Added the end consultation section ✅✅

mediSync/app.py

@@ -1,560 +1,627 @@
-import logging
-import os
-import sys
-import tempfile
-from pathlib import Path
-
-import gradio as gr
-import matplotlib.pyplot as plt
-from PIL import Image
-
-# Add parent directory to path
-parent_dir = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(parent_dir)
-
-# Import our modules
-from models.multimodal_fusion import MultimodalFusion
-from utils.preprocessing import enhance_xray_image, normalize_report_text
-from utils.visualization import (
-    plot_image_prediction,
-    plot_multimodal_results,
-    plot_report_entities,
-)
-
-# Set up logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
-    handlers=[logging.StreamHandler(), logging.FileHandler("mediSync.log")],
-)
-logger = logging.getLogger(__name__)
-
-# Create temporary directory for sample data if it doesn't exist
-os.makedirs(os.path.join(parent_dir, "data", "sample"), exist_ok=True)
-
-
-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")
-
-        # Initialize models with None for lazy loading
-        self.fusion_model = None
-        self.image_model = None
-        self.text_model = None
-
-    def load_models(self):
-        """
-        Load models if not already loaded.
-
-        Returns:
-            bool: True if models loaded successfully, False otherwise
-        """
-        try:
-            if self.fusion_model is None:
-                self.logger.info("Loading models...")
-                self.fusion_model = MultimodalFusion()
-                self.image_model = self.fusion_model.image_analyzer
-                self.text_model = self.fusion_model.text_analyzer
-                self.logger.info("Models loaded successfully")
-            return True
-
-        except Exception as e:
-            self.logger.error(f"Error loading models: {e}")
-            return False
-
-    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)
-        """
-        try:
-            # Ensure models are loaded
-            if not self.load_models() or self.image_model is None:
-                return image, "Error: Models not loaded properly.", None
-
-            # Save uploaded image to a temporary file
-            temp_dir = tempfile.mkdtemp()
-            temp_path = os.path.join(temp_dir, "upload.png")
-
-            if isinstance(image, str):
-                # Copy the file if it's a path
-                from shutil import copyfile
-
-                copyfile(image, temp_path)
-            else:
-                # Save if it's a Gradio UploadButton image
-                image.save(temp_path)
-
-            # Run image analysis
-            self.logger.info(f"Analyzing image: {temp_path}")
-            results = self.image_model.analyze(temp_path)
-
-            # Create visualization
-            fig = 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)
-
-            # Format results as HTML
-            html_result = f"""
-            <h2>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>
-            <ul>
-            """
-
-            # Add top 5 predictions
-            for label, prob in results.get("predictions", [])[:5]:
-                html_result += f"<li>{label}: {prob:.1%}</li>"
-
-            html_result += "</ul>"
-
-            # Add explanation
-            explanation = self.image_model.get_explanation(results)
-            html_result += f"<h3>Analysis Explanation:</h3><p>{explanation}</p>"
-
-            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
-
-    def analyze_text(self, text):
-        """
-        Analyze a medical report text.
