file modified

app.py

@@ -294,78 +294,83 @@ def customLLMBot(user_input, uploaded_image, chat_history):
 def chatbot_ui():
     logger.info("Setting up Gradio interface...")
     with gr.Blocks() as demo:
-        gr.Markdown("# DermaCare Chatbot Doctor")
-
-        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")
-
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown("### Upload Image for Prediction")
-                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 handle_submit(user_query, image, history):
-            logger.info("User submitted a query.")
-            response, audio = customLLMBot(user_query, image, history)
-            return response, audio, None, "", history
-
-        def clear_prediction(prediction_image, prediction_output, description_output):
-            logger.info("Clearing prediction results.")
-            return None, "", ""
-
-        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],
-        )
-
-        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],
-        )
+        gr.Markdown("## Dr. SkinCare - Virtual Dermatologist")
+
+    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],
+            )
+
+        # 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],
+            )
+
 
     logger.info("Gradio interface setup complete.")
     return demo