mediSync/app.py

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>"


import logging
import os
import sys
import tempfile
from pathlib import Path
import requests
import gradio as gr
import matplotlib.pyplot as plt
from PIL import Image
import json

# Import configuration
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",
            "http://localhost:600/complete_appointment",
            "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",
    handlers=[logging.StreamHandler(), logging.FileHandler("mediSync.log")],
)
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.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):
                    os.remove(temp_file)
                    self.logger.debug(f"Cleaned up temporary file: {temp_file}")
            except Exception as e:
                self.logger.warning(f"Failed to clean up temporary file {temp_file}: {e}")
        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:
            self.logger.error(f"Error loading models: {e}")
            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
        except Exception as e:
            self.logger.error(f"Error enhancing image: {e}")
            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

        if not self.load_models():
            return image, "Error: Models not loaded properly.", 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,
                "has_abnormality": False,
                "predictions": [
                    ("Normal", 0.85),
                    ("Pneumonia", 0.10),
                    ("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
            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

    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

        if not self.load_models():
            return text, "Error: Models not loaded properly.", 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},
                    {"text": "55-year-old male", "type": "PATIENT", "confidence": 0.90},
                    {"text": "cough and fever", "type": "SYMPTOM", "confidence": 0.88}
                ],
                "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

    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

        if not self.load_models():
            return "Error: Models not loaded properly.", 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,
                "image_contribution": "Normal cardiac silhouette and clear lung fields",
                "text_contribution": "Clinical history supports normal findings",
                "recommendations": [
                    "Follow-up CT for the 8mm nodular opacity",
                    "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

    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>
            <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>
            <ul>
        """
        
        for finding in results.get("key_findings", []):
            html_result += f"<li>{finding}</li>"
        
        html_result += "</ul>"
        
        html_result += "<h3>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>
            <p>{results.get("image_contribution", "No image analysis available")}</p>
            
            <h3>Text Contribution:</h3>
            <p>{results.get("text_contribution", "No text analysis available")}</p>
            
            <h3>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')
        ax.axis('off')
        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.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.set_title('Analysis Confidence', fontweight='bold')
        
        # Recommendations count
        recommendations = results.get("recommendations", [])
        ax2.bar(['Recommendations'], [len(recommendations)], color='lightblue')
        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;"/>'

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:
                    return {"status": "error", "message": "Appointment not found"}
                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
            except requests.exceptions.Timeout:
                logger.warning(f"Timeout connecting to {flask_api_url}")
                continue
            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", 
            "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_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)
        4. Click "End Consultation" when finished to complete your appointment
        """)

        # 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
            )

        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",
            )

        # End Consultation Section
        with gr.Row():
            with gr.Column():
                end_consultation_btn = gr.Button(
                    "End Consultation", 
                    variant="stop", 
                    size="lg",
                    elem_classes=["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.
            """)

        # 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 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>"
            
            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;'>
                    <h3>✅ Consultation Completed Successfully!</h3>
                    <p>{result['message']}</p>
                    <p>Your appointment has been marked as completed.</p>
                    <button onclick="window.open('{doctors_urls['local']}', '_blank')" 
                            style="background-color: #007bff; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer; margin-top: 10px;">
                        Return to Doctors Page (Local)
                    </button>
                    <button onclick="window.open('{doctors_urls['production']}', '_blank')" 
                            style="background-color: #28a745; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer; margin-top: 10px; margin-left: 10px;">
                        Return to Doctors Page (Production)
                    </button>
                </div>
                """
            else:
                html_response = f"""
                <div style='color: red; padding: 15px; background-color: #ffe6e6; border-radius: 5px; 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(
            handle_end_consultation,
            inputs=[appointment_id_input],
            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, ' '));
        }
        
        // Function to populate appointment ID from URL
        function populateAppointmentId() {
            var appointmentId = getUrlParameter('appointment_id');
            console.log('Found appointment ID:', appointmentId);
            
            if (appointmentId) {
                // Try multiple selectors to find the appointment ID input
                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:', element);
                        element.value = appointmentId;
                        // Trigger change event
                        var event = new Event('input', { bubbles: true });
                        element.dispatchEvent(event);
                        console.log('Set appointment ID to:', appointmentId);
                        return true;
                    }
                }
                
                // If not found by placeholder, try by label
                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);
                            return true;
                        }
                    }
                }
                
                console.log('Could not find appointment ID input field');
            } else {
                console.log('No appointment ID found in URL');
            }
            return false;
        }
        
        // 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
        });
        </script>
        """)

    # Run the interface
    interface.launch()


if __name__ == "__main__":
    create_interface()