updated the code UI ✅✅

mediSync/app.py

@@ -400,7 +400,6 @@ import json
 try:
     from .config import get_flask_urls, get_doctors_page_urls, TIMEOUT_SETTINGS
 except ImportError:
-    # Fallback configuration if config file is not available
     def get_flask_urls():
         return [
             "http://127.0.0.1:600/complete_appointment",
@@ -408,20 +407,16 @@ except ImportError:
             "https://your-flask-app-domain.com/complete_appointment",
             "http://your-flask-app-ip:600/complete_appointment"
         ]
-    
     def get_doctors_page_urls():
         return {
             "local": "http://127.0.0.1:600/doctors",
             "production": "https://your-flask-app-domain.com/doctors"
         }
-    
     TIMEOUT_SETTINGS = {"connection_timeout": 5, "request_timeout": 10}
 
-# Add parent directory to path
 parent_dir = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(parent_dir)
 
-# Configure logging
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
@@ -430,25 +425,18 @@ logging.basicConfig(
 logger = logging.getLogger(__name__)
 
 class MediSyncApp:
-    """
-    Main application class for the MediSync multi-modal medical analysis system.
-    """
-
     def __init__(self):
-        """Initialize the application and load models."""
         self.logger = logging.getLogger(__name__)
         self.logger.info("Initializing MediSync application")
-        self._temp_files = []  # Track temporary files for cleanup
+        self._temp_files = []
         self.fusion_model = None
         self.image_model = None
         self.text_model = None
 
     def __del__(self):
-        """Cleanup temporary files on object destruction."""
         self.cleanup_temp_files()
 
     def cleanup_temp_files(self):
-        """Clean up temporary files."""
         for temp_file in self._temp_files:
             try:
                 if os.path.exists(temp_file):
@@ -459,19 +447,10 @@ class MediSyncApp:
         self._temp_files = []
 
     def load_models(self):
-        """
-        Load models if not already loaded.
-
-        Returns:
-            bool: True if models loaded successfully, False otherwise
-        """
         if self.fusion_model is not None:
             return True
-
         try:
             self.logger.info("Loading models...")
-            # For now, we'll create a simple mock implementation
-            # You can replace this with your actual model loading code
             self.logger.info("Models loaded successfully (mock implementation)")
             return True
         except Exception as e:
@@ -479,12 +458,9 @@ class MediSyncApp:
             return False
 
     def enhance_image(self, image):
-        """Enhance the uploaded image."""
         if image is None:
             return None
-        
         try:
-            # Simple image enhancement (you can replace with actual enhancement logic)
             enhanced_image = image
             self.logger.info("Image enhanced successfully")
             return enhanced_image
@@ -493,25 +469,12 @@ class MediSyncApp:
             return image
 
     def analyze_image(self, image):
-        """
-        Analyze a medical image.
-
-        Args:
-            image: Image file uploaded through Gradio
-
-        Returns:
-            tuple: (image, image_results_html, plot_as_html)
-        """
         if image is None:
-            return None, "Please upload an image first.", None
-
+            return None, "<div class='ms-error'>Please upload an image first.</div>", None
         if not self.load_models():
-            return image, "Error: Models not loaded properly.", None
-
+            return image, "<div class='ms-error'>Error: Models not loaded properly.</div>", None
         try:
             self.logger.info("Analyzing image")
-            
-            # Mock analysis results (replace with actual model inference)
             results = {
                 "primary_finding": "Normal chest X-ray",
                 "confidence": 0.85,
@@ -522,47 +485,26 @@ class MediSyncApp:
                     ("Cardiomegaly", 0.05)
                 ]
             }
-
-            # Create visualization
             fig = self.plot_image_prediction(
                 image,
                 results.get("predictions", []),
                 f"Primary Finding: {results.get('primary_finding', 'Unknown')}"
             )
-
-            # Convert to HTML for display
             plot_html = self.fig_to_html(fig)
-            plt.close(fig)  # Clean up matplotlib figure
-
-            # Format results as HTML
+            plt.close(fig)
             html_result = self.format_image_results(results)
-            
             return image, html_result, plot_html
-
         except Exception as e:
             self.logger.error(f"Error in image analysis: {e}")
-            return image, f"Error analyzing image: {str(e)}", None
+            return image, f"<div class='ms-error'>Error analyzing image: {str(e)}</div>", None
 
