mediSync/app.py

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 configuration for end consultation logic
# try:
#     from .config import get_flask_urls, get_doctors_page_urls, TIMEOUT_SETTINGS
# except ImportError:
#     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)

# Import our modules for model and utility logic
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__)

# Ensure sample data directory exists
os.makedirs(os.path.join(parent_dir, "data", "sample"), exist_ok=True)

 

# Import configuration
try:
    from .config import get_flask_urls, get_doctors_page_urls, TIMEOUT_SETTINGS
except ImportError:
    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}

parent_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append(parent_dir)

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:
    def __init__(self):
        self.logger = logging.getLogger(__name__)
        self.logger.info("Initializing MediSync application")
        self._temp_files = []
        self.fusion_model = None
        self.image_model = None
        self.text_model = None

    def __del__(self):
        self.cleanup_temp_files()

    def cleanup_temp_files(self):
        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):
        if self.fusion_model is not None:
            return True
        try:
            self.logger.info("Loading models...")
            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):
        if image is None:
            return None
        try:
            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):
        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")
            results = {
                "primary_finding": "Normal chest X-ray",
                "confidence": 0.85,
                "has_abnormality": False,
                "predictions": [
                    ("Normal", 0.85),
                    ("Pneumonia", 0.10),
                    ("Cardiomegaly", 0.05)
                ]
            }
            fig = self.plot_image_prediction(
                image,
                results.get("predictions", []),
                f"Primary Finding: {results.get('primary_finding', 'Unknown')}"
            )
            plot_html = self.fig_to_html(fig)
            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

    def analyze_text(self, text):
        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")
            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"]
            }
            html_result = self.format_text_results(results)
            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):
        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")
            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"
                ]
            }
            html_result = self.format_multimodal_results(results)
            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):
        html_result = f"""
        <div class="medisync-card medisync-card-bg">
            <h2 class="medisync-title medisync-blue">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):
        html_result = f"""
        <div class="medisync-card medisync-card-bg">
            <h2 class="medisync-title medisync-green">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):
        html_result = f"""
        <div class="medisync-card medisync-card-bg">
            <h2 class="medisync-title medisync-purple">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):
        fig, ax = plt.subplots(figsize=(10, 6))
        ax.imshow(image)
        ax.set_title(title, fontsize=14, fontweight='bold', color='#007bff')
        ax.axis('off')
        return fig

    def create_entity_visualization(self, 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='#00bfae')
            ax.set_title('Entity Types Found in Text', fontsize=14, fontweight='bold', color='#00bfae')
            ax.set_ylabel('Count', color='#00bfae')
            plt.xticks(rotation=45, color='#222')
            plt.yticks(color='#222')
        return self.fig_to_html(fig)

    def create_multimodal_visualization(self, results):
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
        confidence = results.get("confidence", 0)
        ax1.pie([confidence, 1-confidence], labels=['Confidence', 'Uncertainty'],
                colors=['#00bfae', '#ff7675'], autopct='%1.1f%%', textprops={'color': '#222'})
        ax1.set_title('Analysis Confidence', fontweight='bold', color='#00bfae')
        recommendations = results.get("recommendations", [])
        ax2.bar(['Recommendations'], [len(recommendations)], color='#6c63ff')
        ax2.set_title('Number of Recommendations', fontweight='bold', color='#6c63ff')
        ax2.set_ylabel('Count', color='#6c63ff')
        plt.tight_layout()
        return self.fig_to_html(fig)

    def fig_to_html(self, fig):
        import io
        import base64
        buf = io.BytesIO()
        fig.savefig(buf, format='png', bbox_inches='tight', dpi=100, facecolor=fig.get_facecolor())
        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; background: transparent;"/>'

def complete_appointment(appointment_id):
    try:
        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
        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():
    app = MediSyncApp()
    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.
    """

