app.py

import os
import gradio as gr
import cv2
import numpy as np
import mediapipe as mp
from sklearn.linear_model import LinearRegression
import random
import base64
import joblib
from datetime import datetime
import shutil
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib import colors

# Initialize the face mesh model
mp_face_mesh = mp.solutions.face_mesh
face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True,
                                  max_num_faces=1,
                                  refine_landmarks=True,
                                  min_detection_confidence=0.5)

# Functions for feature extraction
def extract_features(image, landmarks):
    red_channel = image[:, :, 2]
    green_channel = image[:, :, 1]
    blue_channel = image[:, :, 0]

    red_percent = 100 * np.mean(red_channel) / 255
    green_percent = 100 * np.mean(green_channel) / 255
    blue_percent = 100 * np.mean(blue_channel) / 255

    return [red_percent, green_percent, blue_percent]

def train_model(output_range):
    X = [[
        random.uniform(0.2, 0.5),
        random.uniform(0.05, 0.2),
        random.uniform(0.05, 0.2),
        random.uniform(0.2, 0.5),
        random.uniform(0.2, 0.5),
        random.uniform(0.2, 0.5),
        random.uniform(0.2, 0.5)
    ] for _ in range(100)]
    y = [random.uniform(*output_range) for _ in X]
    model = LinearRegression().fit(X, y)
    return model

# Load models
try:
    hemoglobin_model = joblib.load("hemoglobin_model_from_anemia_dataset.pkl")
    spo2_model = joblib.load("spo2_model_simulated.pkl")
    hr_model = joblib.load("heart_rate_model.pkl")
except FileNotFoundError:
    print("Error: One or more .pkl model files are missing. Please upload them.")
    exit(1)

models = {
    "Hemoglobin": hemoglobin_model,
    "WBC Count": train_model((4.0, 11.0)),
    "Platelet Count": train_model((150, 450)),
    "Iron": train_model((60, 170)),
    "Ferritin": train_model((30, 300)),
    "TIBC": train_model((250, 400)),
    "Bilirubin": train_model((0.3, 1.2)),
    "Creatinine": train_model((0.6, 1.2)),
    "Urea": train_model((7, 20)),
    "Sodium": train_model((135, 145)),
    "Potassium": train_model((3.5, 5.1)),
    "TSH": train_model((0.4, 4.0)),
    "Cortisol": train_model((5, 25)),
    "FBS": train_model((70, 110)),
    "HbA1c": train_model((4.0, 5.7)),
    "Albumin": train_model((3.5, 5.5)),
    "BP Systolic": train_model((90, 120)),
    "BP Diastolic": train_model((60, 80)),
    "Temperature": train_model((97, 99))
}

# Helper function for risk level color coding
def get_risk_color(value, normal_range):
    low, high = normal_range
    if value < low:
        return ("Low", "🔻", "#fff3cd")
    elif value > high:
        return ("High", "🔺", "#f8d7da")
    else:
        return ("Normal", "✅", "#d4edda")

# Function to build table for test results
def build_table(title, rows):
    html = (
        f'<div style="margin-bottom: 25px; border-radius: 8px; overflow: hidden; border: 1px solid #e0e0e0;">'
        f'<div style="background: linear-gradient(135deg, #f5f7fa, #c3cfe2); padding: 12px 16px; border-bottom: 1px solid #e0e0e0;">'
        f'<h4 style="margin: 0; color: #2c3e50; font-size: 16px; font-weight: 600;">{title}</h4>'
        f'</div>'
        f'<table style="width:100%; border-collapse:collapse; background: white;">'
        f'<thead><tr style="background:#f8f9fa;"><th style="padding:12px 8px;border-bottom:2px solid #dee2e6;color:#495057;font-weight:600;text-align:left;font-size:13px;">Test</th><th style="padding:12px 8px;border-bottom:2px solid #dee2e6;color:#495057;font-weight:600;text-align:center;font-size:13px;">Result</th><th style="padding:12px 8px;border-bottom:2px solid #dee2e6;color:#495057;font-weight:600;text-align:center;font-size:13px;">Range</th><th style="padding:12px 8px;border-bottom:2px solid #dee2e6;color:#495057;font-weight:600;text-align:center;font-size:13px;">Level</th></tr></thead><tbody>'
    )
    for i, (label, value, ref) in enumerate(rows):
        level, icon, bg = get_risk_color(value, ref)
        row_bg = "#f8f9fa" if i % 2 == 0 else "white"
        if level != "Normal":
            row_bg = bg
        