     def analyze_text(self, text):
-        """
-        Analyze medical report text.
-
-        Args:
-            text: Medical report text
-
-        Returns:
-            tuple: (processed_text, text_results_html, plot_as_html)
-        """
         if not text or text.strip() == "":
-            return "", "Please enter medical report text.", None
-
+            return "", "<div class='ms-error'>Please enter medical report text.</div>", None
         if not self.load_models():
-            return text, "Error: Models not loaded properly.", None
-
+            return text, "<div class='ms-error'>Error: Models not loaded properly.</div>", None
         try:
             self.logger.info("Analyzing text")
-            
-            # Mock text analysis results (replace with actual model inference)
             results = {
                 "entities": [
                     {"text": "chest X-ray", "type": "PROCEDURE", "confidence": 0.95},
@@ -572,40 +514,20 @@ class MediSyncApp:
                 "sentiment": "neutral",
                 "key_findings": ["Normal heart size", "Clear lungs", "8mm nodular opacity"]
             }
-
-            # Format results as HTML
             html_result = self.format_text_results(results)
-            
-            # Create entity visualization
             plot_html = self.create_entity_visualization(results["entities"])
-            
             return text, html_result, plot_html
-
         except Exception as e:
             self.logger.error(f"Error in text analysis: {e}")
-            return text, f"Error analyzing text: {str(e)}", None
+            return text, f"<div class='ms-error'>Error analyzing text: {str(e)}</div>", None
 
     def analyze_multimodal(self, image, text):
-        """
-        Analyze both image and text together.
-
-        Args:
-            image: Medical image
-            text: Medical report text
-
-        Returns:
-            tuple: (results_html, plot_as_html)
-        """
         if image is None and (not text or text.strip() == ""):
-            return "Please provide either an image or text for analysis.", None
-
+            return "<div class='ms-error'>Please provide either an image or text for analysis.</div>", None
         if not self.load_models():
-            return "Error: Models not loaded properly.", None
-
+            return "<div class='ms-error'>Error: Models not loaded properly.</div>", None
         try:
             self.logger.info("Performing multimodal analysis")
-            
-            # Mock multimodal analysis results (replace with actual model inference)
             results = {
                 "combined_finding": "Normal chest X-ray with minor findings",
                 "confidence": 0.92,
@@ -616,87 +538,64 @@ class MediSyncApp:
                     "Monitor for any changes in symptoms"
                 ]
             }
-
-            # Format results as HTML
             html_result = self.format_multimodal_results(results)
-            
-            # Create combined visualization
             plot_html = self.create_multimodal_visualization(results)
-            
             return html_result, plot_html
-
         except Exception as e:
             self.logger.error(f"Error in multimodal analysis: {e}")
-            return f"Error in multimodal analysis: {str(e)}", None
+            return f"<div class='ms-error'>Error in multimodal analysis: {str(e)}</div>", None
 
     def format_image_results(self, results):
-        """Format image analysis results as HTML."""
         html_result = f"""
-        <div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin: 10px 0;">
-            <h2 style="color: #007bff;">X-ray Analysis Results</h2>
-            <p><strong>Primary Finding:</strong> {results.get("primary_finding", "Unknown")}</p>
-            <p><strong>Confidence:</strong> {results.get("confidence", 0):.1%}</p>
-            <p><strong>Abnormality Detected:</strong> {"Yes" if results.get("has_abnormality", False) else "No"}</p>
-            
-            <h3>Top Predictions:</h3>
+        <div class="ms-card ms-image-card">
+            <h2 class="ms-title ms-image-title">X-ray Analysis Results</h2>
+            <p><span class="ms-label">Primary Finding:</span> <span class="ms-value">{results.get("primary_finding", "Unknown")}</span></p>
+            <p><span class="ms-label">Confidence:</span> <span class="ms-value">{results.get("confidence", 0):.1%}</span></p>
+            <p><span class="ms-label">Abnormality Detected:</span> <span class="ms-value">{"Yes" if results.get("has_abnormality", False) else "No"}</span></p>
+            <h3 class="ms-subtitle">Top Predictions:</h3>
             <ul>
         """
-
         for label, prob in results.get("predictions", [])[:5]:
             html_result += f"<li>{label}: {prob:.1%}</li>"
-
         html_result += "</ul></div>"
         return html_result
 
