import gradio as gr
import os
import re
import logging
import base64
from datetime import datetime
from PIL import Image
import html

from .patient_history import PatientHistoryManager, ReportGenerator

def pil_to_base64(pil_image):
    """Convert PIL Image to base64 data URL"""
    import io
    import base64
    from PIL import Image
    
    if pil_image is None:
        return None
        
    try:
        # Convert image to RGB if it's not already
        if pil_image.mode != 'RGB':
            pil_image = pil_image.convert('RGB')
            
        buffer = io.BytesIO()
        pil_image.save(buffer, format='PNG')
        img_str = base64.b64encode(buffer.getvalue()).decode()
        return f"data:image/png;base64,{img_str}"
    except Exception as e:
        logging.error(f"Error converting PIL image to base64: {e}")
        return None

class UIComponents:
    def __init__(self, auth_manager, database_manager, wound_analyzer):
        self.auth_manager = auth_manager
        self.database_manager = database_manager
        self.wound_analyzer = wound_analyzer
        self.current_user = {}
        self.patient_history_manager = PatientHistoryManager(database_manager)
        self.report_generator = ReportGenerator()
        
        # Ensure uploads directory exists
        if not os.path.exists("uploads"):
            os.makedirs("uploads", exist_ok=True)

    def image_to_base64(self, image_path):
        """Convert image to base64 data URL for embedding in HTML"""
        if not image_path or not os.path.exists(image_path):
            return None
            
        try:
            with open(image_path, "rb") as image_file:
                encoded_string = base64.b64encode(image_file.read()).decode()
                
            # Determine image format
            image_ext = os.path.splitext(image_path)[1].lower()
            if image_ext in [".jpg", ".jpeg"]:
                mime_type = "image/jpeg"
            elif image_ext == ".png":
                mime_type = "image/png"
            elif image_ext == ".gif":
                mime_type = "image/gif"
            else:
                mime_type = "image/png"  # Default to PNG
                
            return f"data:{mime_type};base64,{encoded_string}"
        except Exception as e:
            logging.error(f"Error converting image to base64: {e}")
            return None

    def markdown_to_html(self, markdown_text):
        """Convert markdown text to proper HTML format with enhanced support"""
        if not markdown_text:
            return ""
        
        # Escape HTML entities first to prevent issues with special characters
        html_text = html.escape(markdown_text)

        # Convert headers
        html_text = re.sub(r"^### (.*?)$", r"<h3>\1</h3>", html_text, flags=re.MULTILINE)
        html_text = re.sub(r"^## (.*?)$", r"<h2>\1</h2>", html_text, flags=re.MULTILINE)
        html_text = re.sub(r"^# (.*?)$", r"<h1>\1</h1>", html_text, flags=re.MULTILINE)
        
        # Convert bold text
        html_text = re.sub(r"\*\*(.*?)\*\*", r"<strong>\1</strong>", html_text)
        
        # Convert italic text
        html_text = re.sub(r"\*(.*?)\*", r"<em>\1</em>", html_text)

        # Convert code blocks (triple backticks)
        html_text = re.sub(r"```(.*?)```", r"<pre><code>\1</code></pre>", html_text, flags=re.DOTALL)
        # Convert inline code (single backticks)
        html_text = re.sub(r"`(.*?)`", r"<code>\1</code>", html_text)

        # Convert blockquotes
        html_text = re.sub(r"^> (.*?)$", r"<blockquote>\1</blockquote>", html_text, flags=re.MULTILINE)

        # Convert links
        html_text = re.sub(r"\[(.*?)\]\((.*?)\)", r"<a href=\"\2\">\1</a>", html_text)

        # Convert horizontal rules
        html_text = re.sub(r"^\s*[-*_]{3,}\s*$", r"<hr>", html_text, flags=re.MULTILINE)

        # Convert bullet points
        lines = html_text.split("\n")
        in_list = False
        result_lines = []
        
        for line in lines:
            stripped = line.strip()
            if stripped.startswith("- "):
                if not in_list:
                    result_lines.append("<ul>")
                    in_list = True
                result_lines.append(f"<li>{stripped[2:]}</li>")
            else:
                if in_list:
                    result_lines.append("</ul>")
                    in_list = False
                if stripped:
                    result_lines.append(f"<p>{stripped}</p>")
                else:
                    result_lines.append("<br>")
        
        if in_list:
            result_lines.append("</ul>")
        
        return "\n".join(result_lines)

    def get_organizations_dropdown(self):
        """Get list of organizations for dropdown"""
        try:
            organizations = self.database_manager.get_organizations()
            return [f"{org['org_name']} - {org['location']}" for org in organizations]
        except Exception as e:
            logging.error(f"Error getting organizations: {e}")
            return ["Default Hospital - Location"]

    def get_custom_css(self):
        return """
/* =================== SMARTHEAL CSS =================== */
/* Global Styling */
body, html {
    margin: 0 !important;
    padding: 0 !important;
    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu', 'Cantarell', sans-serif !important;
    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%) !important;
    color: #1A202C !important;
    line-height: 1.6 !important;
}

/* Professional Header with Logo */
.medical-header {
    background: linear-gradient(135deg, #3182ce 0%, #2c5aa0 100%) !important;
    color: white !important;
    padding: 32px 40px !important;
    border-radius: 20px 20px 0 0 !important;
    display: flex !important;
    align-items: center !important;
    justify-content: center !important;
    margin-bottom: 0 !important;
    box-shadow: 0 10px 40px rgba(49, 130, 206, 0.3) !important;
    border: none !important;
    position: relative !important;
    overflow: hidden !important;
}

.logo {
    width: 80px !important;
    height: 80px !important;
    border-radius: 50% !important;
    margin-right: 24px !important;
    border: 4px solid rgba(255, 255, 255, 0.3) !important;
    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.2) !important;
    background: white !important;
    padding: 4px !important;
}

.medical-header h1 {
    font-size: 3.5rem !important;
    font-weight: 800 !important;
    margin: 0 !important;
    text-shadow: 2px 2px 8px rgba(0, 0, 0, 0.3) !important;
    background: linear-gradient(45deg, #ffffff, #f8f9fa) !important;
    -webkit-background-clip: text !important;
    -webkit-text-fill-color: transparent !important;
    background-clip: text !important;
    filter: drop-shadow(2px 2px 4px rgba(0, 0, 0, 0.3)) !important;
}

.medical-header p {
    font-size: 1.3rem !important;
    margin: 8px 0 0 0 !important;
    opacity: 0.95 !important;
    font-weight: 500 !important;
    text-shadow: 1px 1px 4px rgba(0, 0, 0, 0.2) !important;
}

