear_prediction_btn = gr.Button("Clear Prediction")
+
+                def clear_prediction(prediction_image, prediction_output, description_output):
+                    logger.info("Clearing prediction results.")
+                    return None, "", ""
+
+                predict_btn.click(
+                    predict_image,
+                    inputs=[prediction_image],
+                    outputs=[prediction_output, description_output],
+                )
+
+                clear_prediction_btn.click(
+                    clear_prediction,
+                    inputs=[prediction_image, prediction_output, description_output],
+                    outputs=[prediction_image, prediction_output, description_output],
+                )
+
+    
 
 
     logger.info("Gradio interface setup complete.")