        # Format the value with appropriate units
        if "Count" in label or "Platelet" in label:
            value_str = f"{value:.0f}"
        else:
            value_str = f"{value:.2f}"
            
        html += f'<tr style="background:{row_bg};border-bottom:1px solid #e9ecef;"><td style="padding:10px 8px;color:#2c3e50;font-weight:500;">{label}</td><td style="padding:10px 8px;text-align:center;color:#2c3e50;font-weight:600;">{value_str}</td><td style="padding:10px 8px;text-align:center;color:#6c757d;font-size:12px;">{ref[0]} - {ref[1]}</td><td style="padding:10px 8px;text-align:center;font-weight:600;color:{"#28a745" if level == "Normal" else "#dc3545" if level == "High" else "#ffc107"};">{icon} {level}</td></tr>'
    html += '</tbody></table></div>'
    return html

# Function to save the health report to PDF
def save_results_to_pdf(test_results, filename):
    try:
        # Create a PDF document
        doc = SimpleDocTemplate(filename, pagesize=letter)
        styles = getSampleStyleSheet()
        
        # Define custom styles
        title_style = ParagraphStyle(
            name='Title',
            fontSize=16,
            leading=20,
            alignment=1,  # Center
            spaceAfter=20,
            textColor=colors.black,
            fontName='Helvetica-Bold'
        )
        body_style = ParagraphStyle(
            name='Body',
            fontSize=12,
            leading=14,
            spaceAfter=10,
            textColor=colors.black,
            fontName='Helvetica'
        )
        
        # Build the PDF content
        flowables = []
        
        # Add title
        flowables.append(Paragraph("Health Report", title_style))
        
        # Add test results to the report
        for label, value in test_results.items():
            line = f"{label}: {value}"
            flowables.append(Paragraph(line, body_style))
            flowables.append(Spacer(1, 12))
        
        # Build the PDF
        doc.build(flowables)
        return f"PDF saved successfully as {filename}", filename
    except Exception as e:
        return f"Error saving PDF: {str(e)}", None

# Build health card layout
def build_health_card(profile_image, test_results, summary, patient_name="", patient_age="", patient_gender="", patient_id="", pdf_filepath=""):
    from datetime import datetime
    current_date = datetime.now().strftime("%B %d, %Y")
    
    html = f"""
    <div id="health-card" style="font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; max-width: 700px; margin: 20px auto; border-radius: 16px; background: linear-gradient(135deg, #e3f2fd 0%, #f3e5f5 100%); border: 2px solid #ddd; box-shadow: 0 8px 32px rgba(0, 0, 0, 0.15); padding: 30px; color: #1a1a1a;">
        
        <div style="background-color: rgba(255, 255, 255, 0.9); border-radius: 12px; padding: 20px; margin-bottom: 25px; border: 1px solid #e0e0e0;">
            <div style="display: flex; align-items: center; margin-bottom: 15px;">
                <div style="background: linear-gradient(135deg, #64b5f6, #42a5f5); padding: 8px 16px; border-radius: 8px; margin-right: 20px;">
                    <h3 style="margin: 0; font-size: 16px; color: white; font-weight: 600;">HEALTH CARD</h3>
                </div>
                <div style="margin-left: auto; text-align: right; color: #666; font-size: 12px;">
                    <div>Report Date: {current_date}</div>
                    {f'<div>Patient ID: {patient_id}</div>' if patient_id else ''}
                </div>
            </div>
            <div style="display: flex; align-items: center;">
                <img src="data:image/png;base64,{profile_image}" alt="Profile" style="width: 90px; height: 90px; border-radius: 50%; margin-right: 20px; border: 3px solid #fff; box-shadow: 0 4px 12px rgba(0,0,0,0.1);">
                <div>
                    <h2 style="margin: 0; font-size: 28px; color: #2c3e50; font-weight: 700;">{patient_name if patient_name else "Lab Test Results"}</h2>
                    <p style="margin: 4px 0 0 0; color: #666; font-size: 14px;">{f"Age: {patient_age} | Gender: {patient_gender}" if patient_age and patient_gender else "AI-Generated Health Analysis"}</p>
                    <p style="margin: 4px 0 0 0; color: #888; font-size: 12px;">Face-Based Health Analysis Report</p>
                </div>
            </div>
        </div>

