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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", "π»", "#FFCCCC") | |
elif value > high: | |
return ("High", "πΊ", "#FFE680") | |
else: | |
return ("Normal", "β ", "#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]} β {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): | |
# If video is passed as a path, open it using OpenCV | |
if isinstance(video, str): | |
cap = cv2.VideoCapture(video) | |
else: | |
# If video is passed as a numpy array, treat it as an in-memory video | |
cap = cv2.VideoCapture() | |
cap.open(video) | |
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("π©Έ Hematology", [("Hemoglobin", models["Hemoglobin"].predict([hr_features])[0], (13.5, 17.5))]), | |
build_table("𧬠Iron Panel", [("Iron", models["Iron"].predict([hr_features])[0], (60, 170))]), | |
build_table("π§ͺ Electrolytes", [("Sodium", models["Sodium"].predict([hr_features])[0], (135, 145))]), | |
build_table("β€οΈ Vitals", [("Heart Rate", heart_rate, (60, 100)), ("SpO2", spo2, (95, 100))]), | |
]) | |
return html_output | |
# Gradio Interface setup | |
with gr.Blocks() as demo: | |
gr.Markdown(""" | |
# π§ Face-Based Lab Test AI Report (Video Mode) | |
Upload a short face video (10β30s) 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="πΈ Upload Face Image") | |
video_input = gr.Video(label="π½ Upload Face Video", sources=["upload", "webcam"]) | |
submit_btn = gr.Button("π Analyze") | |
with gr.Column(): | |
result_html = gr.HTML(label="π§ͺ Health Report Table") | |
result_image = gr.Image(label="π· 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("""--- | |
β Table Format β’ AI Prediction β’ rPPG-based HR β’ Dynamic Summary β’ Multilingual Support β’ CTA""") | |
demo.launch() | |