updated code ✅✅`

mediSync/app.py

@@ -400,7 +400,6 @@ class MediSyncApp:
 
 def create_interface():
     """Create and launch the Gradio interface with all fixes implemented."""
-
     app = MediSyncApp()
 
     # Example medical report for demo
@@ -427,263 +426,152 @@ def create_interface():
     try:
         sample_images_dir = Path(__file__).parent.parent / "data" / "sample"
         os.makedirs(sample_images_dir, exist_ok=True)
-        
-        # Check for existing images first
         sample_images = list(sample_images_dir.glob("*.png")) + list(sample_images_dir.glob("*.jpg"))
-        
-        if not sample_images:
-            # Download fallback sample image if none exist
-            fallback_url = "https://raw.githubusercontent.com/ieee8023/covid-chestxray-dataset/master/images/1-s2.0-S0929664620300449-gr2_lrg-a.jpg"
-            sample_path = sample_images_dir / "sample_xray.jpg"
-            
-            try:
-                response = requests.get(fallback_url, timeout=10)
-                if response.status_code == 200:
-                    with open(sample_path, 'wb') as f:
-                        f.write(response.content)
-                    sample_image_path = str(sample_path)
-                    logging.info("Successfully downloaded fallback sample image")
-                else:
-                    logging.warning(f"Failed to download sample image. Status code: {response.status_code}")
-            except Exception as download_error:
-                logging.warning(f"Could not download sample image: {str(download_error)}")
-        else:
+        if sample_images:
             sample_image_path = str(sample_images[0])
     except Exception as e:
         logging.error(f"Error setting up sample images: {str(e)}")
 
-    # Define interface with robust parameter handling
     with gr.Blocks(
         title="MediSync: Multi-Modal Medical Analysis System", 
         theme=gr.themes.Soft()
     ) as interface:
-        # Get appointment ID from URL parameters
-        try:
-            from gradio.context import Context
-            appointment_id_value = Context.request.query_params.get("appointment_id", "") if hasattr(Context, 'request') else ""
-        except Exception as e:
-            logging.warning(f"Could not get URL parameters: {str(e)}")
-            appointment_id_value = ""
-
-        # Appointment ID Textbox (hidden)
-        appointment_id = gr.Textbox(
-            label="Appointment ID",
-            visible=False,
-            value=appointment_id_value if appointment_id_value else ""
-        )
-
-        # Function to load appointment ID from URL parameters
-        def load_appointment_id():
-            try:
-                from gradio.context import Context
-                if hasattr(Context, 'request') and Context.request:
-                    return Context.request.query_params.get("appointment_id", "")
-            except:
-                pass
-            return ""
-
-        # Load appointment ID when interface loads
+        # JavaScript to extract appointment ID from URL
+        js_extract_id = """
+        function() {
+            const params = new URLSearchParams(window.location.search);
+            return params.get('appointment_id') || '';
+        }
+        """
+        
+        # Hidden appointment ID component
+        appointment_id = gr.Textbox(visible=False)
+        
+        # Load appointment ID from URL when interface loads
         interface.load(
-            fn=load_appointment_id,
+            fn=None,
+            _js=js_extract_id,
             outputs=appointment_id
         )
 
         gr.Markdown("""
         # MediSync: Multi-Modal Medical Analysis System
-        
-        This AI-powered healthcare solution combines X-ray image analysis with patient report text processing 
-        to provide comprehensive medical insights.
-        
         ## How to Use
         1. Upload a chest X-ray image
-        2. Enter the corresponding medical report text
-        3. Choose the analysis type: image-only, text-only, or multimodal (combined)
-        4. Click "End Consultation" when finished to complete your appointment
+        2. Enter medical report text
+        3. Choose analysis type
+        4. Click "End Consultation" when finished
         """)
 