        <div style="background-color: rgba(255, 255, 255, 0.95); border-radius: 12px; padding: 25px; margin-bottom: 25px; border: 1px solid #e0e0e0;">
            {test_results['Hematology']}
            {test_results['Iron Panel']}
            {test_results['Liver & Kidney']}
            {test_results['Electrolytes']}
            {test_results['Vitals']}
        </div>

        <div style="background-color: rgba(255, 255, 255, 0.95); padding: 20px; border-radius: 12px; border: 1px solid #e0e0e0; margin-bottom: 25px;">
            <h4 style="margin: 0 0 15px 0; color: #2c3e50; font-size: 18px; font-weight: 600;">📝 Summary & Recommendations</h4>
            <div style="color: #444; line-height: 1.6;">
                {summary}
            </div>
        </div>

        <div style="display: flex; gap: 15px; justify-content: center; flex-wrap: wrap;">
            <a href="{pdf_filepath}" download="Health_Report.pdf">
                <button style="padding: 12px 24px; background: linear-gradient(135deg, #4caf50, #45a049); color: white; border: none; border-radius: 8px; cursor: pointer; font-weight: 600; font-size: 14px; box-shadow: 0 4px 12px rgba(76, 175, 80, 0.3); transition: all 0.3s;">
                    📥 Download Report
                </button>
            </a>
        </div>
    </div>
    <style>
    @media print {{
        /* Hide input sections during print */
        .input-container {{ display: none; }}
        /* Keep only the health card visible */
        #health-card {{ display: block; }}
    }}
    </style>
    """
    return html

# Initialize global variable for patient details
current_patient_details = {'name': '', 'age': '', 'gender': '', 'id': ''}

# Route the inputs to the correct function
def route_inputs(mode, image, video, patient_name, patient_age, patient_gender, patient_id):
    if mode == "Image" and image is None:
        return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None
    if mode == "Video" and video is None:
        return "<div style='color:red;'>⚠️ Error: No video provided.</div>", None
    
    # Store patient details globally for use in analyze_face
    global current_patient_details
    current_patient_details = {
        'name': patient_name,
        'age': patient_age, 
        'gender': patient_gender,
        'id': patient_id
    }
    
    health_card_html, pdf_file_path = analyze_face(image if mode == "Image" else video)
    return health_card_html, pdf_file_path


# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("""# 🧠 Face-Based Lab Test AI Report (Video Mode)""")
    with gr.Row():
        with gr.Column(elem_id="input-container"):
            gr.Markdown("### Patient Information")
            patient_name = gr.Textbox(label="Patient Name", placeholder="Enter patient name")
            patient_age = gr.Number(label="Age", value=25, minimum=1, maximum=120)
            patient_gender = gr.Radio(label="Gender", choices=["Male", "Female", "Other"], value="Male")
            patient_id = gr.Textbox(label="Patient ID", placeholder="Enter patient ID (optional)")
            
            gr.Markdown("### Image/Video Input")
            mode_selector = gr.Radio(label="Choose Input Mode",
                                     choices=["Image", "Video"],
                                     value="Image")
            image_input = gr.Image(type="numpy", label="📸 Upload Face Image")
            video_input = gr.Video(label="Upload Face Video",
                                   sources=["upload", "webcam"])
            submit_btn = gr.Button("🔍 Analyze")
        with gr.Column(elem_id="output-container"):
            result_html = gr.HTML(label="🧪 Health Report Table")
            result_pdf = gr.File(label="Download Health Report PDF", interactive=False)

            submit_btn.click(fn=route_inputs,
                             inputs=[mode_selector, image_input, video_input, patient_name, patient_age, patient_gender, patient_id],
                             outputs=[result_html, result_pdf])

# Launch Gradio for Replit
demo.launch(server_name="0.0.0.0", server_port=7860)