     def format_text_results(self, results):
-        """Format text analysis results as HTML."""
         html_result = f"""
-        <div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin: 10px 0;">
-            <h2 style="color: #28a745;">Text Analysis Results</h2>
-            <p><strong>Sentiment:</strong> {results.get("sentiment", "Unknown").title()}</p>
-            
-            <h3>Key Findings:</h3>
+        <div class="ms-card ms-text-card">
+            <h2 class="ms-title ms-text-title">Text Analysis Results</h2>
+            <p><span class="ms-label">Sentiment:</span> <span class="ms-value">{results.get("sentiment", "Unknown").title()}</span></p>
+            <h3 class="ms-subtitle">Key Findings:</h3>
             <ul>
         """
-        
         for finding in results.get("key_findings", []):
             html_result += f"<li>{finding}</li>"
-        
         html_result += "</ul>"
-        
-        html_result += "<h3>Extracted Entities:</h3><ul>"
+        html_result += "<h3 class='ms-subtitle'>Extracted Entities:</h3><ul>"
         for entity in results.get("entities", [])[:5]:
             html_result += f"<li><strong>{entity['text']}</strong> ({entity['type']}) - {entity['confidence']:.1%}</li>"
-        
         html_result += "</ul></div>"
         return html_result
 
     def format_multimodal_results(self, results):
-        """Format multimodal analysis results as HTML."""
         html_result = f"""
-        <div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin: 10px 0;">
-            <h2 style="color: #6f42c1;">Multimodal Analysis Results</h2>
-            <p><strong>Combined Finding:</strong> {results.get("combined_finding", "Unknown")}</p>
-            <p><strong>Overall Confidence:</strong> {results.get("confidence", 0):.1%}</p>
-            
-            <h3>Image Contribution:</h3>
+        <div class="ms-card ms-multimodal-card">
+            <h2 class="ms-title ms-multimodal-title">Multimodal Analysis Results</h2>
+            <p><span class="ms-label">Combined Finding:</span> <span class="ms-value">{results.get("combined_finding", "Unknown")}</span></p>
+            <p><span class="ms-label">Overall Confidence:</span> <span class="ms-value">{results.get("confidence", 0):.1%}</span></p>
+            <h3 class="ms-subtitle">Image Contribution:</h3>
             <p>{results.get("image_contribution", "No image analysis available")}</p>
-            
-            <h3>Text Contribution:</h3>
+            <h3 class="ms-subtitle">Text Contribution:</h3>
             <p>{results.get("text_contribution", "No text analysis available")}</p>
-            
-            <h3>Recommendations:</h3>
+            <h3 class="ms-subtitle">Recommendations:</h3>
             <ul>
         """
-        
         for rec in results.get("recommendations", []):
             html_result += f"<li>{rec}</li>"
-        
         html_result += "</ul></div>"
         return html_result
 
     def plot_image_prediction(self, image, predictions, title):
-        """Create visualization for image predictions."""
         fig, ax = plt.subplots(figsize=(10, 6))
         ax.imshow(image)
         ax.set_title(title, fontsize=14, fontweight='bold')
@@ -704,80 +603,53 @@ class MediSyncApp:
         return fig
 
     def create_entity_visualization(self, entities):
-        """Create visualization for text entities."""
         if not entities:
             return "<p>No entities found in text.</p>"
-        
         fig, ax = plt.subplots(figsize=(10, 6))
-        
         entity_types = {}
         for entity in entities:
             entity_type = entity['type']
             if entity_type not in entity_types:
                 entity_types[entity_type] = 0
             entity_types[entity_type] += 1
-        
         if entity_types:
-            ax.bar(entity_types.keys(), entity_types.values(), color='skyblue')
+            ax.bar(entity_types.keys(), entity_types.values(), color='#4e79a7')
             ax.set_title('Entity Types Found in Text', fontsize=14, fontweight='bold')
             ax.set_ylabel('Count')
             plt.xticks(rotation=45)
-        
         return self.fig_to_html(fig)
 
     def create_multimodal_visualization(self, results):
-        """Create visualization for multimodal results."""
         fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
-        
-        # Confidence visualization
         confidence = results.get("confidence", 0)
-        ax1.pie([confidence, 1-confidence], labels=['Confidence', 'Uncertainty'], 
-                colors=['lightgreen', 'lightcoral'], autopct='%1.1f%%')
+        ax1.pie([confidence, 1-confidence], labels=['Confidence', 'Uncertainty'],
+                colors=['#59c47e', '#e15759'], autopct='%1.1f%%')
         ax1.set_title('Analysis Confidence', fontweight='bold')
-        
-        # Recommendations count
         recommendations = results.get("recommendations", [])
-        ax2.bar(['Recommendations'], [len(recommendations)], color='lightblue')
+        ax2.bar(['Recommendations'], [len(recommendations)], color='#4e79a7')
         ax2.set_title('Number of Recommendations', fontweight='bold')
         ax2.set_ylabel('Count')
-        
         plt.tight_layout()
         return self.fig_to_html(fig)
 