    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 = str(sample_images[0]) if sample_images else None

    with gr.Blocks(
        title="MediSync: Multi-Modal Medical Analysis System",
        theme=gr.themes.Default(),  # Use Default for HuggingFace dark/light support
        css="""
        /* Modern neumorphic card style for all result containers */
        .medisync-card {
            border-radius: 18px;
            box-shadow: 0 4px 24px 0 rgba(0,0,0,0.10), 0 1.5px 4px 0 rgba(0,191,174,0.08);
            margin: 18px 0;
            padding: 24px 24px 18px 24px;
            font-size: 1.08rem;
            transition: background 0.2s, color 0.2s;
        }
        .medisync-card-bg {
            background: var(--background-fill-primary, #f8f9fa);
            color: var(--body-text-color, #222);
        }
        .medisync-title {
            font-weight: 700;
            margin-bottom: 0.7em;
        }
        .medisync-blue { color: #00bfae; }
        .medisync-green { color: #28a745; }
        .medisync-purple { color: #6c63ff; }
        .medisync-card ul, .medisync-card ol {
            margin-left: 1.2em;
        }
        .medisync-card li {
            margin-bottom: 0.2em;
        }
        /* Button and input styling for modern look */
        .gr-button, .end-consultation-btn {
            border-radius: 8px !important;
            font-weight: 600 !important;
            font-size: 1.08rem !important;
            transition: background 0.2s, color 0.2s;
        }
        .end-consultation-btn {
            background: linear-gradient(90deg, #dc3545 60%, #ff7675 100%) !important;
            border: none !important;
            color: #fff !important;
            box-shadow: 0 2px 8px 0 rgba(220,53,69,0.10);
        }
        .end-consultation-btn:hover {
            background: linear-gradient(90deg, #c82333 60%, #ff7675 100%) !important;
        }
        /* Responsive tweaks */
        @media (max-width: 900px) {
            .medisync-card { padding: 16px 8px 12px 8px; }
        }
        /* Ensure text is visible in dark mode */
        html[data-theme="dark"] .medisync-card-bg {
            background: #23272f !important;
            color: #f8fafc !important;
        }
        html[data-theme="dark"] .medisync-title {
            color: #00bfae !important;
        }
        html[data-theme="dark"] .medisync-blue { color: #00bfae !important; }
        html[data-theme="dark"] .medisync-green { color: #00e676 !important; }
        html[data-theme="dark"] .medisync-purple { color: #a385ff !important; }
        /* Make sure all gradio labels and text are visible */
        label, .gr-label, .gr-text, .gr-html, .gr-markdown {
            color: var(--body-text-color, #222) !important;
        }
        html[data-theme="dark"] label, html[data-theme="dark"] .gr-label, html[data-theme="dark"] .gr-text, html[data-theme="dark"] .gr-html, html[data-theme="dark"] .gr-markdown {
            color: #f8fafc !important;
        }
        """
    ) as interface:
        gr.Markdown(
            """
            <div style="display: flex; align-items: center; gap: 16px; margin-bottom: 0.5em;">
                <img src="https://cdn.jsdelivr.net/gh/saqib-ali-buriro/medivance-assets/medivance_logo.png" alt="Medivance Logo" style="height: 38px; border-radius: 8px; background: #fff; box-shadow: 0 2px 8px 0 rgba(26,115,232,0.10);">
                <span style="font-size: 2.1rem; font-weight: 700; color: #00bfae;">MediSync</span>
            </div>
            <div style="font-size: 1.18rem; margin-bottom: 1.2em;">
                <span style="color: var(--body-text-color, #222);">AI-powered Multi-Modal Medical Analysis System</span>
            </div>
            <div style="font-size: 1.05rem; margin-bottom: 1.2em;">
                <span style="color: var(--body-text-color, #222);">Seamlessly analyze X-ray images and medical reports for comprehensive healthcare insights.</span>
            </div>
            <div style="margin-bottom: 1.2em;">
                <ul style="font-size: 1.01rem; color: var(--body-text-color, #222);">
                    <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="medisync-header"
        )

        with gr.Row():
            import urllib.parse
            try:
                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,
                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", elem_id="multi_img_input")
                    multi_img_enhance = gr.Button("Enhance Image", icon="✨")
                    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="🔎")
                with gr.Column():
                    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",
                )

