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# Enhanced Face-Based Lab Test Predictor with AI Models for 30 Lab Metrics
import gradio as gr
import cv2
import numpy as np
import mediapipe as mp
from sklearn.linear_model import LinearRegression
import random
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)
def extract_features(image, landmarks):
mean_intensity = np.mean(image)
h, w, _ = image.shape
bbox_width = max(pt.x for pt in landmarks) - min(pt.x for pt in landmarks)
bbox_height = max(pt.y for pt in landmarks) - min(pt.y for pt in landmarks)
def dist(p1, p2):
return ((p1.x - p2.x)**2 + (p1.y - p2.y)**2) ** 0.5
eye_dist = dist(landmarks[33], landmarks[263])
nose_len = dist(landmarks[1], landmarks[2]) + dist(landmarks[2], landmarks[98])
jaw_width = dist(landmarks[234], landmarks[454])
left_cheek = landmarks[234]
right_cheek = landmarks[454]
cx1, cy1 = int(left_cheek.x * w), int(left_cheek.y * h)
cx2, cy2 = int(right_cheek.x * w), int(right_cheek.y * h)
skin_tone1 = np.mean(image[cy1-5:cy1+5, cx1-5:cx1+5]) if 5 <= cy1 < h-5 and 5 <= cx1 < w-5 else 0
skin_tone2 = np.mean(image[cy2-5:cy2+5, cx2-5:cx2+5]) if 5 <= cy2 < h-5 and 5 <= cx2 < w-5 else 0
avg_skin_tone = (skin_tone1 + skin_tone2) / 2
return [mean_intensity, bbox_width, bbox_height, eye_dist, nose_len, jaw_width, avg_skin_tone]
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
models = {
"Hemoglobin": train_model((13.5, 17.5)),
"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))
}
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")
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
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