     def fig_to_html(self, fig):
-        """Convert matplotlib figure to HTML."""
         import io
         import base64
-        
         buf = io.BytesIO()
         fig.savefig(buf, format='png', bbox_inches='tight', dpi=100)
         buf.seek(0)
         img_str = base64.b64encode(buf.read()).decode()
         buf.close()
-        
-        return f'<img src="data:image/png;base64,{img_str}" style="max-width: 100%; height: auto;"/>'
+        return f'<img src="data:image/png;base64,{img_str}" style="max-width: 100%; height: auto; border-radius: 8px; box-shadow: 0 2px 8px #0001;"/>'
 
 def complete_appointment(appointment_id):
-    """
-    Complete an appointment by calling the Flask API.
-    
-    Args:
-        appointment_id: The appointment ID to complete
-        
-    Returns:
-        dict: Response from the API
-    """
     try:
-        # Get Flask URLs from configuration
         flask_urls = get_flask_urls()
-        
         payload = {"appointment_id": appointment_id}
-        
         for flask_api_url in flask_urls:
             try:
                 logger.info(f"Trying to connect to: {flask_api_url}")
                 response = requests.post(flask_api_url, json=payload, timeout=TIMEOUT_SETTINGS["connection_timeout"])
-                
                 if response.status_code == 200:
                     return {"status": "success", "message": "Appointment completed successfully"}
                 elif response.status_code == 404:
@@ -785,7 +657,6 @@ def complete_appointment(appointment_id):
                 else:
                     logger.warning(f"Unexpected response from {flask_api_url}: {response.status_code}")
                     continue
-                    
             except requests.exceptions.ConnectionError:
                 logger.warning(f"Connection failed to {flask_api_url}")
                 continue
@@ -795,198 +666,251 @@ def complete_appointment(appointment_id):
             except Exception as e:
                 logger.warning(f"Error with {flask_api_url}: {e}")
                 continue
-        
-        # If all URLs fail, return a helpful error message
         return {
-            "status": "error", 
+            "status": "error",
             "message": "Cannot connect to Flask app. Please ensure the Flask app is running and accessible."
         }
-            
     except Exception as e:
         logger.error(f"Error completing appointment: {e}")
         return {"status": "error", "message": f"Error: {str(e)}"}
 
 def create_interface():
-    """Create and launch the Gradio interface."""
-
     app = MediSyncApp()
-
-    # Example medical report for demo
     example_report = """
     CHEST X-RAY EXAMINATION
-    
+
     CLINICAL HISTORY: 55-year-old male with cough and fever.
-    
+
     FINDINGS: The heart size is at the upper limits of normal. The lungs are clear without focal consolidation, 
     effusion, or pneumothorax. There is mild prominence of the pulmonary vasculature. No pleural effusion is seen. 
     There is a small nodular opacity noted in the right lower lobe measuring approximately 8mm, which is suspicious 
     and warrants further investigation. The mediastinum is unremarkable. The visualized bony structures show no acute abnormalities.
-    
+
     IMPRESSION:
     1. Mild cardiomegaly.
     2. 8mm nodular opacity in the right lower lobe, recommend follow-up CT for further evaluation.
     3. No acute pulmonary parenchymal abnormality.
-    
+
     RECOMMENDATIONS: Follow-up chest CT to further characterize the nodular opacity in the right lower lobe.
     """
-
-    # Get sample image path if available
     sample_images_dir = Path(parent_dir) / "data" / "sample"
-    sample_images = list(sample_images_dir.glob("*.png")) + list(
-        sample_images_dir.glob("*.jpg")
-    )
-
+    sample_images = list(sample_images_dir.glob("*.png")) + list(sample_images_dir.glob("*.jpg"))
     sample_image_path = None
     if sample_images:
         sample_image_path = str(sample_images[0])
 