        with gr.Tab("🖼️ Image Analysis"):
            with gr.Row():
                with gr.Column():
                    img_input = gr.Image(label="Upload X-ray Image", type="pil", elem_id="img_input")
                    img_enhance = gr.Button("Enhance Image", icon="✨")
                    img_analyze_btn = gr.Button("Analyze Image", variant="primary", icon="🔎")
                with gr.Column():
                    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.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,
                        elem_id="text_input"
                    )
                    text_analyze_btn = gr.Button("Analyze Text", variant="primary", icon="🔎")
                with gr.Column():
                    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=[[example_report]],
                inputs=[text_input],
                label="Example Medical Report",
            )

        with gr.Row():
            with gr.Column():
                end_consultation_btn = gr.Button(
                    "End Consultation",
                    variant="stop",
                    size="lg",
                    elem_classes=["end-consultation-btn"],
                    icon="🛑"
                )
                end_consultation_status = gr.HTML(label="Status", elem_id="end_consultation_status")

        with gr.Tab("ℹ️ About"):
            gr.Markdown(
                """
                <div class="medisync-card medisync-card-bg">
                <h2 class="medisync-title medisync-blue">About MediSync</h2>
                <p>
                <b>MediSync</b> is an AI-powered healthcare solution that uses multi-modal analysis to provide comprehensive insights from medical images and reports.
                </p>
                <h3>Key Features</h3>
                <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>
                <h3>Models Used</h3>
                <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>
                <h3 style="color:#dc3545;">Important Disclaimer</h3>
                <p>
                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.
                </p>
                </div>
                """
            )

        # 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],
        )

        def handle_end_consultation(appointment_id):
            if not appointment_id or appointment_id.strip() == "":
                return "<div style='color: #dc3545; 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":
                doctors_urls = get_doctors_page_urls()
                html_response = f"""
                <div style='color: #28a745; 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: #00bfae; 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: #6c63ff; 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:
                if "Cannot connect to Flask app" in result['message']:
                    html_response = f"""
                    <div style='color: #ff9800; padding: 15px; background-color: #fff3cd; border-radius: 5px; 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>
                        <p><strong>Next Steps:</strong></p>
                        <ol>
                            <li>Copy your appointment ID: <code>{appointment_id.strip()}</code></li>
                            <li>Return to your Flask app (doctors page)</li>
                            <li>Manually complete the appointment using the appointment ID</li>
                        </ol>
                        <div style="margin-top: 15px;">
                            <button onclick="window.open('http://127.0.0.1:600/complete_appointment_manual?appointment_id={appointment_id.strip()}', '_blank')" 
                                    style="background-color: #00bfae; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer; margin-right: 10px;">
                                Complete Appointment
                            </button>
                            <button onclick="window.open('http://127.0.0.1:600/doctors', '_blank')" 
                                    style="background-color: #6c63ff; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer; margin-right: 10px;">
                                Return to Doctors Page
                            </button>
                            <button onclick="navigator.clipboard.writeText('{appointment_id.strip()}')" 
                                    style="background-color: #23272f; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;">
                                Copy Appointment ID
                            </button>
                        </div>
                    </div>
                    """
                else:
                    html_response = f"""
                    <div style='color: #dc3545; 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]
        )

        # JavaScript for appointment ID auto-population
        gr.HTML("""
        <script>
        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 populateAppointmentId() {
            var appointmentId = getUrlParameter('appointment_id');
            if (appointmentId) {
                var input = document.getElementById('appointment_id_input');
                if (input) {
                    input.value = appointmentId;
                    var event = new Event('input', { bubbles: true });
                    input.dispatchEvent(event);
                }
            }
        }
        document.addEventListener('DOMContentLoaded', function() {
            setTimeout(populateAppointmentId, 800);
        });
        window.addEventListener('load', function() {
            setTimeout(populateAppointmentId, 1200);
        });
        </script>
        """)

    interface.launch()

if __name__ == "__main__":
    create_interface()

# Some tests on this code