-
-        Args:
-            text: Report text input through Gradio
-
-        Returns:
-            tuple: (text, text_results_html, entities_plot_html)
-        """
-        try:
-            # Ensure models are loaded
-            if not self.load_models() or self.text_model is None:
-                return text, "Error: Models not loaded properly.", None
-
-            # Check for empty text
-            if not text or len(text.strip()) < 10:
-                return (
-                    text,
-                    "Error: Please enter a valid medical report text (at least 10 characters).",
-                    None,
-                )
-
-            # Normalize text
-            normalized_text = normalize_report_text(text)
-
-            # Run text analysis
-            self.logger.info("Analyzing medical report text")
-            results = self.text_model.analyze(normalized_text)
-
-            # Get entities and create visualization
-            entities = results.get("entities", {})
-            fig = plot_report_entities(normalized_text, entities)
-
-            # Convert to HTML for display
-            entities_plot_html = self.fig_to_html(fig)
-
-            # Format results as HTML
-            html_result = f"""
-            <h2>Medical Report Analysis Results</h2>
-            <p><strong>Severity Level:</strong> {results.get("severity", {}).get("level", "Unknown")}</p>
-            <p><strong>Severity Score:</strong> {results.get("severity", {}).get("score", 0)}/4</p>
-            <p><strong>Confidence:</strong> {results.get("severity", {}).get("confidence", 0):.1%}</p>
-            
-            <h3>Key Findings:</h3>
-            <ul>
-            """
-
-            # Add findings
-            findings = results.get("findings", [])
-            if findings:
-                for finding in findings:
-                    html_result += f"<li>{finding}</li>"
-            else:
-                html_result += "<li>No specific findings detailed.</li>"
-
-            html_result += "</ul>"
-
-            # Add entities
-            html_result += "<h3>Extracted Medical Entities:</h3>"
-
-            for category, items in entities.items():
-                if items:
-                    html_result += f"<p><strong>{category.capitalize()}:</strong> {', '.join(items)}</p>"
-
-            # Add follow-up recommendations
-            html_result += "<h3>Follow-up Recommendations:</h3><ul>"
-            followups = results.get("followup_recommendations", [])
-
-            if followups:
-                for rec in followups:
-                    html_result += f"<li>{rec}</li>"
-            else:
-                html_result += "<li>No specific follow-up recommendations.</li>"
-
-            html_result += "</ul>"
-
-            return text, html_result, entities_plot_html
-
-        except Exception as e:
-            self.logger.error(f"Error in text analysis: {e}")
-            return text, f"Error analyzing text: {str(e)}", None
-
-    def analyze_multimodal(self, image, text):
-        """
-        Perform multimodal analysis of image and text.
-
-        Args:
-            image: Image file uploaded through Gradio
-            text: Report text input through Gradio
-
-        Returns:
-            tuple: (results_html, multimodal_plot_html)
-        """
-        try:
-            # Ensure models are loaded
-            if not self.load_models() or self.fusion_model is None:
-                return "Error: Models not loaded properly.", None
-
-            # Check for empty inputs
-            if image is None:
-                return "Error: Please upload an X-ray image for analysis.", None
-
-            if not text or len(text.strip()) < 10:
-                return (
-                    "Error: Please enter a valid medical report text (at least 10 characters).",
-                    None,
-                )
-
-            # Save uploaded image to a temporary file
-            temp_dir = tempfile.mkdtemp()
-            temp_path = os.path.join(temp_dir, "upload.png")
-
-            if isinstance(image, str):
-                # Copy the file if it's a path
-                from shutil import copyfile
-
-                copyfile(image, temp_path)
-            else:
-                # Save if it's a Gradio UploadButton image
-                image.save(temp_path)
-
-            # Normalize text
-            normalized_text = normalize_report_text(text)
-
-            # Run multimodal analysis
-            self.logger.info("Performing multimodal analysis")
-            results = self.fusion_model.analyze(temp_path, normalized_text)
-
-            # Create visualization
-            fig = plot_multimodal_results(results, image, text)
-
-            # Convert to HTML for display
-            plot_html = self.fig_to_html(fig)
-
-            # Generate explanation
-            explanation = self.fusion_model.get_explanation(results)
-
-            # Format results as HTML
-            html_result = f"""
-            <h2>Multimodal Medical Analysis Results</h2>
-            
-            <h3>Overview</h3>
-            <p><strong>Primary Finding:</strong> {results.get("primary_finding", "Unknown")}</p>
-            <p><strong>Severity Level:</strong> {results.get("severity", {}).get("level", "Unknown")}</p>
-            <p><strong>Severity Score:</strong> {results.get("severity", {}).get("score", 0)}/4</p>
-            <p><strong>Agreement Score:</strong> {results.get("agreement_score", 0):.0%}</p>
-            
-            <h3>Detailed Findings</h3>
-            <ul>
-            """
-
-            # Add findings
-            findings = results.get("findings", [])
-            if findings:
-                for finding in findings:
-                    html_result += f"<li>{finding}</li>"
-            else:
-                html_result += "<li>No specific findings detailed.</li>"
-
-            html_result += "</ul>"
-
-            # Add follow-up recommendations
-            html_result += "<h3>Recommended Follow-up</h3><ul>"
-            followups = results.get("followup_recommendations", [])
-
-            if followups:
-                for rec in followups:
-                    html_result += f"<li>{rec}</li>"
-            else:
-                html_result += (
-                    "<li>No specific follow-up recommendations provided.</li>"
-                )
-
-            html_result += "</ul>"
-
-            # Add confidence note
-            confidence = results.get("severity", {}).get("confidence", 0)
-            html_result += f"""
-            <p><em>Note: This analysis has a confidence level of {confidence:.0%}. 