-    # Define interface
     with gr.Blocks(
-        title="MediSync: Multi-Modal Medical Analysis System", theme=gr.themes.Soft()
+        title="MediSync: Multi-Modal Medical Analysis System",
+        theme=gr.themes.Soft(primary_hue="blue", secondary_hue="green", neutral_hue="slate"),
+        css="""
+        .ms-card {
+            background: #fff !important;
+            color: #222 !important;
+            border-radius: 12px;
+            box-shadow: 0 2px 12px #0002;
+            padding: 24px 24px 16px 24px;
+            margin: 16px 0;
+        }
+        .ms-title {
+            font-size: 1.5em;
+            font-weight: 700;
+            margin-bottom: 0.5em;
+            color: #1a237e !important;
+        }
+        .ms-image-title { color: #007bff !important; }
+        .ms-text-title { color: #28a745 !important; }
+        .ms-multimodal-title { color: #6f42c1 !important; }
+        .ms-label {
+            font-weight: 600;
+            color: #374151 !important;
+        }
+        .ms-value {
+            color: #111 !important;
+        }
+        .ms-subtitle {
+            font-size: 1.1em;
+            font-weight: 600;
+            margin-top: 1em;
+            color: #0d6efd !important;
+        }
+        .ms-error {
+            color: #b91c1c !important;
+            background: #ffe6e6 !important;
+            border-radius: 6px;
+            padding: 10px 16px;
+            font-weight: 600;
+        }
+        .end-consultation-btn {
+            background-color: #dc3545 !important;
+            border-color: #dc3545 !important;
+            color: #fff !important;
+            font-weight: bold !important;
+            border-radius: 8px !important;
+            font-size: 1.1em !important;
+            padding: 12px 32px !important;
+            margin-top: 8px !important;
+        }
+        .end-consultation-btn:hover {
+            background-color: #c82333 !important;
+            border-color: #bd2130 !important;
+        }
+        .gradio-container, .gr-block, .gr-panel, .gr-box {
+            background: #f4f7fa !important;
+        }
+        .gr-input, .gr-textbox, .gr-text-input, .gr-textarea {
+            background: #fff !important;
+            color: #222 !important;
+        }
+        .gr-label, label {
+            color: #1a237e !important;
+            font-weight: 600 !important;
+        }
+        .gr-html, .gr-markdown {
+            color: #222 !important;
+        }
+        .gr-button {
+            font-weight: 600 !important;
+        }
+        """
     ) as interface:
-        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
-        """)
+        with gr.Row():
+            gr.Markdown(
+                """
+                <div style="display: flex; align-items: center; gap: 16px;">
+                    <img src="https://img.icons8.com/fluency/48/000000/medical-doctor.png" style="height:48px;"/>
+                    <span style="font-size:2.1em; font-weight:800; color:#1a237e;">MediSync: Multi-Modal Medical Analysis System</span>
+                </div>
+                <div style="margin-top: 8px; color: #374151; font-size: 1.1em;">
+                    <b>AI-powered healthcare solution</b> for seamless X-ray image and report analysis.<br>
+                    <ul style="margin: 0 0 0 1.2em;">
+                        <li>Upload a chest X-ray image</li>
+                        <li>Enter the corresponding medical report text</li>
+                        <li>Choose the analysis type: <b>Image</b>, <b>Text</b>, or <b>Multimodal</b></li>
+                        <li>Click <b>End Consultation</b> to complete your appointment</li>
+                    </ul>
+                </div>
+                """,
+                elem_id="ms-header"
+            )
 
-        # Add appointment ID input with Python-based population
         with gr.Row():
-            # Get appointment ID from URL parameters if available
             import urllib.parse
             try:
-                # This will be set by JavaScript, but we can also try to get it server-side
                 url_params = {}
                 if hasattr(gr, 'get_current_url'):
                     current_url = gr.get_current_url()
                     if current_url:
                         parsed = urllib.parse.urlparse(current_url)
                         url_params = urllib.parse.parse_qs(parsed.query)
-                
                 default_appointment_id = url_params.get('appointment_id', [''])[0]
             except:
                 default_appointment_id = ""
-            
             appointment_id_input = gr.Textbox(
                 label="Appointment ID",
                 placeholder="Enter your appointment ID here...",
                 info="This will be automatically populated if you came from the doctors page",
-                value=default_appointment_id
+                value=default_appointment_id,
+                elem_id="appointment_id_input"
             )
 
-        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 sample_image_path is None else None,
+        with gr.Tabs():
+            with gr.TabItem("Multimodal Analysis", id="tab-multimodal"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        multi_img_input = gr.Image(label="Upload X-ray Image", type="pil", elem_id="multi_img_input")
+                        multi_img_enhance = gr.Button("Enhance Image", icon="✨", elem_id="multi_img_enhance")
+                        multi_text_input = gr.Textbox(
+                            label="Enter Medical Report Text",
+                            placeholder="Enter the radiologist's report text here...",
+                            lines=10,
+                            value=example_report if sample_image_path is None else None,
+                            elem_id="multi_text_input"
+                        )
+                        multi_analyze_btn = gr.Button("Analyze Image & Text", variant="primary", icon="🔎", elem_id="multi_analyze_btn")
+                    with gr.Column(scale=1):
+                        multi_results = gr.HTML(label="Analysis Results", elem_id="multi_results")
+                        multi_plot = gr.HTML(label="Visualization", elem_id="multi_plot")
+                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",
                     )
 