/* Enhanced Form Styling */
.gr-form {
    background: linear-gradient(145deg, #ffffff 0%, #f8f9fa 100%) !important;
    border-radius: 20px !important;
    padding: 32px !important;
    margin: 24px 0 !important;
    box-shadow: 0 16px 48px rgba(0, 0, 0, 0.1) !important;
    border: 1px solid rgba(229, 62, 62, 0.1) !important;
    backdrop-filter: blur(10px) !important;
    position: relative !important;
    overflow: hidden !important;
}

/* Professional Input Fields */
.gr-textbox, .gr-number {
    border-radius: 12px !important;
    border: 2px solid #E2E8F0 !important;
    background: #FFFFFF !important;
    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important;
    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.05) !important;
    font-size: 1rem !important;
    color: #1A202C !important;
    padding: 16px 20px !important;
}

.gr-textbox:focus, .gr-number:focus, .gr-textbox input:focus, .gr-number input:focus {
    border-color: #E53E3E !important;
    box-shadow: 0 0 0 4px rgba(229, 62, 62, 0.1) !important;
    background: #FFFFFF !important;
    outline: none !important;
    transform: translateY(-1px) !important;
}

/* Enhanced Button Styling */
button.gr-button, button.gr-button-primary {
    background: linear-gradient(135deg, #E53E3E 0%, #C53030 100%) !important;
    color: #FFFFFF !important;
    border: none !important;
    border-radius: 12px !important;
    font-weight: 700 !important;
    padding: 16px 32px !important;
    font-size: 1.1rem !important;
    letter-spacing: 0.5px !important;
    text-align: center !important;
    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important;
    box-shadow: 0 4px 16px rgba(229, 62, 62, 0.3) !important;
    position: relative !important;
    overflow: hidden !important;
    text-transform: uppercase !important;
    cursor: pointer !important;
}

button.gr-button:hover, button.gr-button-primary:hover {
    background: linear-gradient(135deg, #C53030 0%, #9C2A2A 100%) !important;
    box-shadow: 0 8px 32px rgba(229, 62, 62, 0.4) !important;
    transform: translateY(-3px) !important;
}

/* Professional Status Messages */
.status-success {
    background: linear-gradient(135deg, #F0FFF4 0%, #E6FFFA 100%) !important;
    border: 2px solid #38A169 !important;
    color: #22543D !important;
    padding: 20px 24px !important;
    border-radius: 16px !important;
    font-weight: 600 !important;
    margin: 16px 0 !important;
    box-shadow: 0 8px 24px rgba(56, 161, 105, 0.2) !important;
    backdrop-filter: blur(10px) !important;
}

.status-error {
    background: linear-gradient(135deg, #FFF5F5 0%, #FED7D7 100%) !important;
    border: 2px solid #E53E3E !important;
    color: #742A2A !important;
    padding: 20px 24px !important;
    border-radius: 16px !important;
    font-weight: 600 !important;
    margin: 16px 0 !important;
    box-shadow: 0 8px 24px rgba(229, 62, 62, 0.2) !important;
    backdrop-filter: blur(10px) !important;
}

.status-warning {
    background: linear-gradient(135deg, #FFFAF0 0%, #FEEBC8 100%) !important;
    border: 2px solid #DD6B20 !important;
    color: #9C4221 !important;
    padding: 20px 24px !important;
    border-radius: 16px !important;
    font-weight: 600 !important;
    margin: 16px 0 !important;
    box-shadow: 0 8px 24px rgba(221, 107, 32, 0.2) !important;
    backdrop-filter: blur(10px) !important;
}

/* Image gallery styling for better visualization */
.image-gallery {
    display: grid;
    grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
    gap: 20px;
    margin: 20px 0;
}

.image-item {
    background: #f8f9fa;
    border-radius: 12px;
    padding: 15px;
    box-shadow: 0 4px 12px rgba(0,0,0,0.1);
    text-align: center;
}

.image-item img {
    max-width: 100%;
    height: auto;
    border-radius: 8px;
    box-shadow: 0 2px 8px rgba(0,0,0,0.15);
}

.image-item h4 {
    margin: 15px 0 5px 0;
    color: #2d3748;
    font-weight: 600;
}

.image-item p {
    margin: 0;
    color: #666;
    font-size: 0.9em;
}

/* Analyze button special styling */
#analyze-btn {
    background: linear-gradient(135deg, #1B5CF3 0%, #1E3A8A 100%) !important;
    color: #FFFFFF !important;
    border: none !important;
    border-radius: 8px !important;
    font-weight: 700 !important;
    padding: 14px 28px !important;
    font-size: 1.1rem !important;
    letter-spacing: 0.5px !important;
    text-align: center !important;
    transition: all 0.2s ease-in-out !important;
}

#analyze-btn:hover {
    background: linear-gradient(135deg, #174ea6 0%, #123b82 100%) !important;
    box-shadow: 0 4px 14px rgba(27, 95, 193, 0.4) !important;
    transform: translateY(-2px) !important;
}

/* Responsive design */
@media (max-width: 768px) {
    .medical-header {
        padding: 16px !important;
        text-align: center !important;
    }
    
    .medical-header h1 {
        font-size: 2rem !important;
    }
    
    .logo {
        width: 48px !important;
        height: 48px !important;
        margin-right: 16px !important;
    }
    
    .gr-form {
        padding: 16px !important;
        margin: 8px 0 !important;
    }
    
    .image-gallery {
        grid-template-columns: 1fr;
    }
}
"""

    def create_interface(self):
        """Create the main Gradio interface with comprehensive analysis display"""
        with gr.Blocks(css=self.get_custom_css(), title="SmartHeal - AI Wound Care Assistant") as app:
            
            # Header with SmartHeal logo
            logo_url = "https://scontent.fccu31-2.fna.fbcdn.net/v/t39.30808-6/275933824_102121829111657_3325198727201325354_n.jpg?_nc_cat=104&ccb=1-7&_nc_sid=6ee11a&_nc_ohc=45krrEUpcSUQ7kNvwGVdiMW&_nc_oc=AdkTdxEC_TkYGiyDkEtTJZ_DFZELW17XKFmWpswmFqGB7JSdvTyWtnrQyLS0USngEiY&_nc_zt=23&_nc_ht=scontent.fccu31-2.fna&_nc_gid=ufAA4Hj5gTRwON5POYzz0Q&oh=00_AfW1-jLEN5RGeggqOvGgEaK_gdg0EDgxf_VhKbZwFLUO0Q&oe=6897A98B"
            
            gr.HTML(f"""
            <div class="medical-header">
                <img src="{logo_url}" class="logo" alt="SmartHeal Logo">
                <div>
                    <h1>SmartHeal AI</h1>
                    <p>Advanced Wound Care Analysis & Clinical Support System</p>
                </div>
            </div>
            """)
            