-            Please consult with healthcare professionals for official diagnosis.</em></p>
-            """
-
-            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
-
-    def enhance_image(self, image):
-        """
-        Enhance X-ray image contrast.
-
-        Args:
-            image: Image file uploaded through Gradio
-
-        Returns:
-            PIL.Image: Enhanced image
-        """
-        try:
-            if image is None:
-                return None
-
-            # Save uploaded image to a temporary file
-            temp_dir = tempfile.mkdtemp()
-            temp_path = os.path.join(temp_dir, "upload.png")
-
-            if isinstance(image, str):
-                # Copy the file if it's a path
-                from shutil import copyfile
-
-                copyfile(image, temp_path)
-            else:
-                # Save if it's a Gradio UploadButton image
-                image.save(temp_path)
-
-            # Enhance image
-            self.logger.info(f"Enhancing image: {temp_path}")
-            output_path = os.path.join(temp_dir, "enhanced.png")
-            enhance_xray_image(temp_path, output_path)
-
-            # Load enhanced image
-            enhanced = Image.open(output_path)
-            return enhanced
-
-        except Exception as e:
-            self.logger.error(f"Error enhancing image: {e}")
-            return image  # Return original image on error
-
-    def fig_to_html(self, fig):
-        """Convert matplotlib figure to HTML for display in Gradio."""
-        try:
-            import base64
-            import io
-
-            buf = io.BytesIO()
-            fig.savefig(buf, format="png", bbox_inches="tight")
-            buf.seek(0)
-            img_str = base64.b64encode(buf.read()).decode("utf-8")
-            plt.close(fig)
-
-            return f'<img src="data:image/png;base64,{img_str}" alt="Analysis Plot">'
-
-        except Exception as e:
-            self.logger.error(f"Error converting figure to HTML: {e}")
-            return "<p>Error displaying visualization.</p>"
-
-
-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_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()
-    ) 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)
-        """)
-
-        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,
-                    )
-
-                    multi_analyze_btn = gr.Button(
-                        "Analyze Image & Text", variant="primary"
-                    )
-
-                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:
-                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")
-
-                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.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",
-            )
-
-        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.
-            """)
-
-        # 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],
-        )
-
-    # Run the interface
-    interface.launch()
-
-
-if __name__ == "__main__":
-    create_interface()
+import logging
+import os
+import sys
+import tempfile
+from pathlib import Path
+
+import gradio as gr
+import matplotlib.pyplot as plt
+from PIL import Image
+
+# Add parent directory to path
+parent_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(parent_dir)
+
+# Import our modules
+from models.multimodal_fusion import MultimodalFusion
+from utils.preprocessing import enhance_xray_image, normalize_report_text
+from utils.visualization import (
+    plot_image_prediction,
+    plot_multimodal_results,
+    plot_report_entities,
+)
+
+# Set up logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    handlers=[logging.StreamHandler(), logging.FileHandler("mediSync.log")],
+)
+logger = logging.getLogger(__name__)
+
+# Create temporary directory for sample data if it doesn't exist
+os.makedirs(os.path.join(parent_dir, "data", "sample"), exist_ok=True)
+
+
+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")
+
+        # Initialize models with None for lazy loading
+        self.fusion_model = None
+        self.image_model = None
+        self.text_model = None
+
+    def load_models(self):
+        """
+        Load models if not already loaded.