         with gr.Tab("Multimodal Analysis"):
             with gr.Row():
                 with gr.Column():
                     multi_img_input = gr.Image(label="Upload X-ray Image", type="pil")
-                    multi_img_enhance = gr.Button("Enhance Image")
-
                     multi_text_input = gr.Textbox(
-                        label="Enter Medical Report Text",
-                        placeholder="Enter the radiologist's report text here...",
-                        lines=10,
-                        value=example_report if not sample_image_path else None,
-                    )
-
-                    multi_analyze_btn = gr.Button(
-                        "Analyze Image & Text", variant="primary"
+                        label="Medical Report Text",
+                        value=example_report,
+                        lines=10
                     )
-
+                    multi_analyze_btn = gr.Button("Analyze", variant="primary")
                 with gr.Column():
-                    multi_results = gr.HTML(label="Analysis Results")
+                    multi_results = gr.HTML(label="Results")
                     multi_plot = gr.HTML(label="Visualization")
 
-            if sample_image_path:
-                gr.Examples(
-                    examples=[[sample_image_path, example_report]],
-                    inputs=[multi_img_input, multi_text_input],
-                    label="Example X-ray and Report",
-                )
-
         with gr.Tab("Image Analysis"):
             with gr.Row():
                 with gr.Column():
                     img_input = gr.Image(label="Upload X-ray Image", type="pil")
-                    img_enhance = gr.Button("Enhance Image")
-                    img_analyze_btn = gr.Button("Analyze Image", variant="primary")
-
+                    img_analyze_btn = gr.Button("Analyze", variant="primary")
                 with gr.Column():
                     img_output = gr.Image(label="Processed Image")
-                    img_results = gr.HTML(label="Analysis Results")
-                    img_plot = gr.HTML(label="Visualization")
-
-            if sample_image_path:
-                gr.Examples(
-                    examples=[[sample_image_path]],
-                    inputs=[img_input],
-                    label="Example X-ray Image",
-                )
+                    img_results = gr.HTML(label="Results")
 
         with gr.Tab("Text Analysis"):
             with gr.Row():
                 with gr.Column():
                     text_input = gr.Textbox(
-                        label="Enter Medical Report Text",
-                        placeholder="Enter the radiologist's report text here...",
-                        lines=10,
+                        label="Medical Report Text",
                         value=example_report,
+                        lines=10
                     )
-                    text_analyze_btn = gr.Button("Analyze Text", variant="primary")
-
+                    text_analyze_btn = gr.Button("Analyze", variant="primary")
                 with gr.Column():
-                    text_output = gr.Textbox(label="Processed Text")
-                    text_results = gr.HTML(label="Analysis Results")
-                    text_plot = gr.HTML(label="Entity Visualization")
-
-            gr.Examples(
-                examples=[[example_report]],
-                inputs=[text_input],
-                label="Example Medical Report",
-            )
-
-        with gr.Tab("About"):
-            gr.Markdown("""
-            ## About MediSync
-            
-            MediSync is an AI-powered healthcare solution that uses multi-modal analysis to provide comprehensive insights from medical images and reports.
-            
-            ### Key Features
-            
-            - **X-ray Image Analysis**: Detects abnormalities in chest X-rays using pre-trained vision models
-            - **Medical Report Processing**: Extracts key information from patient reports using NLP models
-            - **Multi-modal Integration**: Combines insights from both image and text data for more accurate analysis
-            
-            ### Models Used
-            
-            - **X-ray Analysis**: facebook/deit-base-patch16-224-medical-cxr
-            - **Medical Text Analysis**: medicalai/ClinicalBERT
-            
-            ### Important Disclaimer
-            
-            This tool is for educational and research purposes only. It is not intended to provide medical advice or replace professional healthcare. Always consult with qualified healthcare providers for medical decisions.
-            """)
-
-             
+                    text_results = gr.HTML(label="Results")
 