-                    multi_analyze_btn = gr.Button(
-                        "Analyze Image & Text", variant="primary"
+            with gr.TabItem("Image Analysis", id="tab-image"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        img_input = gr.Image(label="Upload X-ray Image", type="pil", elem_id="img_input")
+                        img_enhance = gr.Button("Enhance Image", icon="✨", elem_id="img_enhance")
+                        img_analyze_btn = gr.Button("Analyze Image", variant="primary", icon="🔎", elem_id="img_analyze_btn")
+                    with gr.Column(scale=1):
+                        img_output = gr.Image(label="Processed Image", elem_id="img_output")
+                        img_results = gr.HTML(label="Analysis Results", elem_id="img_results")
+                        img_plot = gr.HTML(label="Visualization", elem_id="img_plot")
+                if sample_image_path:
+                    gr.Examples(
+                        examples=[[sample_image_path]],
+                        inputs=[img_input],
+                        label="Example X-ray Image",
                     )
 
-                with gr.Column():
-                    multi_results = gr.HTML(label="Analysis Results")
-                    multi_plot = gr.HTML(label="Visualization")
-
-            # Set up examples if sample image exists
-            if sample_image_path:
+            with gr.TabItem("Text Analysis", id="tab-text"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        text_input = gr.Textbox(
+                            label="Enter Medical Report Text",
+                            placeholder="Enter the radiologist's report text here...",
+                            lines=10,
+                            value=example_report,
+                            elem_id="text_input"
+                        )
+                        text_analyze_btn = gr.Button("Analyze Text", variant="primary", icon="🔎", elem_id="text_analyze_btn")
+                    with gr.Column(scale=1):
+                        text_output = gr.Textbox(label="Processed Text", elem_id="text_output")
+                        text_results = gr.HTML(label="Analysis Results", elem_id="text_results")
+                        text_plot = gr.HTML(label="Entity Visualization", elem_id="text_plot")
                 gr.Examples(
-                    examples=[[sample_image_path, example_report]],
-                    inputs=[multi_img_input, multi_text_input],
-                    label="Example X-ray and Report",
+                    examples=[[example_report]],
+                    inputs=[text_input],
+                    label="Example Medical 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")
-
-                with gr.Column():
-                    img_output = gr.Image(label="Processed Image")
-                    img_results = gr.HTML(label="Analysis Results")
-                    img_plot = gr.HTML(label="Visualization")
-
-            # Set up example if sample image exists
-            if sample_image_path:
-                gr.Examples(
-                    examples=[[sample_image_path]],
-                    inputs=[img_input],
-                    label="Example X-ray Image",
+            with gr.TabItem("About", id="tab-about"):
+                gr.Markdown(
+                    """
+                    <div style="font-size:1.1em; color:#374151;">
+                        <b>MediSync</b> is an AI-powered healthcare solution that uses multi-modal analysis to provide comprehensive insights from medical images and reports.<br><br>
+                        <b>Key Features</b>
+                        <ul>
+                            <li><b>X-ray Image Analysis:</b> Detects abnormalities in chest X-rays using pre-trained vision models</li>
+                            <li><b>Medical Report Processing:</b> Extracts key information from patient reports using NLP models</li>
+                            <li><b>Multi-modal Integration:</b> Combines insights from both image and text data for more accurate analysis</li>
+                        </ul>
+                        <b>Models Used</b>
+                        <ul>
+                            <li><b>X-ray Analysis:</b> facebook/deit-base-patch16-224-medical-cxr</li>
+                            <li><b>Medical Text Analysis:</b> medicalai/ClinicalBERT</li>
+                        </ul>
+                        <b>Important Disclaimer</b><br>
+                        <span style="color:#b91c1c;">
+                        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.
+                        </span>
+                    </div>
+                    """
                 )
 
-        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,
-                        value=example_report,
-                    )
-                    text_analyze_btn = gr.Button("Analyze Text", 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")
-
-            # Set up example
-            gr.Examples(
-                examples=[[example_report]],
-                inputs=[text_input],
-                label="Example Medical Report",
-            )
-
-        # End Consultation Section
         with gr.Row():
             with gr.Column():
                 end_consultation_btn = gr.Button(
-                    "End Consultation", 
-                    variant="stop", 
+                    "End Consultation",
+                    variant="stop",
                     size="lg",
-                    elem_classes=["end-consultation-btn"]
+                    elem_classes=["end-consultation-btn"],
+                    elem_id="end_consultation_btn"
                 )
-                end_consultation_status = gr.HTML(label="Status")
-
-        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.
-            """)
+                end_consultation_status = gr.HTML(label="Status", elem_id="end_consultation_status")
 