+
+        Returns:
+            bool: True if models loaded successfully, False otherwise
+        """
+        try:
+            if self.fusion_model is None:
+                self.logger.info("Loading models...")
+                self.fusion_model = MultimodalFusion()
+                self.image_model = self.fusion_model.image_analyzer
+                self.text_model = self.fusion_model.text_analyzer
+                self.logger.info("Models loaded successfully")
+            return True
+
+        except Exception as e:
+            self.logger.error(f"Error loading models: {e}")
+            return False
+
+    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)
+        """
+        try:
+            # Ensure models are loaded
+            if not self.load_models() or self.image_model is None:
+                return image, "Error: Models not loaded properly.", None
+
+            # Save uploaded image to a temporary file
+            temp_dir = tempfile.mkdtemp()
+            temp_path = os.path.join(temp_dir, "upload.png")
+
+            if isinstance(image, str):
+                # Copy the file if it's a path
+                from shutil import copyfile
+
+                copyfile(image, temp_path)
+            else:
+                # Save if it's a Gradio UploadButton image
+                image.save(temp_path)
+
+            # Run image analysis
+            self.logger.info(f"Analyzing image: {temp_path}")
+            results = self.image_model.analyze(temp_path)
+
+            # Create visualization
+            fig = 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)
+
+            # Format results as HTML
+            html_result = f"""
+            <h2>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>
+            <ul>
+            """
+
+            # Add top 5 predictions
+            for label, prob in results.get("predictions", [])[:5]:
+                html_result += f"<li>{label}: {prob:.1%}</li>"
+
+            html_result += "</ul>"
+
+            # Add explanation
+            explanation = self.image_model.get_explanation(results)
+            html_result += f"<h3>Analysis Explanation:</h3><p>{explanation}</p>"
+
+            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
+
+    def analyze_text(self, text):
+        """
+        Analyze a medical report text.
+
+        Args:
+            text: Report text input through Gradio
+
+        Returns:
+            tuple: (text, text_results_html, entities_plot_html)
+        """
+        try:
+            # Ensure models are loaded
+            if not self.load_models() or self.text_model is None:
+                return text, "Error: Models not loaded properly.", None
+
+            # Check for empty text
+            if not text or len(text.strip()) < 10:
+                return (
+                    text,
+                    "Error: Please enter a valid medical report text (at least 10 characters).",
+                    None,
+                )
+
+            # Normalize text
+            normalized_text = normalize_report_text(text)
+
+            # Run text analysis
+            self.logger.info("Analyzing medical report text")
+            results = self.text_model.analyze(normalized_text)
+
+            # Get entities and create visualization
+            entities = results.get("entities", {})
+            fig = plot_report_entities(normalized_text, entities)
+
+            # Convert to HTML for display
+            entities_plot_html = self.fig_to_html(fig)
+
+            # Format results as HTML
+            html_result = f"""
+            <h2>Medical Report Analysis Results</h2>
+            <p><strong>Severity Level:</strong> {results.get("severity", {}).get("level", "Unknown")}</p>
+            <p><strong>Severity Score:</strong> {results.get("severity", {}).get("score", 0)}/4</p>
+            <p><strong>Confidence:</strong> {results.get("severity", {}).get("confidence", 0):.1%}</p>
+            
+            <h3>Key Findings:</h3>
+            <ul>
+            """
+
+            # Add findings
+            findings = results.get("findings", [])
+            if findings:
+                for finding in findings:
+                    html_result += f"<li>{finding}</li>"
+            else:
+                html_result += "<li>No specific findings detailed.</li>"
+
+            html_result += "</ul>"
+
+            # Add entities
+            html_result += "<h3>Extracted Medical Entities:</h3>"
+
+            for category, items in entities.items():
+                if items:
+                    html_result += f"<p><strong>{category.capitalize()}:</strong> {', '.join(items)}</p>"
+
+            # Add follow-up recommendations
+            html_result += "<h3>Follow-up Recommendations:</h3><ul>"
+            followups = results.get("followup_recommendations", [])
+
+            if followups:
+                for rec in followups:
+                    html_result += f"<li>{rec}</li>"
+            else:
+                html_result += "<li>No specific follow-up recommendations.</li>"
+
+            html_result += "</ul>"
+
+            return text, html_result, entities_plot_html
+
+        except Exception as e:
+            self.logger.error(f"Error in text analysis: {e}")
+            return text, f"Error analyzing text: {str(e)}", None
+
+    def analyze_multimodal(self, image, text):
+        """
+        Perform multimodal analysis of image and text.