            # Professional disclaimer
            gr.HTML("""
        <div style="border: 2px solid #FF6B6B; background-color: #FFE5E5; padding: 15px; border-radius: 12px; margin: 10px 0;">
            <h3 style="color: #D63031; margin-top: 0;">⚠️ IMPORTANT DISCLAIMER</h3>
            <p><strong>This model is for testing and educational purposes only and is NOT a replacement for professional medical advice.</strong></p>
            <p>Information generated may be inaccurate. Always consult a qualified healthcare provider for medical concerns. This AI system uses chain-of-thought reasoning to show its decision-making process, but should never be used as the sole basis for clinical decisions.</p>
            <p><em>Uploaded images may be stored and used for testing and model improvement purposes.</em></p>
        </div>
        """)
            
            # Main interface with conditional visibility
            with gr.Row():
                # Authentication Panel (visible when not logged in)
                with gr.Column(visible=True) as auth_panel:
                    gr.HTML("""
                    <div style="text-align: center; margin: 40px 0;">
                        <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 40px; border-radius: 20px; box-shadow: 0 20px 40px rgba(0,0,0,0.1); max-width: 500px; margin: 0 auto;">
                            <h2 style="color: white; font-size: 2.5rem; margin-bottom: 10px; font-weight: 700;">🏥 SmartHeal Access</h2>
                            <p style="color: rgba(255,255,255,0.9); font-size: 1.1rem; margin-bottom: 30px;">Secure Healthcare Professional Portal</p>
                        </div>
                    </div>
                    """)
                    
                    with gr.Tabs():
                        with gr.Tab("🔐 Professional Login") as login_tab:
                            gr.HTML("""
                            <div style="background: white; padding: 40px; border-radius: 16px; box-shadow: 0 8px 32px rgba(0,0,0,0.1); margin: 20px auto; max-width: 450px;">
                                <div style="text-align: center; margin-bottom: 30px;">
                                    <h3 style="color: #2d3748; font-size: 1.8rem; margin-bottom: 8px;">Welcome Back</h3>
                                    <p style="color: #718096; font-size: 1rem;">Access your professional dashboard</p>
                                </div>
                            </div>
                            """)
                            
                            login_username = gr.Textbox(
                                label="👤 Username", 
                                placeholder="Enter your username"
                            )
                            login_password = gr.Textbox(
                                label="🔒 Password", 
                                type="password", 
                                placeholder="Enter your secure password"
                            )
                            
                            login_btn = gr.Button(
                                "🚀 Sign In to Dashboard", 
                                variant="primary", 
                                size="lg"
                            )
                            
                            login_status = gr.HTML(
                                value="<div style='text-align: center; color: #718096; font-size: 0.9rem; margin-top: 15px;'>Enter your credentials to access the system</div>"
                            )

                        with gr.Tab("📝 New Registration") as signup_tab:
                            gr.HTML("""
                            <div style="background: white; padding: 40px; border-radius: 16px; box-shadow: 0 8px 32px rgba(0,0,0,0.1); margin: 20px auto; max-width: 450px;">
                                <div style="text-align: center; margin-bottom: 30px;">
                                    <h3 style="color: #2d3748; font-size: 1.8rem; margin-bottom: 8px;">Create Account</h3>
                                    <p style="color: #718096; font-size: 1rem;">Join the SmartHeal healthcare network</p>
                                </div>
                            </div>
                            """)
                            
                            signup_username = gr.Textbox(
                                label="👤 Username", 
                                placeholder="Choose a unique username"
                            )
                            signup_email = gr.Textbox(
                                label="📧 Email Address", 
                                placeholder="Enter your professional email"
                            )
                            signup_password = gr.Textbox(
                                label="🔒 Password", 
                                type="password", 
                                placeholder="Create a strong password"
                            )
                            signup_name = gr.Textbox(
                                label="👨‍⚕️ Full Name", 
                                placeholder="Enter your full professional name"
                            )
                            signup_role = gr.Radio(
                                ["practitioner", "organization"], 
                                label="🏥 Account Type", 
                                value="practitioner"
                            )
                            
                            # Organization-specific fields
                            with gr.Group(visible=False) as org_fields:
                                gr.HTML("<h4 style='color: #2d3748; margin: 20px 0 10px 0;'>🏢 Organization Details</h4>")
                                org_name = gr.Textbox(label="Organization Name", placeholder="Enter organization name")
                                phone = gr.Textbox(label="Phone Number", placeholder="Enter contact number")
                                country_code = gr.Textbox(label="Country Code", placeholder="e.g., +1, +44")
                                department = gr.Textbox(label="Department", placeholder="e.g., Emergency, Surgery")
                                location = gr.Textbox(label="Location", placeholder="City, State/Province, Country")
                            
                            # Practitioner-specific fields
                            with gr.Group(visible=True) as prac_fields:
                                gr.HTML("<h4 style='color: #2d3748; margin: 20px 0 10px 0;'>🏥 Affiliation</h4>")
                                organization_dropdown = gr.Dropdown(
                                    choices=self.get_organizations_dropdown(), 
                                    label="Select Your Organization"
                                )
                            
                            signup_btn = gr.Button(
                                "✨ Create Professional Account", 
                                variant="primary", 
                                size="lg"
                            )
                            
                            signup_status = gr.HTML(
                                value="<div style='text-align: center; color: #718096; font-size: 0.9rem; margin-top: 15px;'>Fill in your details to create an account</div>"
                            )
                
                # Practitioner Interface (hidden initially)
                with gr.Column(visible=False) as practitioner_panel:
                    gr.HTML('<div class="medical-card-title">👩‍⚕️ Practitioner Dashboard</div>')
                    
                    user_info = gr.HTML("")
                    logout_btn_prac = gr.Button("🚪 Logout", variant="secondary")
                    
                    # Main tabs for different functions
                    with gr.Tabs():
                        # WOUND ANALYSIS TAB
                        with gr.Tab("🔬 Wound Analysis"):
                            with gr.Row():
                                with gr.Column(scale=1):
                                    gr.HTML("<h3>📋 Patient Information</h3>")
                                    patient_name = gr.Textbox(label="Patient Name", placeholder="Enter patient's full name")
                                    patient_age = gr.Number(label="Age", value=30, minimum=0, maximum=120)
                                    patient_gender = gr.Dropdown(
                                        choices=["Male", "Female", "Other"], 
                                        label="Gender", 
                                        value="Male"
                                    )
                                    
