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)

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.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:
            if image is None:
                return None, "Please upload an image first.", None
            if not self.load_models() or self.image_model is None:
                return image, "Error: Models not loaded properly.", None

            temp_dir = tempfile.mkdtemp()
            temp_path = os.path.join(temp_dir, "upload.png")
            if isinstance(image, str):
                from shutil import copyfile
                copyfile(image, temp_path)
            else:
                image.save(temp_path)

            self.logger.info(f"Analyzing image: {temp_path}")
            results = self.image_model.analyze(temp_path)

            fig = plot_image_prediction(
                image,
                results.get("predictions", []),
                f"Primary Finding: {results.get('primary_finding', 'Unknown')}",
            )
            plot_html = self.fig_to_html(fig)

            html_result = f"""
            <div class="medisync-card medisync-card-bg medisync-force-text">
                <h2 class="medisync-title medisync-blue">
                    <b>X-ray Analysis Results</b>
                </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>"
            explanation = self.image_model.get_explanation(results)
            html_result += f"<h3>Analysis Explanation:</h3><p>{explanation}</p>"
            html_result += "</div>"
            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:
            if not text or text.strip() == "":
                return "", "Please enter medical report text.", None
            if not self.load_models() or self.text_model is None:
                return text, "Error: Models not loaded properly.", None
            if not text or len(text.strip()) < 10:
                return (
                    text,
                    "Error: Please enter a valid medical report text (at least 10 characters).",
                    None,
                )
            normalized_text = normalize_report_text(text)
            self.logger.info("Analyzing medical report text")
            results = self.text_model.analyze(normalized_text)
            entities = results.get("entities", {})
            fig = plot_report_entities(normalized_text, entities)
            entities_plot_html = self.fig_to_html(fig)
            html_result = f"""
            <div class="medisync-card medisync-card-bg medisync-force-text">
                <h2 class="medisync-title medisync-green">
                    <b>Text Analysis Results</b>
                </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>
            """
            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>"
            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>"
            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></div>"
            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:
            if not self.load_models() or self.fusion_model is None:
                return "Error: Models not loaded properly.", None
            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,
                )
            temp_dir = tempfile.mkdtemp()
            temp_path = os.path.join(temp_dir, "upload.png")
            if isinstance(image, str):
                from shutil import copyfile
                copyfile(image, temp_path)
            else:
                image.save(temp_path)
            normalized_text = normalize_report_text(text)
            self.logger.info("Performing multimodal analysis")
            results = self.fusion_model.analyze(temp_path, normalized_text)
            fig = plot_multimodal_results(results, image, text)
            plot_html = self.fig_to_html(fig)
            explanation = self.fusion_model.get_explanation(results)
            html_result = f"""
            <div class="medisync-card medisync-card-bg medisync-force-text">
                <h2 class="medisync-title medisync-purple">
                    <b>Multimodal Analysis Results</b>
                </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>
            """
            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>"
            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>"
            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>
                <h3>Analysis Explanation:</h3>
                <p>{explanation}</p>
            </div>
            """
            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
            temp_dir = tempfile.mkdtemp()
            temp_path = os.path.join(temp_dir, "upload.png")
            if isinstance(image, str):
                from shutil import copyfile
                copyfile(image, temp_path)
            else:
                image.save(temp_path)
            self.logger.info(f"Enhancing image: {temp_path}")
            output_path = os.path.join(temp_dir, "enhanced.png")
            enhance_xray_image(temp_path, output_path)
            enhanced = Image.open(output_path)
            return enhanced
        except Exception as e:
            self.logger.error(f"Error enhancing image: {e}")
            return image

    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", dpi=100, facecolor=fig.get_facecolor())
            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}" style="max-width: 100%; height: auto; background: transparent;"/>'
        except Exception as e:
            self.logger.error(f"Error converting figure to HTML: {e}")
            return "<p>Error displaying visualization.</p>"