+
+        Args:
+            image: Image file uploaded through Gradio
+            text: Report text input through Gradio
+
+        Returns:
+            tuple: (results_html, multimodal_plot_html)
+        """
+        try:
+            # Ensure models are loaded
+            if not self.load_models() or self.fusion_model is None:
+                return "Error: Models not loaded properly.", None
+
+            # Check for empty inputs
+            if image is None:
+                return "Error: Please upload an X-ray image for analysis.", None
+
+            if not text or len(text.strip()) < 10:
+                return (
+                    "Error: Please enter a valid medical report text (at least 10 characters).",
+                    None,
+                )
+
+            # Save uploaded image to a temporary file
+            temp_dir = tempfile.mkdtemp()
+            temp_path = os.path.join(temp_dir, "upload.png")
+
+            if isinstance(image, str):
+                # Copy the file if it's a path
+                from shutil import copyfile
+
+                copyfile(image, temp_path)
+            else:
+                # Save if it's a Gradio UploadButton image
+                image.save(temp_path)
+
+            # Normalize text
+            normalized_text = normalize_report_text(text)
+
+            # Run multimodal analysis
+            self.logger.info("Performing multimodal analysis")
+            results = self.fusion_model.analyze(temp_path, normalized_text)
+
+            # Create visualization
+            fig = plot_multimodal_results(results, image, text)
+
+            # Convert to HTML for display
+            plot_html = self.fig_to_html(fig)
+
+            # Generate explanation
+            explanation = self.fusion_model.get_explanation(results)
+
+            # Format results as HTML
+            html_result = f"""
+            <h2>Multimodal Medical Analysis Results</h2>
+            
+            <h3>Overview</h3>
+            <p><strong>Primary Finding:</strong> {results.get("primary_finding", "Unknown")}</p>
+            <p><strong>Severity Level:</strong> {results.get("severity", {}).get("level", "Unknown")}</p>
+            <p><strong>Severity Score:</strong> {results.get("severity", {}).get("score", 0)}/4</p>
+            <p><strong>Agreement Score:</strong> {results.get("agreement_score", 0):.0%}</p>
+            
+            <h3>Detailed Findings</h3>
+            <ul>
+            """
+
+            # Add findings
+            findings = results.get("findings", [])
+            if findings:
+                for finding in findings:
+                    html_result += f"<li>{finding}</li>"
+            else:
+                html_result += "<li>No specific findings detailed.</li>"
+
+            html_result += "</ul>"
+
+            # Add follow-up recommendations
+            html_result += "<h3>Recommended Follow-up</h3><ul>"
+            followups = results.get("followup_recommendations", [])
+
+            if followups:
+                for rec in followups:
+                    html_result += f"<li>{rec}</li>"
+            else:
+                html_result += (
+                    "<li>No specific follow-up recommendations provided.</li>"
+                )
+
+            html_result += "</ul>"
+
+            # Add confidence note
+            confidence = results.get("severity", {}).get("confidence", 0)
+            html_result += f"""
+            <p><em>Note: This analysis has a confidence level of {confidence:.0%}. 