                                    gr.HTML("<h3>🩹 Wound Information</h3>")
                                    wound_location = gr.Textbox(label="Wound Location", placeholder="e.g., Left ankle, Right arm")
                                    wound_duration = gr.Textbox(label="Wound Duration", placeholder="e.g., 2 weeks, 1 month")
                                    pain_level = gr.Slider(
                                        minimum=0, maximum=10, value=5, step=1,
                                        label="Pain Level (0-10)"
                                    )
                                    
                                    gr.HTML("<h3>⚕️ Clinical Assessment</h3>")
                                    moisture_level = gr.Dropdown(
                                        choices=["Dry", "Moist", "Wet", "Saturated"], 
                                        label="Moisture Level",
                                        value="Moist"
                                    )
                                    infection_signs = gr.Dropdown(
                                        choices=["None", "Mild", "Moderate", "Severe"],
                                        label="Signs of Infection",
                                        value="None"
                                    )
                                    diabetic_status = gr.Dropdown(
                                        choices=["Non-diabetic", "Type 1", "Type 2", "Gestational"], 
                                        label="Diabetic Status",
                                        value="Non-diabetic"
                                    )
                                
                                with gr.Column(scale=1):
                                    gr.HTML("<h3>📸 Wound Image Upload</h3>")
                                    wound_image = gr.Image(
                                        label="Upload Wound Image",
                                        type="filepath"
                                    )
                                    
                                    gr.HTML("<h3>📝 Medical History</h3>")
                                    previous_treatment = gr.Textbox(
                                        label="Previous Treatment",
                                        placeholder="Describe any previous treatments...",
                                        lines=3
                                    )
                                    medical_history = gr.Textbox(
                                        label="Medical History",
                                        placeholder="Relevant medical conditions, surgeries, etc...",
                                        lines=3
                                    )
                                    medications = gr.Textbox(
                                        label="Current Medications",
                                        placeholder="List current medications...",
                                        lines=2
                                    )
                                    allergies = gr.Textbox(
                                        label="Known Allergies",
                                        placeholder="List any known allergies...",
                                        lines=2
                                    )
                                    additional_notes = gr.Textbox(
                                        label="Additional Notes",
                                        placeholder="Any additional clinical observations...",
                                        lines=3
                                    )
                            
                            analyze_btn = gr.Button("🔬 Analyze Wound", variant="primary", size="lg", elem_id="analyze-btn")
                            analysis_output = gr.HTML("")
                        
                        # PATIENT HISTORY TAB
                        with gr.Tab("📋 Patient History"):
                            with gr.Row():
                                with gr.Column(scale=2):
                                    gr.HTML("<h3>📊 Patient History Dashboard</h3>")
                                    history_btn = gr.Button("📋 Load Patient History", variant="primary")
                                    patient_history_output = gr.HTML("")
                                
                                with gr.Column(scale=1):
                                    gr.HTML("<h3>🔍 Search Specific Patient</h3>")
                                    search_patient_name = gr.Textbox(
                                        label="Patient Name", 
                                        placeholder="Enter patient name to search..."
                                    )
                                    search_patient_btn = gr.Button("🔍 Search Patient History", variant="secondary")
                                    specific_patient_output = gr.HTML("")
            
            # Event handlers
            def handle_login(username, password):
                user_data = self.auth_manager.authenticate_user(username, password)
                if user_data:
                    self.current_user = user_data
                    return {
                        auth_panel: gr.update(visible=False),
                        practitioner_panel: gr.update(visible=True),
                        login_status: "<div class='status-success'>✅ Login successful! Welcome to SmartHeal</div>"
                    }
                else:
                    return {
                        login_status: "<div class='status-error'>❌ Invalid credentials. Please try again.</div>"
                    }
            
            def handle_signup(username, email, password, name, role, org_name, phone, country_code, department, location, organization_dropdown):
                try:
                    if role == "organization":
                        org_data = {
                            'org_name': org_name,
                            'phone': phone,
                            'country_code': country_code,
                            'department': department,
                            'location': location
                        }
                        org_id = self.database_manager.create_organization(org_data)
                        
                        user_data = {
                            'username': username,
                            'email': email,
                            'password': password,
                            'name': name,
                            'role': role,
                            'org_id': org_id
                        }
                    else:
                        # Extract org_id from dropdown selection
                        org_id = 1  # Default organization for now
                        user_data = {
                            'username': username,
                            'email': email,
                            'password': password,
                            'name': name,
                            'role': role,
                            'org_id': org_id
                        }
                    
                    if self.auth_manager.create_user(user_data):
                        return {
                            signup_status: "<div class='status-success'>✅ Account created successfully! Please login.</div>"
                        }
                    else:
                        return {
                            signup_status: "<div class='status-error'>❌ Failed to create account. Username or email may already exist.</div>"
                        }
                except Exception as e:
                    return {
                        signup_status: f"<div class='status-error'>❌ Error: {str(e)}</div>"
                    }
            
            def handle_analysis(patient_name, patient_age, patient_gender, wound_location, wound_duration, 
                    pain_level, moisture_level, infection_signs, diabetic_status, previous_treatment,
                    medical_history, medications, allergies, additional_notes, wound_image):
                try:
                    if not wound_image:
                        return "<div class='status-error'>❌ Please upload a wound image for analysis.</div>"
                    
                    # Show loading state first
                    loading_html = """
                    <div style="text-align:center; padding: 40px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 16px; color: white; margin: 20px 0;">
                        <div style="display:inline-block; border:4px solid rgba(255,255,255,0.3); border-radius:50%; border-top-color:white; width:50px; height:50px; animation:spin 1s linear infinite; margin-bottom: 20px;"></div>
                        <h3 style="margin: 0; font-size: 1.5rem;">🔬 SmartHeal AI Processing</h3>
                        <p style="margin: 10px 0 0 0; font-size: 1rem; opacity: 0.9;">Analyzing wound image with advanced computer vision...</p>
                        <div style="background: rgba(255,255,255,0.1); border-radius: 8px; padding: 10px; margin-top: 15px;">
                            <p style="margin: 0; font-size: 0.9rem;">⚡ Detection → 📏 Segmentation → 🤖 AI Report</p>
                        </div>
                        <style>@keyframes spin {0% {transform:rotate(0deg)} 100% {transform:rotate(360deg)}}</style>
                    </div>
                    """
                    