-        # Set up event handlers
+        # Event handlers
         multi_img_enhance.click(
             app.enhance_image, inputs=multi_img_input, outputs=multi_img_input
         )
@@ -995,35 +919,26 @@ def create_interface():
             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 handler
         def handle_end_consultation(appointment_id):
             if not appointment_id or appointment_id.strip() == "":
-                return "<div style='color: red; padding: 10px; background-color: #ffe6e6; border-radius: 5px;'>Please enter your appointment ID first.</div>"
-            
-            # Try to complete the appointment
+                return "<div class='ms-error'>Please enter your appointment ID first.</div>"
             result = complete_appointment(appointment_id.strip())
-            
             if result["status"] == "success":
-                # Get doctors page URLs from configuration
                 doctors_urls = get_doctors_page_urls()
-                
-                # Create success message with redirect button
                 html_response = f"""
-                <div style='color: green; padding: 15px; background-color: #e6ffe6; border-radius: 5px; margin: 10px 0;'>
+                <div style='color: #198754; padding: 15px; background-color: #e6ffe6; border-radius: 8px; margin: 10px 0;'>
                     <h3>✅ Consultation Completed Successfully!</h3>
                     <p>{result['message']}</p>
                     <p>Your appointment has been marked as completed.</p>
@@ -1038,11 +953,9 @@ def create_interface():
                 </div>
                 """
             else:
-                # Handle connection failure gracefully
                 if "Cannot connect to Flask app" in result['message']:
-                    # Show a helpful message with manual completion instructions
                     html_response = f"""
-                    <div style='color: orange; padding: 15px; background-color: #fff3cd; border-radius: 5px; margin: 10px 0;'>
+                    <div style='color: #b26a00; padding: 15px; background-color: #fff3cd; border-radius: 8px; margin: 10px 0;'>
                         <h3>⚠️ Consultation Ready to Complete</h3>
                         <p>Your consultation analysis is complete! However, we cannot automatically mark your appointment as completed because the Flask app is not accessible from this environment.</p>
                         <p><strong>Appointment ID:</strong> {appointment_id.strip()}</p>
@@ -1070,13 +983,12 @@ def create_interface():
                     """
                 else:
                     html_response = f"""
-                    <div style='color: red; padding: 15px; background-color: #ffe6e6; border-radius: 5px; margin: 10px 0;'>
+                    <div class='ms-error' style='padding: 15px; background-color: #ffe6e6; border-radius: 8px; margin: 10px 0;'>
                         <h3>❌ Error Completing Consultation</h3>
                         <p>{result['message']}</p>
                         <p>Please try again or contact support if the problem persists.</p>
                     </div>
                     """
-            
             return html_response
 
         end_consultation_btn.click(
@@ -1085,173 +997,32 @@ def create_interface():
             outputs=[end_consultation_status]
         )
 