+            Please consult with healthcare professionals for official diagnosis.</em></p>
+            """
+
+            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
+
+    def enhance_image(self, image):
+        """
+        Enhance X-ray image contrast.
+
+        Args:
+            image: Image file uploaded through Gradio
+
+        Returns:
+            PIL.Image: Enhanced image
+        """
+        try:
+            if image is None:
+                return None
+
+            # Save uploaded image to a temporary file
+            temp_dir = tempfile.mkdtemp()
+            temp_path = os.path.join(temp_dir, "upload.png")
+
+            if isinstance(image, str):
+                # Copy the file if it's a path
+                from shutil import copyfile
+
+                copyfile(image, temp_path)
+            else:
+                # Save if it's a Gradio UploadButton image
+                image.save(temp_path)
+
+            # Enhance image
+            self.logger.info(f"Enhancing image: {temp_path}")
+            output_path = os.path.join(temp_dir, "enhanced.png")
+            enhance_xray_image(temp_path, output_path)
+
+            # Load enhanced image
+            enhanced = Image.open(output_path)
+            return enhanced
+
+        except Exception as e:
+            self.logger.error(f"Error enhancing image: {e}")
+            return image  # Return original image on error
+
+    def fig_to_html(self, fig):
+        """Convert matplotlib figure to HTML for display in Gradio."""
+        try:
+            import base64
+            import io
+
+            buf = io.BytesIO()
+            fig.savefig(buf, format="png", bbox_inches="tight")
+            buf.seek(0)
+            img_str = base64.b64encode(buf.read()).decode("utf-8")
+            plt.close(fig)
+
+            return f'<img src="data:image/png;base64,{img_str}" alt="Analysis Plot">'
+
+        except Exception as e:
+            self.logger.error(f"Error converting figure to HTML: {e}")
+            return "<p>Error displaying visualization.</p>"
+
+
+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_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()
+    ) as interface:
+        # Get appointment ID from URL parameters if present
+        appointment_id = gr.Textbox(
+            visible=False,
+            value=gr.Request.query_params.get("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
+        """)
+
+        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,
+                    )
+
+                    multi_analyze_btn = gr.Button(
+                        "Analyze Image & Text", variant="primary"
+                    )
+
+                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:
+                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")
+
+                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.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",
+            )
+
+        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.
+            """)
+
+        # 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],
+        )
+
+        # Handle consultation completion
+        end_consultation_btn.click(
+            fn=complete_consultation,
+            inputs=[appointment_id],
+            outputs=completion_status
+        )
+
+    # Run the interface
+    interface.launch()
+
+def complete_consultation(appointment_id):
+    """Handle consultation completion by notifying the Flask app."""
+    if not appointment_id:
+        return "<div class='alert alert-error'>No appointment ID found. Please contact support.</div>"
+    
+    try:
+        # Call your Flask app's completion endpoint
+        # 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},
+            timeout=10
+        )
+        
+        if response.status_code == 200:
+            # Return JavaScript to redirect back to the doctors page
+            return """
+            <div class='alert alert-success'>
+                Consultation completed successfully. Redirecting...
+                <script>
+                    setTimeout(function() {
+                        window.location.href = "http://127.0.0.1:600/doctors";
+                    }, 2000);
+                </script>
+            </div>
+            """
+        else:
+            return f"""
+            <div class='alert alert-error'>
+                Error completing appointment (Status: {response.status_code}). 
+                Please contact support.
+            </div>
+            """
+            
+    except Exception as e:
+        return f"""
+        <div class='alert alert-error'>
+            Error: {str(e)}
+        </div>
+        """
+
+if __name__ == "__main__":
+    create_interface()