                    # 1. Save questionnaire FIRST to get valid ID
                    questionnaire_data_for_db = {
                        'user_id': self.current_user.get('id', 1),  # Default to 1 if no user logged in
                        'patient_name': patient_name,
                        'patient_age': patient_age,
                        'patient_gender': patient_gender,
                        'wound_location': wound_location,
                        'wound_duration': wound_duration,
                        'pain_level': pain_level,
                        'moisture_level': moisture_level,
                        'infection_signs': infection_signs,
                        'diabetic_status': diabetic_status,
                        'previous_treatment': previous_treatment,
                        'medical_history': medical_history,
                        'medications': medications,
                        'allergies': allergies,
                        'additional_notes': additional_notes
                    }
                    
                    # Save questionnaire FIRST to get the ID
                    questionnaire_response_id = self.database_manager.save_questionnaire(questionnaire_data_for_db)
                    logging.info(f"✅ Questionnaire saved with response ID: {questionnaire_response_id}")
                    
                    # 2. Create AIProcessor questionnaire format  
                    questionnaire_data_for_ai = {
                        'age': patient_age,
                        'diabetic': 'Yes' if diabetic_status != 'Non-diabetic' else 'No',
                        'allergies': allergies,
                        'date_of_injury': 'Unknown',  # Not collected in this form
                        'professional_care': 'Yes',  # Assumed since using professional interface
                        'oozing_bleeding': 'Minor Oozing' if infection_signs != 'None' else 'None',
                        'infection': 'Yes' if infection_signs != 'None' else 'No',
                        'moisture': moisture_level,
                        # Additional comprehensive fields
                        'patient_name': patient_name,
                        'patient_gender': patient_gender,
                        'wound_location': wound_location,
                        'wound_duration': wound_duration,
                        'pain_level': pain_level,
                        'previous_treatment': previous_treatment,
                        'medical_history': medical_history,
                        'medications': medications,
                        'additional_notes': additional_notes
                    }
            
                    # 3. Run AI analysis using the AIProcessor class
                    try:
                        logging.info(f"🔬 Starting AI analysis for image: {wound_image}")
                        
                        # Use the AIProcessor analyze_wound method
                        analysis_result = self.wound_analyzer.analyze_wound(wound_image, questionnaire_data_for_ai)
                        
                        if not analysis_result.get('success', False):
                            error_msg = analysis_result.get('error', 'Analysis failed for unknown reason')
                            logging.error(f"❌ AI Analysis failed: {error_msg}")
                            
                            return f"""
                            <div class='status-error' style='padding: 30px; background: #fff5f5; border-left: 5px solid #e53e3e; margin: 20px 0; border-radius: 12px;'>
                                <h3 style="color: #c53030; margin-top: 0;">❌ AI Analysis Error</h3>
                                <p style="color: #742a2a; font-size: 1.1rem;"><strong>Error Details:</strong></p>
                                <div style='background: #fed7d7; padding: 15px; border-radius: 8px; margin: 10px 0;'>
                                    <code style='color: #742a2a; font-size: 0.9rem;'>{error_msg}</code>
                                </div>
                                <p style="color: #742a2a;">Please try again with a different image or contact technical support.</p>
                                <div style="margin-top: 15px; padding: 15px; background: #fff; border-radius: 8px; border: 1px solid #feb2b2;">
                                    <strong>Troubleshooting Tips:</strong>
                                    <ul style="margin: 10px 0; color: #742a2a;">
                                        <li>Ensure image is clear and well-lit</li>
                                        <li>Wound should be clearly visible in the image</li>
                                        <li>Try a different image format (PNG, JPG)</li>
                                        <li>Check image file size (max 10MB recommended)</li>
                                    </ul>
                                </div>
                            </div>
                            """
                        
                        logging.info("✅ AI Analysis completed successfully")
                        
                        # 4. Save analysis result with valid questionnaire_response_id
                        try:
                            if questionnaire_response_id:
                                self.database_manager.save_analysis_result(questionnaire_response_id, analysis_result)
                                logging.info("✅ Analysis result saved to database")
                        except Exception as db_error:
                            logging.error(f"❌ Database save error: {db_error}")
                            # Continue with display even if DB save fails
                        
                        # 5. Format comprehensive analysis results with all images
                        formatted_analysis = self._format_comprehensive_analysis_results(
                            analysis_result, wound_image, questionnaire_data_for_ai
                        )
                        
                        return formatted_analysis
            
                    except Exception as analysis_error:
                        logging.error(f"❌ AI analysis exception: {analysis_error}", exc_info=True)
                        return f"""
                        <div class='status-error' style='padding: 30px; background: #fff5f5; border-left: 5px solid #e53e3e; margin: 20px 0; border-radius: 12px;'>
                            <h3 style="color: #c53030; margin-top: 0;">❌ Analysis Processing Error</h3>
                            <p style="color: #742a2a;">There was an unexpected error during wound analysis:</p>
                            <div style='background: #fed7d7; padding: 15px; border-radius: 8px; margin: 15px 0;'>
                                <code style='color: #742a2a; font-size: 0.9rem; word-break: break-word;'>{str(analysis_error)}</code>
                            </div>
                            <p style="color: #742a2a;"><strong>Next Steps:</strong></p>
                            <ul style="color: #742a2a; margin: 10px 0;">
                                <li>Refresh the page and try again</li>
                                <li>Check your internet connection</li>
                                <li>Try with a different wound image</li>
                                <li>Contact system administrator if the problem persists</li>
                            </ul>
                        </div>
                        """
            
                except Exception as e:
                    logging.error(f"❌ Analysis handler error: {e}", exc_info=True)
                    return f"""
                    <div class='status-error' style='padding: 30px; background: #fff5f5; border-left: 5px solid #e53e3e; margin: 20px 0; border-radius: 12px;'>
                        <h3 style="color: #c53030; margin-top: 0;">❌ System Error</h3>
                        <p style="color: #742a2a;">A system error occurred while processing your request:</p>
                        <div style='background: #fed7d7; padding: 15px; border-radius: 8px; margin: 15px 0;'>
                            <code style='color: #742a2a; font-size: 0.9rem;'>{str(e)}</code>
                        </div>
                        <p style="color: #742a2a;">Please contact technical support if this issue continues.</p>
                    </div>
                    """

            def handle_logout():
                self.current_user = {}
                return {
                    auth_panel: gr.update(visible=True),
                    practitioner_panel: gr.update(visible=False)
                }
            
            def toggle_role_fields(role):
                if role == "organization":
                    return {
                        org_fields: gr.update(visible=True),
                        prac_fields: gr.update(visible=False)
                    }
                else:
                    return {
                        org_fields: gr.update(visible=False),
                        prac_fields: gr.update(visible=True)
                    }
            
