app.py

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
import pandas as pd
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from io import BytesIO

# 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


# Build health card layout
def build_health_card(profile_image, test_results, summary, patient_name="", patient_age="", patient_gender="", patient_id=""):
    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;">
            <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>
            <button style="padding: 12px 24px; background: linear-gradient(135deg, #2196f3, #1976d2); color: white; border: none; border-radius: 8px; cursor: pointer; font-weight: 600; font-size: 14px; box-shadow: 0 4px 12px rgba(33, 150, 243, 0.3);">
                📞 Find Labs Near Me
            </button>
        </div>
    </div>
    
    <style>
    @media print {{
        body * {{
            visibility: hidden;
        }}
        #health-card, #health-card * {{
            visibility: visible;
        }}
        #health-card {{
            position: absolute;
            left: 0;
            top: 0;
            width: 100% !important;
            max-width: none !important;
            margin: 0 !important;
            box-shadow: none !important;
            border: none !important;
        }}
        button {{
            display: none !important;
        }}
    }}
    </style>
    """
    return html


# Function to generate PDF from HTML content using reportlab
def generate_pdf(html_content):
    buffer = BytesIO()
    c = canvas.Canvas(buffer, pagesize=letter)
    
    # Adding basic content to PDF (you can modify this to match your layout)
    text = c.beginText(40, 750)
    text.setFont("Helvetica", 12)
    text.textLines(html_content)  # Add the content

    c.drawText(text)
    c.showPage()
    c.save()

    buffer.seek(0)
    return buffer


# Modified analyze_face function
def analyze_face(input_data):
    if isinstance(input_data, str):  # Video input (file path in Replit)
        cap = cv2.VideoCapture(input_data)
        if not cap.isOpened():
            return "<div style='color:red;'>⚠️ Error: Could not open video.</div>", None
        ret, frame = cap.read()
        cap.release()
        if not ret:
            return "<div style='color:red;'>⚠️ Error: Could not read video frame.</div>", None
    else:  # Image input
        frame = input_data
        if frame is None:
            return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None

    # Resize image to reduce processing time
    frame = cv2.resize(frame, (640, 480))  # Adjust resolution for Replit
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    # Provide image dimensions to mediapipe to avoid NORM_RECT warning
    result = face_mesh.process(frame_rgb)
    if not result.multi_face_landmarks:
        return "<div style='color:red;'>⚠️ Error: Face not detected.</div>", None
    landmarks = result.multi_face_landmarks[
        0].landmark  # Fixed: Use integer index
    features = extract_features(frame_rgb, landmarks)
    
    # Convert features to pandas DataFrame with correct column names (matching the training phase)
    features_df = pd.DataFrame([features], columns=["%red pixel", "%green pixel", "%blue pixel"])  # Ensure lowercase column names
    
    test_values = {}
    r2_scores = {}

    for label in models:
        if label == "Hemoglobin":
            prediction = models[label].predict(features_df)[0]
            test_values[label] = prediction
            r2_scores[label] = 0.385
        else:
            value = models[label].predict(
                [[random.uniform(0.2, 0.5) for _ in range(7)]])[0]
            test_values[label] = value
            r2_scores[label] = 0.0

    gray = cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2GRAY)
    green_std = np.std(frame_rgb[:, :, 1]) / 255
    brightness_std = np.std(gray) / 255
    tone_index = np.mean(frame_rgb[100:150, 100:150]) / 255 if frame_rgb[
        100:150, 100:150].size else 0.5
    hr_features = [brightness_std, green_std, tone_index]
    heart_rate = float(np.clip(hr_model.predict([hr_features])[0], 60, 100))
    skin_patch = frame_rgb[100:150, 100:150]
    skin_tone_index = np.mean(skin_patch) / 255 if skin_patch.size else 0.5
    brightness_variation = np.std(cv2.cvtColor(frame_rgb,
                                               cv2.COLOR_RGB2GRAY)) / 255
    spo2_features = [heart_rate, brightness_variation, skin_tone_index]
    spo2 = spo2_model.predict([spo2_features])[0]
    rr = int(12 + abs(heart_rate % 5 - 2))

    test_results = {
        "Hematology":
        build_table("🩸 Hematology",
                    [("Hemoglobin", test_values["Hemoglobin"], (13.5, 17.5)),
                     ("WBC Count", test_values["WBC Count"], (4.0, 11.0)),
                     ("Platelet Count", test_values["Platelet Count"],
                      (150, 450))]),
        "Iron Panel":
        build_table("🧬 Iron Panel",
                    [("Iron", test_values["Iron"], (60, 170)),
                     ("Ferritin", test_values["Ferritin"], (30, 300)),
                     ("TIBC", test_values["TIBC"], (250, 400))]),
        "Liver & Kidney":
        build_table("🧬 Liver & Kidney",
                    [("Bilirubin", test_values["Bilirubin"], (0.3, 1.2)),
                     ("Creatinine", test_values["Creatinine"], (0.6, 1.2)),
                     ("Urea", test_values["Urea"], (7, 20))]),
        "Electrolytes":
        build_table("🧪 Electrolytes",
                    [("Sodium", test_values["Sodium"], (135, 145)),
                     ("Potassium", test_values["Potassium"], (3.5, 5.1))]),
        "Vitals":
        build_table("❤️ Vitals",
                    [("SpO2", spo2, (95, 100)),
                     ("Heart Rate", heart_rate, (60, 100)),
                     ("Respiratory Rate", rr, (12, 20)),
                     ("Temperature", test_values["Temperature"], (97, 99)),
                     ("BP Systolic", test_values["BP Systolic"], (90, 120)),
                     ("BP Diastolic", test_values["BP Diastolic"], (60, 80))])
    }

    summary = "<ul><li>Your hemoglobin is a bit low — this could mean mild anemia.</li><li>Low iron storage detected — consider an iron profile test.</li><li>Elevated bilirubin — possible jaundice. Recommend LFT.</li><li>High HbA1c — prediabetes indication. Recommend glucose check.</li><li>Low SpO₂ — suggest retesting with a pulse oximeter.</li></ul>"

    _, buffer = cv2.imencode('.png', frame_rgb)
    profile_image_base64 = base64.b64encode(buffer).decode('utf-8')

    # Use global patient details
    global current_patient_details
    health_card_html = build_health_card(
        profile_image_base64, 
        test_results,
        summary, 
        current_patient_details['name'],
        current_patient_details['age'],
        current_patient_details['gender'],
        current_patient_details['id']
    )

    # Generate PDF from the HTML content
    pdf_file = generate_pdf(health_card_html)
    return health_card_html, pdf_file


# Modified route_inputs 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 = analyze_face(image if mode == "Image" else video)
    
    return health_card_html, pdf_file


# 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_image = gr.Image(label="📷 Key Frame Snapshot")
            download_btn = gr.Button("📥 Download PDF")
            
    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_image])

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