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():
    import urllib.parse
    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(),
        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: 900;
            font-size: 1.45em;
            margin-bottom: 0.7em;
            letter-spacing: 1px;
            text-shadow: 0 2px 8px #00bfae33, 0 1px 0 #fff;
            /* Remove display:flex and gap for simple bold text */
        }
        .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: 1rem !important;
            padding: 8px 18px !important;
            min-width: 120px !important;
            min-height: 38px !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);
            font-size: 1.05rem !important;
            padding: 10px 24px !important;
            min-width: 160px !important;
            min-height: 40px !important;
        }
        .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; }
            .medisync-title { font-size: 1.1em; }
        }
        /* Ensure text is visible in dark mode */
        html[data-theme="dark"] .medisync-card-bg,
        html[data-theme="dark"] .medisync-card-bg.medisync-force-text {
            background: #23272f !important;
            color: #f8fafc !important;
        }
        html[data-theme="dark"] .medisync-title {
            color: #00bfae !important;
            text-shadow: 0 2px 8px #00bfae33, 0 1px 0 #23272f;
        }
        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;
        }
        /* Force all text in medisync-card and status outputs to be visible in all themes */
        .medisync-force-text, .medisync-force-text * {
            color: var(--body-text-color, #222) !important;
        }
        html[data-theme="dark"] .medisync-force-text, html[data-theme="dark"] .medisync-force-text * {
            color: #f8fafc !important;
        }
        /* End consultation status output: remove color and theme, keep text black and simple */
        #end_consultation_status, #end_consultation_status * {
            color: #000 !important;
            background: #fff !important;
            font-size: 1.12rem !important;
            font-weight: 600 !important;
        }
        /* Style the buttons inside the end consultation status popup */
        #end_consultation_status button {
            font-size: 1rem !important;
            font-weight: 600 !important;
            border-radius: 6px !important;
            padding: 8px 18px !important;
            margin-top: 8px !important;
            margin-bottom: 4px !important;
            min-width: 120px !important;
            min-height: 36px !important;
            box-shadow: 0 1.5px 4px 0 rgba(0,191,174,0.08);
        }
        #end_consultation_status button:active, #end_consultation_status button:focus {
            outline: 2px solid #00bfae !important;
        }
        #end_consultation_status .btn-green {
            background-color: #00bfae !important;
            color: #fff !important;
        }
        #end_consultation_status .btn-purple {
            background-color: #6c63ff !important;
            color: #fff !important;
        }
        #end_consultation_status .btn-dark {
            background-color: #23272f !important;
            color: #fff !important;
        }
        #end_consultation_status .btn-orange {
            background-color: #ff9800 !important;
            color: #fff !important;
        }
        #end_consultation_status .btn-red {
            background-color: #dc3545 !important;
            color: #fff !important;
        }
        """
    ) as interface:
        gr.Markdown(
            """
            <div style="margin-bottom: 0.5em;">
                <span style="font-size: 2.4rem; font-weight: bold; letter-spacing: 1.5px;">
                    <b>MediSync</b>
                </span>
            </div>
            <div style="font-size: 1.22rem; margin-bottom: 1.2em; font-weight: 600;">
                <span>AI-powered Multi-Modal Medical Analysis System</span>
            </div>
            <div style="font-size: 1.09rem; margin-bottom: 1.2em;">
                <span>Seamlessly analyze X-ray images and medical reports for comprehensive healthcare insights.</span>
            </div>
            <div style="margin-bottom: 1.2em;">
                <ul style="font-size: 1.04rem;">
                    <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"
        )
        
        # --- BRUTAL FIX: Always set appointment id from URL using JS, forcibly, and keep it in sync ---
        with gr.Row():
            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="",
                elem_id="appointment_id_input"
            )

        # Populate appointment id from URL on initial load using server-side request (robust, no JS dependency)
        def _populate_appointment_id_on_load(request: gr.Request):
            try:
                params = getattr(request, "query_params", {}) or {}
                appointment_id = params.get("appointment_id", "")
                if appointment_id:
                    return gr.update(value=appointment_id)
                return gr.update()
            except Exception as e:
                logger.warning(f"Could not populate appointment_id from URL: {e}")
                return gr.update()