            def load_patient_history():
                try:
                    user_id = self.current_user.get('id', 1)
                    if not user_id:
                        return "<div class='status-error'>❌ Please login first.</div>"
                    
                    history_data = self.patient_history_manager.get_user_patient_history(user_id)
                    formatted_history = self.patient_history_manager.format_history_for_display(history_data)
                    return formatted_history
                except Exception as e:
                    logging.error(f"Error loading patient history: {e}")
                    return f"<div class='status-error'>❌ Error loading history: {str(e)}</div>"
            
            def search_specific_patient(patient_name):
                try:
                    user_id = self.current_user.get('id', 1)
                    if not user_id:
                        return "<div class='status-error'>❌ Please login first.</div>"
                    
                    if not patient_name.strip():
                        return "<div class='status-warning'>⚠️ Please enter a patient name to search.</div>"
                    
                    patient_data = self.patient_history_manager.search_patient_by_name(user_id, patient_name.strip())
                    if patient_data:
                        formatted_data = self.patient_history_manager.format_patient_data_for_display(patient_data)
                        return formatted_data
                    else:
                        return f"<div class='status-warning'>⚠️ No records found for patient: {patient_name}</div>"
                        
                except Exception as e:
                    logging.error(f"Error searching patient: {e}")
                    return f"<div class='status-error'>❌ Error searching patient: {str(e)}</div>"
            
            # Bind event handlers
            login_btn.click(
                handle_login,
                inputs=[login_username, login_password],
                outputs=[auth_panel, practitioner_panel, login_status]
            )
            
            signup_btn.click(
                handle_signup,
                inputs=[signup_username, signup_email, signup_password, signup_name, signup_role, 
                       org_name, phone, country_code, department, location, organization_dropdown],
                outputs=[signup_status]
            )
            
            signup_role.change(
                toggle_role_fields,
                inputs=[signup_role],
                outputs=[org_fields, prac_fields]
            )
            
            analyze_btn.click(
                handle_analysis,
                inputs=[patient_name, patient_age, patient_gender, wound_location, wound_duration,
                       pain_level, moisture_level, infection_signs, diabetic_status, previous_treatment,
                       medical_history, medications, allergies, additional_notes, wound_image],
                outputs=[analysis_output]
            )
            
            logout_btn_prac.click(
                handle_logout,
                outputs=[auth_panel, practitioner_panel]
            )
            
            history_btn.click(
                load_patient_history,
                outputs=[patient_history_output]
            )
            
            search_patient_btn.click(
                search_specific_patient,
                inputs=[search_patient_name],
                outputs=[specific_patient_output]
            )
        
        return app   
    
    def _format_comprehensive_analysis_results(self, analysis_result, image_url=None, questionnaire_data=None):
        """Format comprehensive analysis results with all visualization images from AIProcessor."""
        try:
            # Extract the core analysis results from AIProcessor
            success = analysis_result.get('success', False)
            if not success:
                error_msg = analysis_result.get('error', 'Unknown error')
                return f"<div class='status-error'>❌ Analysis failed: {error_msg}</div>"
            
            visual_analysis = analysis_result.get('visual_analysis', {})
            report = analysis_result.get('report', '')
            saved_image_path = analysis_result.get('saved_image_path', '')
            
            # Extract wound metrics
            wound_type = visual_analysis.get('wound_type', 'Unknown')
            length_cm = visual_analysis.get('length_cm', 0)
            breadth_cm = visual_analysis.get('breadth_cm', 0)
            area_cm2 = visual_analysis.get('surface_area_cm2', 0)
            detection_confidence = visual_analysis.get('detection_confidence', 0)
            
            # Get image paths for visualizations
            detection_image_path = visual_analysis.get('detection_image_path', '')
            segmentation_image_path = visual_analysis.get('segmentation_image_path', '')
            original_image_path = visual_analysis.get('original_image_path', '')
            
            # Convert images to base64 for embedding
            original_image_base64 = None
            detection_image_base64 = None
            segmentation_image_base64 = None
            
            # Original uploaded image
            if image_url and os.path.exists(image_url):
                original_image_base64 = self.image_to_base64(image_url)
            elif original_image_path and os.path.exists(original_image_path):
                original_image_base64 = self.image_to_base64(original_image_path)
            elif saved_image_path and os.path.exists(saved_image_path):
                original_image_base64 = self.image_to_base64(saved_image_path)
            
            # Detection visualization
            if detection_image_path and os.path.exists(detection_image_path):
                detection_image_base64 = self.image_to_base64(detection_image_path)
            
            # Segmentation visualization  
            if segmentation_image_path and os.path.exists(segmentation_image_path):
                segmentation_image_base64 = self.image_to_base64(segmentation_image_path)
            
            # Generate risk assessment from questionnaire data
            risk_assessment = self._generate_risk_assessment(questionnaire_data)
            risk_level = risk_assessment['risk_level']
            risk_score = risk_assessment['risk_score']
            risk_factors = risk_assessment['risk_factors']
            
            # Set risk class for styling
            risk_class = "low"
            if risk_level.lower() == "moderate":
                risk_class = "moderate"
            elif risk_level.lower() in ["high", "very high"]:
                risk_class = "high"
            
            # Format risk factors
            risk_factors_html = "<ul>" + "".join(f"<li>{factor}</li>" for factor in risk_factors) + "</ul>" if risk_factors else "<p>No specific risk factors identified.</p>"
            
            # Create image gallery
            image_gallery_html = ""
            if original_image_base64 or detection_image_base64 or segmentation_image_base64:
                image_gallery_html = '<div class="image-gallery">'
                
                if original_image_base64:
                    image_gallery_html += f'''
                    <div class="image-item">
                        <img src="{original_image_base64}" alt="Original Wound Image">
                        <h4>📸 Original Wound Image</h4>
                        <p>Uploaded image for analysis</p>
                    </div>
                    '''
                
                if detection_image_base64:
                    image_gallery_html += f'''
                    <div class="image-item">
                        <img src="{detection_image_base64}" alt="Wound Detection">
                        <h4>🎯 Wound Detection</h4>
                        <p>AI-detected wound boundaries with {detection_confidence:.1%} confidence</p>
                    </div>
                    '''
                
                if segmentation_image_base64:
                    image_gallery_html += f'''
                    <div class="image-item">
                        <img src="{segmentation_image_base64}" alt="Wound Segmentation">
                        <h4>📏 Wound Segmentation</h4>
                        <p>Detailed wound area measurement and analysis</p>
                    </div>
                    '''
                
                image_gallery_html += '</div>'
            
