app.py

import os  # Import the os module
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, Table, TableStyle
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 (showing only the Health Card section)
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))
        flowables.append(Spacer(1, 12))
        
        # Only add the health card content here
        for label, value in test_results.items():
            flowables.append(Paragraph(value, 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


# Function for Health Card Output (removes patient details)
def build_health_card(profile_image, test_results, summary):
    from datetime import datetime
    current_date = datetime.now().strftime("%B %d, %Y")
    
    # Build the Health Card HTML
    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>
                </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;">Face-Based Health Analysis Report</h2>
                </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}
        </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;">
            <button onclick="window.print()" 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>
        </div>
    </div>
    """
    return html

# Define the Gradio interface
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

    # Process the inputs for analysis
    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():
            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():
            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 interface
demo.launch(server_name="0.0.0.0", server_port=7860)