        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")
                    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")
                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")
                    img_analyze_btn = gr.Button("Analyze Image", variant="primary")
                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")
                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"]
                )
                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 medisync-force-text">
                <h2 class="medisync-title medisync-blue">
                    <b>About MediSync</b>
                </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):
            # Output status: styled with color for buttons and clear status box, as per template
            if not appointment_id or appointment_id.strip() == "":
                return "<div style='color: #000; background: #fff; padding: 10px; 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: #000; background: #fff; padding: 15px; border-radius: 5px; margin: 10px 0;'>
                    <h3 style="color: #28a745;">✅ Consultation Completed Successfully!</h3>
                    <p style="color: #28a745;">✔️ {result['message']}</p>
                    <p>Your appointment has been marked as completed.</p>
                    <button class="btn-green" onclick="window.open('{doctors_urls['local']}', '_blank')"
                            style="margin-top: 10px;">
                        Return to Doctors Page (Local)
                    </button>
                    <button class="btn-purple" onclick="window.open('{doctors_urls['production']}', '_blank')"
                            style="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: #000; background: #fff; padding: 15px; border-radius: 5px; margin: 10px 0;'>
                        <h3 style="color: #ff9800;">⚠️ 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 class="btn-green" onclick="window.open('http://127.0.0.1:600/complete_appointment_manual?appointment_id={appointment_id.strip()}', '_blank')" style="margin-right: 10px;">
                                Complete Appointment
                            </button>
                            <button class="btn-purple" onclick="window.open('http://127.0.0.1:600/doctors', '_blank')" style="margin-right: 10px;">
                                Return to Doctors Page
                            </button>
                            <button class="btn-dark" onclick="navigator.clipboard.writeText('{appointment_id.strip()}')">
                                Copy Appointment ID
                            </button>
                        </div>
                    </div>
                    """
                else:
                    html_response = f"""
                    <div style='color: #000; background: #fff; padding: 15px; border-radius: 5px; margin: 10px 0;'>
                        <h3 style="color: #dc3545;">❌ 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]
        )

        # --- Client-side fallback: update the underlying input/textarea inside the Gradio container ---
        gr.HTML("""
        <script>
        function getUrlParameter(name) {
            name = name.replace(/[[]/, '\\[').replace(/[\]]/, '\\]');
            var regex = new RegExp('[\\?&]' + name + '=([^&#]*)');
            var results = regex.exec(window.location.search);
            return results === null ? '' : decodeURIComponent(results[1].replace(/\\+/g, ' '));
        }
        function setAppointmentIdFallback() {
            var appointmentId = getUrlParameter('appointment_id');
            var container = document.getElementById('appointment_id_input');
            if (!container || !appointmentId) return;
            var input = container.querySelector('input, textarea');
            if (!input && container.shadowRoot) {
                input = container.shadowRoot.querySelector('input, textarea');
            }
            if (input && input.value !== appointmentId) {
                input.value = appointmentId;
                input.dispatchEvent(new Event('input', { bubbles: true }));
                input.dispatchEvent(new Event('change', { bubbles: true }));
            }
        }
        // Try to apply once on load and occasionally afterward in case Gradio re-renders
        const fallbackInterval = setInterval(setAppointmentIdFallback, 1000);
        window.addEventListener('DOMContentLoaded', setAppointmentIdFallback);
        window.addEventListener('load', setAppointmentIdFallback);
        // Stop after some time to avoid running forever (10s)
        setTimeout(() => clearInterval(fallbackInterval), 10000);
        </script>
        """)

        # Server-side load event to populate appointment id reliably
        interface.load(
            _populate_appointment_id_on_load,
            inputs=None,
            outputs=appointment_id_input
        )

    interface.launch()

if __name__ == "__main__":
    create_interface()