            # Convert markdown report to HTML
            report_html = ""
            if report:
                report_html = self.markdown_to_html(report)
            
            # Final comprehensive HTML output
            html_output = f"""
            <div style="max-width: 1200px; margin: 0 auto; background: white; border-radius: 16px; box-shadow: 0 8px 32px rgba(0,0,0,0.1); overflow: hidden;">
                <div style="background: linear-gradient(135deg, #3182ce 0%, #2c5aa0 100%); color: white; padding: 40px; text-align: center;">
                    <h1 style="margin: 0; font-size: 32px; font-weight: 700;">🔬 SmartHeal AI Comprehensive Analysis</h1>
                    <p style="margin: 15px 0 0 0; opacity: 0.9; font-size: 18px;">Advanced Computer Vision & Medical AI Assessment</p>
                    <div style="background: rgba(255,255,255,0.1); padding: 15px; border-radius: 8px; margin-top: 20px;">
                        <p style="margin: 0; font-size: 16px;"><strong>Patient:</strong> {questionnaire_data.get('patient_name', 'Unknown')} | <strong>Analysis Date:</strong> {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
                    </div>
                </div>
                
                <div style="padding: 40px;">
                    <div class="status-success" style="margin-bottom: 30px;">
                        <strong>✅ Analysis Status:</strong> Analysis completed successfully with comprehensive wound assessment
                    </div>
                    
                    <!-- Image Gallery Section -->
                    <div style="margin-bottom: 40px;">
                        <h2 style="color: #2d3748; font-size: 24px; margin-bottom: 20px; border-bottom: 2px solid #e53e3e; padding-bottom: 10px;">🖼️ Visual Analysis Gallery</h2>
                        {image_gallery_html}
                    </div>
                    
                    <!-- Wound Detection & Classification -->
                    <div style="background: #f8f9fa; padding: 30px; border-radius: 12px; margin-bottom: 30px;">
                        <h2 style="color: #2d3748; margin-top: 0;">🔍 Wound Detection & Classification</h2>
                        <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 20px; margin: 20px 0;">
                            <div style="background: white; padding: 20px; border-radius: 8px; text-align: center; box-shadow: 0 2px 8px rgba(0,0,0,0.1);">
                                <h3 style="color: #3182ce; margin: 0 0 10px 0;">Wound Type</h3>
                                <p style="font-weight: 600; font-size: 18px; color: #2d3748; margin: 0;">{wound_type}</p>
                            </div>
                            <div style="background: white; padding: 20px; border-radius: 8px; text-align: center; box-shadow: 0 2px 8px rgba(0,0,0,0.1);">
                                <h3 style="color: #3182ce; margin: 0 0 10px 0;">Detection Confidence</h3>
                                <p style="font-weight: 600; font-size: 18px; color: #2d3748; margin: 0;">{detection_confidence:.1%}</p>
                            </div>
                            <div style="background: white; padding: 20px; border-radius: 8px; text-align: center; box-shadow: 0 2px 8px rgba(0,0,0,0.1);">
                                <h3 style="color: #3182ce; margin: 0 0 10px 0;">Location</h3>
                                <p style="font-weight: 600; font-size: 18px; color: #2d3748; margin: 0;">{questionnaire_data.get('wound_location', 'Not specified')}</p>
                            </div>
                        </div>
                    </div>
                    
                    <!-- Wound Measurements -->
                    <div style="background: #e7f5ff; padding: 30px; border-radius: 12px; margin-bottom: 30px;">
                        <h2 style="color: #2d3748; margin-top: 0;">📏 Wound Measurements</h2>
                        <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); gap: 20px; margin: 20px 0;">
                            <div style="background: white; padding: 20px; border-radius: 8px; text-align: center; box-shadow: 0 2px 8px rgba(0,0,0,0.1);">
                                <h3 style="color: #e53e3e; margin: 0 0 10px 0;">Length</h3>
                                <p style="font-weight: 700; font-size: 24px; color: #2d3748; margin: 0;">{length_cm:.2f} cm</p>
                            </div>
                            <div style="background: white; padding: 20px; border-radius: 8px; text-align: center; box-shadow: 0 2px 8px rgba(0,0,0,0.1);">
                                <h3 style="color: #e53e3e; margin: 0 0 10px 0;">Width</h3>
                                <p style="font-weight: 700; font-size: 24px; color: #2d3748; margin: 0;">{breadth_cm:.2f} cm</p>
                            </div>
                            <div style="background: white; padding: 20px; border-radius: 8px; text-align: center; box-shadow: 0 2px 8px rgba(0,0,0,0.1);">
                                <h3 style="color: #e53e3e; margin: 0 0 10px 0;">Surface Area</h3>
                                <p style="font-weight: 700; font-size: 24px; color: #2d3748; margin: 0;">{area_cm2:.2f} cm²</p>
                            </div>
                        </div>
                    </div>
                    
                    <!-- Risk Assessment -->
                    <div style="background: #fff4e6; padding: 30px; border-radius: 12px; margin-bottom: 30px;">
                        <h2 style="color: #2d3748; margin-top: 0;">⚠️ Risk Assessment</h2>
                        <div style="display: flex; align-items: center; margin: 20px 0; flex-wrap: wrap;">
                            <div style="background: {'#d4edda' if risk_class == 'low' else '#fff3cd' if risk_class == 'moderate' else '#f8d7da'}; 
                                    color: {'#155724' if risk_class == 'low' else '#856404' if risk_class == 'moderate' else '#721c24'}; 
                                    padding: 15px 30px; 
                                    border-radius: 30px; 
                                    font-weight: 700; 
                                    font-size: 20px;
                                    text-transform: uppercase;
                                    letter-spacing: 1px;
                                    margin-right: 20px;
                                    margin-bottom: 10px;">
                                {risk_level} RISK
                            </div>
                            <div style="font-size: 18px; color: #2d3748;">
                                <strong>Risk Score:</strong> {risk_score}/10
                            </div>
                        </div>
                        <div style="background: white; padding: 25px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 8px rgba(0,0,0,0.05);">
                            <h3 style="margin-top: 0; color: #2d3748;">Identified Risk Factors:</h3>
                            {risk_factors_html}
                        </div>
                    </div>
                    