         # Consultation completion section
         with gr.Row():
-            with gr.Column():
-                end_consultation_btn = gr.Button(
-                    "End Consultation", 
-                    variant="stop",
-                    size="lg"
-                )
-                completion_status = gr.HTML()
-
-        # Set up event handlers
-        multi_img_enhance.click(
-            app.enhance_image, inputs=multi_img_input, outputs=multi_img_input
-        )
-        multi_analyze_btn.click(
-            app.analyze_multimodal,
-            inputs=[multi_img_input, multi_text_input],
-            outputs=[multi_results, multi_plot],
-        )
-
-        img_enhance.click(app.enhance_image, inputs=img_input, outputs=img_output)
-        img_analyze_btn.click(
-            app.analyze_image,
-            inputs=img_input,
-            outputs=[img_output, img_results, img_plot],
-        )
-
-        text_analyze_btn.click(
-            app.analyze_text,
-            inputs=text_input,
-            outputs=[text_output, text_results, text_plot],
-        )
+            end_consultation_btn = gr.Button(
+                "End Consultation", 
+                variant="stop",
+                size="lg"
+            )
+            completion_status = gr.HTML()
 
-        def complete_consultation(appointment_id):
-            """Handle consultation completion."""
-            if not appointment_id:
-                return "<div class='alert alert-error'>No appointment ID found. Please contact support.</div>"
+        def complete_consultation(app_id):
+            print(f"Processing completion for appointment: {app_id}")
+            
+            if not app_id:
+                return "<div class='alert alert-error'>No appointment ID found</div>"
             
             try:
-                # Replace with your actual Flask app URL
-                flask_app_url = "http://127.0.0.1:600/complete_consultation"
-                
                 response = requests.post(
-                    flask_app_url,
-                    json={"appointment_id": appointment_id},
+                    "http://127.0.0.1:600/complete_appointment",
+                    json={"appointment_id": app_id},
                     timeout=10
                 )
                 
                 if response.status_code == 200:
                     return """
                     <div class='alert alert-success'>
-                        Consultation completed successfully. Redirecting...
+                        Completed! Redirecting...
                         <script>
-                            setTimeout(function() {
-                                window.location.href = "http://127.0.0.1:600/doctors";
+                            setTimeout(() => {
+                                window.location.href = "http://127.0.0.1:600/doctors#consultations";
                             }, 2000);
                         </script>
                     </div>
                     """
-                else:
-                    return f"""
-                    <div class='alert alert-error'>
-                        Error completing appointment (Status: {response.status_code}). 
-                        Please contact support.
-                    </div>
-                    """
-                    
+                return f"""
+                <div class='alert alert-error'>
+                    Error: {response.json().get('message', 'Unknown error')}
+                </div>
+                """
             except Exception as e:
                 return f"""
                 <div class='alert alert-error'>
-                    Error: {str(e)}
+                    Connection error: {str(e)}
                 </div>
                 """
 
         end_consultation_btn.click(
             fn=complete_consultation,
-            inputs=[appointment_id],
+            inputs=appointment_id,
             outputs=completion_status
         )
 
+        # Analysis button handlers
+        multi_analyze_btn.click(
+            app.analyze_multimodal,
+            inputs=[multi_img_input, multi_text_input],
+            outputs=[multi_results, multi_plot]
+        )
+        img_analyze_btn.click(
+            app.analyze_image,
+            inputs=img_input,
+            outputs=[img_output, img_results]
+        )
+        text_analyze_btn.click(
+            app.analyze_text,
+            inputs=text_input,
+            outputs=text_results
+        )
+
     try:
         interface.launch()
     except Exception as e:
         logging.error(f"Failed to launch interface: {str(e)}")
-        raise RuntimeError("Failed to launch MediSync interface") from e
-
+        raise
 
 if __name__ == "__main__":
-    logging.basicConfig(
-        level=logging.INFO,
-        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-    )
-    create_interface()
+    logging.basicConfig(level=logging.INFO)
+    create_interface()