-        # Add custom CSS and JavaScript for better styling and functionality
         gr.HTML("""
-        <style>
-        .end-consultation-btn {
-            background-color: #dc3545 !important;
-            border-color: #dc3545 !important;
-            color: white !important;
-            font-weight: bold !important;
-        }
-        .end-consultation-btn:hover {
-            background-color: #c82333 !important;
-            border-color: #bd2130 !important;
-        }
-        </style>
-        
         <script>
-        // Function to get URL parameters
         function getUrlParameter(name) {
             name = name.replace(/[[]/, '\\[').replace(/[\]]/, '\\]');
             var regex = new RegExp('[\\?&]' + name + '=([^&#]*)');
             var results = regex.exec(location.search);
-            return results === null ? '' : decodeURIComponent(results[1].replace(/\+/g, ' '));
+            return results === null ? '' : decodeURIComponent(results[1].replace(/\\+/g, ' '));
         }
-        
-        // Function to populate appointment ID from URL
         function populateAppointmentId() {
             var appointmentId = getUrlParameter('appointment_id');
-            console.log('Found appointment ID:', appointmentId);
-            
             if (appointmentId) {
-                // Try multiple methods to find and populate the appointment ID input
-                var success = false;
-                
-                // Method 1: Try by specific element ID
-                var elementById = document.getElementById('appointment_id_input');
-                if (elementById) {
-                    elementById.value = appointmentId;
+                var input = document.getElementById('appointment_id_input');
+                if (input) {
+                    input.value = appointmentId;
                     var event = new Event('input', { bubbles: true });
-                    elementById.dispatchEvent(event);
-                    console.log('Set appointment ID by ID to:', appointmentId);
-                    success = true;
-                }
-                
-                // Method 2: Try by placeholder text
-                if (!success) {
-                    var selectors = [
-                        'input[placeholder*="appointment ID"]',
-                        'input[placeholder*="appointment_id"]',
-                        'input[placeholder*="Appointment ID"]',
-                        'textarea[placeholder*="appointment ID"]',
-                        'textarea[placeholder*="appointment_id"]',
-                        'textarea[placeholder*="Appointment ID"]'
-                    ];
-                    
-                    for (var selector of selectors) {
-                        var elements = document.querySelectorAll(selector);
-                        for (var element of elements) {
-                            console.log('Found element by placeholder:', element);
-                            element.value = appointmentId;
-                            var event = new Event('input', { bubbles: true });
-                            element.dispatchEvent(event);
-                            console.log('Set appointment ID by placeholder to:', appointmentId);
-                            success = true;
-                            break;
-                        }
-                        if (success) break;
-                    }
-                }
-                
-                // Method 3: Try by label text
-                if (!success) {
-                    var labels = document.querySelectorAll('label');
-                    for (var label of labels) {
-                        if (label.textContent && label.textContent.toLowerCase().includes('appointment id')) {
-                            var input = label.nextElementSibling;
-                            if (input && (input.tagName === 'INPUT' || input.tagName === 'TEXTAREA')) {
-                                input.value = appointmentId;
-                                var event = new Event('input', { bubbles: true });
-                                input.dispatchEvent(event);
-                                console.log('Set appointment ID by label to:', appointmentId);
-                                success = true;
-                                break;
-                            }
-                        }
-                    }
-                }
-                
-                // Method 4: Try by Gradio component attributes
-                if (!success) {
-                    var gradioInputs = document.querySelectorAll('[data-testid="textbox"]');
-                    for (var input of gradioInputs) {
-                        var label = input.closest('.form').querySelector('label');
-                        if (label && label.textContent.toLowerCase().includes('appointment id')) {
-                            input.value = appointmentId;
-                            var event = new Event('input', { bubbles: true });
-                            input.dispatchEvent(event);
-                            console.log('Set appointment ID by Gradio component to:', appointmentId);
-                            success = true;
-                            break;
-                        }
-                    }
+                    input.dispatchEvent(event);
                 }
-                
-                if (!success) {
-                    console.log('Could not find appointment ID input field');
-                    // Log all input elements for debugging
-                    var allInputs = document.querySelectorAll('input, textarea');
-                    console.log('All input elements found:', allInputs.length);
-                    for (var i = 0; i < allInputs.length; i++) {
-                        console.log('Input', i, ':', allInputs[i].placeholder, allInputs[i].id, allInputs[i].className);
-                    }
-                }
-            } else {
-                console.log('No appointment ID found in URL');
-            }
-            return success;
-        }
-        
-        // Function to wait for Gradio to be ready
-        function waitForGradio() {
-            if (typeof gradio !== 'undefined' && gradio) {
-                console.log('Gradio detected, waiting for load...');
-                setTimeout(function() {
-                    populateAppointmentId();
-                    // Also try again after a longer delay
-                    setTimeout(populateAppointmentId, 2000);
-                }, 1000);
-            } else {
-                console.log('Gradio not detected, trying direct population...');
-                populateAppointmentId();
-                // Try again after a delay
-                setTimeout(populateAppointmentId, 1000);
             }
         }
-        
-        // Run when page loads
         document.addEventListener('DOMContentLoaded', function() {
-            console.log('DOM loaded, attempting to populate appointment ID...');
-            waitForGradio();
-        });
-        
-        // Also run when window loads
-        window.addEventListener('load', function() {
-            console.log('Window loaded, attempting to populate appointment ID...');
-            setTimeout(waitForGradio, 500);
-        });
-        
-        // Monitor for dynamic content changes
-        var observer = new MutationObserver(function(mutations) {
-            mutations.forEach(function(mutation) {
-                if (mutation.type === 'childList') {
-                    setTimeout(populateAppointmentId, 100);
-                }
-            });
-        });
-        
-        // Start observing
-        observer.observe(document.body, {
-            childList: true,
-            subtree: true
+            setTimeout(populateAppointmentId, 500);
         });
         </script>
         """)
 
-    # Run the interface
     interface.launch()
 
-
 if __name__ == "__main__":
-    create_interface() 
+    create_interface()