                    <!-- Patient Information Summary -->
                    <div style="background: #f0f8f0; padding: 30px; border-radius: 12px; margin-bottom: 30px;">
                        <h2 style="color: #2d3748; margin-top: 0;">👤 Patient Information Summary</h2>
                        <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 15px;">
                            <div><strong>Age:</strong> {questionnaire_data.get('age', 'Not specified')} years</div>
                            <div><strong>Gender:</strong> {questionnaire_data.get('patient_gender', 'Not specified')}</div>
                            <div><strong>Diabetic Status:</strong> {questionnaire_data.get('diabetic', 'Unknown')}</div>
                            <div><strong>Pain Level:</strong> {questionnaire_data.get('pain_level', 'Not assessed')}/10</div>
                            <div><strong>Wound Duration:</strong> {questionnaire_data.get('wound_duration', 'Not specified')}</div>
                            <div><strong>Moisture Level:</strong> {questionnaire_data.get('moisture', 'Not assessed')}</div>
                        </div>
                        {f"<div style='margin-top: 20px;'><strong>Medical History:</strong> {questionnaire_data.get('medical_history', 'None provided')}</div>" if questionnaire_data.get('medical_history') else ""}
                        {f"<div style='margin-top: 10px;'><strong>Current Medications:</strong> {questionnaire_data.get('medications', 'None listed')}</div>" if questionnaire_data.get('medications') else ""}
                        {f"<div style='margin-top: 10px;'><strong>Known Allergies:</strong> {questionnaire_data.get('allergies', 'None listed')}</div>" if questionnaire_data.get('allergies') else ""}
                    </div>
                    
                    <!-- AI Generated Report -->
                    {f'<div style="background: #f8f9fa; padding: 30px; border-radius: 12px; margin-bottom: 30px;"><h2 style="color: #2d3748; margin-top: 0;">🤖 AI-Generated Clinical Report</h2><div style="background: white; padding: 25px; border-radius: 8px; box-shadow: 0 2px 8px rgba(0,0,0,0.05);">{report_html}</div></div>' if report_html else ''}
                    
                    <!-- Important Disclaimers -->
                    <div style="background: #fff5f5; border: 2px solid #feb2b2; padding: 25px; border-radius: 12px; margin: 30px 0;">
                        <h3 style="color: #c53030; margin-top: 0;">⚠️ Important Medical Disclaimers</h3>
                        <ul style="color: #742a2a; line-height: 1.6;">
                            <li><strong>Not a Medical Diagnosis:</strong> This AI analysis is for informational purposes only and does not constitute medical advice, diagnosis, or treatment.</li>
                            <li><strong>Professional Consultation Required:</strong> Always consult with qualified healthcare professionals for proper clinical assessment and treatment decisions.</li>
                            <li><strong>Measurement Accuracy:</strong> All measurements are estimates based on computer vision algorithms and should be verified with clinical tools.</li>
                            <li><strong>Risk Assessment Limitations:</strong> Risk factors are based on provided information and may not reflect the complete clinical picture.</li>
                        </ul>
                    </div>
                    
                    <!-- Footer -->
                    <div style="text-align: center; padding: 30px 0; border-top: 2px solid #e2e8f0; margin-top: 30px;">
                        <p style="color: #6c757d; font-style: italic; font-size: 16px; margin: 0;">
                            🏥 Analysis completed by <strong>SmartHeal AI</strong> - Advanced Wound Care Assistant<br>
                            <small>Report generated on {datetime.now().strftime('%B %d, %Y at %I:%M %p')}</small>
                        </p>
                    </div>
                </div>
            </div>
            """
            
            return html_output

        except Exception as e:
            logging.error(f"Error formatting comprehensive results: {e}")
            return f"<div class='status-error'>❌ Error displaying results: {str(e)}</div>"
    
    def _generate_risk_assessment(self, questionnaire_data):
        """Generate risk assessment based on questionnaire data"""
        if not questionnaire_data:
            return {'risk_level': 'Unknown', 'risk_score': 0, 'risk_factors': []}
        
        risk_factors = []
        risk_score = 0
        
        try:
            # Age assessment
            age = questionnaire_data.get('age', 0)
            if isinstance(age, str):
                try:
                    age = int(age)
                except ValueError:
                    age = 0
                    
            if age > 65:
                risk_factors.append("Advanced age (>65 years)")
                risk_score += 2
            elif age > 50:
                risk_factors.append("Older adult (50-65 years)")
                risk_score += 1
            
            # Diabetic status
            diabetic_status = str(questionnaire_data.get('diabetic', '')).lower()
            if 'yes' in diabetic_status:
                risk_factors.append("Diabetes mellitus")
                risk_score += 3
            
            # Infection signs
            infection = str(questionnaire_data.get('infection', '')).lower()
            if 'yes' in infection:
                risk_factors.append("Signs of infection present")
                risk_score += 3
            
            # Pain level
            pain_level = questionnaire_data.get('pain_level', 0)
            if isinstance(pain_level, str):
                try:
                    pain_level = float(pain_level)
                except ValueError:
                    pain_level = 0
                    
            if pain_level >= 7:
                risk_factors.append("High pain level (≥7/10)")
                risk_score += 2
            elif pain_level >= 5:
                risk_factors.append("Moderate pain level (5-6/10)")
                risk_score += 1
            
            # Wound duration
            duration = str(questionnaire_data.get('wound_duration', '')).lower()
            if any(term in duration for term in ['month', 'months', 'year', 'years']):
                risk_factors.append("Chronic wound (>4 weeks)")
                risk_score += 3
            
            # Moisture level
            moisture = str(questionnaire_data.get('moisture', '')).lower()
            if any(term in moisture for term in ['wet', 'saturated']):
                risk_factors.append("Excessive wound exudate")
                risk_score += 1
            
            # Medical history analysis
            medical_history = str(questionnaire_data.get('medical_history', '')).lower()
            if any(term in medical_history for term in ['vascular', 'circulation', 'heart']):
                risk_factors.append("Cardiovascular disease")
                risk_score += 2
            if any(term in medical_history for term in ['immune', 'cancer', 'steroid']):
                risk_factors.append("Immune system compromise")
                risk_score += 2
            if any(term in medical_history for term in ['smoking', 'tobacco']):
                risk_factors.append("Smoking history")
                risk_score += 2
            
            # Determine risk level
            if risk_score >= 8:
                risk_level = "Very High"
            elif risk_score >= 6:
                risk_level = "High"
            elif risk_score >= 3:
                risk_level = "Moderate"
            else:
                risk_level = "Low"
                
            return {
                'risk_score': risk_score,
                'risk_level': risk_level,
                'risk_factors': risk_factors
            }
            
        except Exception as e:
            logging.error(f"Risk assessment error: {e}")
            return {
                'risk_score': 0,
                'risk_level': 'Unknown',
                'risk_factors': ['Unable to assess risk due to data processing error']
            }