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 joblib

# Setup for Face Mesh detection
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)

# Function to extract color features from the image
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]

# Mock models training (for demonstration)
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 pre-trained models for Hemoglobin, SPO2, and Heart Rate
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")

# Model dictionary setup for other tests
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))
}

# Function to determine risk level
def get_risk_color(value, normal_range):
    low, high = normal_range
    if value < low:
        return ("Low", "🔻", "#FFCCCC")
    elif value > high:
        return ("High", "🔺", "#FFE680")
    else:
        return ("Normal", "✅", "#CCFFCC")

# Function to build an HTML table for displaying test results
def build_table(title, rows):
    html = (
        f'<div style="margin-bottom: 24px;">'
        f'<h4 style="margin: 8px 0;">{title}</h4>'
        f'<table style="width:100%; border-collapse:collapse;">'
        f'<thead><tr style="background:#f0f0f0;"><th style="padding:8px;border:1px solid #ccc;">Test</th><th style="padding:8px;border:1px solid #ccc;">Result</th><th style="padding:8px;border:1px solid #ccc;">Expected Range</th><th style="padding:8px;border:1px solid #ccc;">Level</th></tr></thead><tbody>'
    )
    for label, value, ref in rows:
        level, icon, bg = get_risk_color(value, ref)
        html += f'<tr style="background:{bg};"><td style="padding:6px;border:1px solid #ccc;">{label}</td><td style="padding:6px;border:1px solid #ccc;">{value:.2f}</td><td style="padding:6px;border:1px solid #ccc;">{ref[0]} – {ref[1]}</td><td style="padding:6px;border:1px solid #ccc;">{icon} {level}</td></tr>'
    html += '</tbody></table></div>'
    return html

# Analyzing video for health metrics
def analyze_video(video_path):
    import matplotlib.pyplot as plt
    cap = cv2.VideoCapture(video_path)
    brightness_vals = []
    green_vals = []
    frame_sample = None
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        if frame_sample is None:
            frame_sample = frame.copy()
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        green = frame[:, :, 1]
        brightness_vals.append(np.mean(gray))
        green_vals.append(np.mean(green))
    cap.release()
    
    # Simulate heart rate and SPO2 estimation
    brightness_std = np.std(brightness_vals) / 255
    green_std = np.std(green_vals) / 255
    tone_index = np.mean(frame_sample[100:150, 100:150]) / 255 if frame_sample[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))
    spo2_features = [heart_rate, np.std(brightness_vals), np.mean(frame_sample[100:150, 100:150])]
    spo2 = spo2_model.predict([spo2_features])[0]
    
    # Generating the health card with test results
    html_output = "".join([
        build_table("🩸 Hematology", [("Hemoglobin", models["Hemoglobin"].predict([hr_features])[0], (13.5, 17.5))]),
        build_table("🧬 Iron Panel", [("Iron", models["Iron"].predict([hr_features])[0], (60, 170))]),
        build_table("🧪 Electrolytes", [("Sodium", models["Sodium"].predict([hr_features])[0], (135, 145))]),
        build_table("❤️ Vitals", [("Heart Rate", heart_rate, (60, 100)), ("SpO2", spo2, (95, 100))]),
    ])
    return html_output

# Gradio Interface setup
with gr.Blocks() as demo:
    gr.Markdown("""
    # 🧠 Face-Based Lab Test AI Report (Video Mode)
    Upload a short face video (10–30s) to infer health diagnostics using rPPG analysis.
    """)
    with gr.Row():
        with gr.Column():
            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")

    def route_inputs(mode, image, video):
        return analyze_video(video) if mode == "Video" else analyze_video(image)

    submit_btn.click(fn=route_inputs, inputs=[mode_selector, image_input, video_input], outputs=[result_html, result_image])

    gr.Markdown("""---
✅ Table Format • AI Prediction • rPPG-based HR • Dynamic Summary • Multilingual Support • CTA""")

demo.launch()