# 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):
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
import joblib
hemoglobin_model = joblib.load("hemoglobin_model_from_anemia_dataset.pkl")
hemoglobin_r2 = 0.385
import joblib
spo2_model = joblib.load("spo2_model_simulated.pkl")
hr_model = joblib.load("heart_rate_model.pkl")
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))
}
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''
f'
{title} '
f'
'
f'Test Result Expected Range Level '
)
for label, value, ref in rows:
level, icon, bg = get_risk_color(value, ref)
html += f'{label} {value:.2f} {ref[0]} – {ref[1]} {icon} {level}
❤️ HR: {heart_rate:.2f} bpm, SpO₂: {spo2:.2f}%, RR: {rr} bpm ⚠️ Error: No image provided.
", None
frame_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
result = face_mesh.process(frame_rgb)
if not result.multi_face_landmarks:
return "⚠️ Error: Face not detected.
", None
landmarks = result.multi_face_landmarks[0].landmark
features = extract_features(frame_rgb, landmarks)
test_values = {}
r2_scores = {}
for label in models:
if label == "Hemoglobin":
prediction = models[label].predict([features])[0]
test_values[label] = prediction
r2_scores[label] = hemoglobin_r2
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 # simulate other 7D inputs
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))
html_output = "".join([
f'Hemoglobin R² Score: {r2_scores.get("Hemoglobin", "NA"):.2f}
',
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))]),
build_table("🧬 Iron Panel", [("Iron", test_values["Iron"], (60, 170)), ("Ferritin", test_values["Ferritin"], (30, 300)), ("TIBC", test_values["TIBC"], (250, 400))]),
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))]),
build_table("🧪 Electrolytes", [("Sodium", test_values["Sodium"], (135, 145)), ("Potassium", test_values["Potassium"], (3.5, 5.1))]),
build_table("🧁 Metabolic & Thyroid", [("FBS", test_values["FBS"], (70, 110)), ("HbA1c", test_values["HbA1c"], (4.0, 5.7)), ("TSH", test_values["TSH"], (0.4, 4.0))]),
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))]),
build_table("🩹 Other Indicators", [("Cortisol", test_values["Cortisol"], (5, 25)), ("Albumin", test_values["Albumin"], (3.5, 5.5))])
])
summary = ""
summary += "
📝 Summary for You "
if test_values["Hemoglobin"] < 13.5:
summary += "Your hemoglobin is a bit low — this could mean mild anemia. "
if test_values["Iron"] < 60 or test_values["Ferritin"] < 30:
summary += "Low iron storage detected — consider an iron profile test. "
if test_values["Bilirubin"] > 1.2:
summary += "Elevated bilirubin — possible jaundice. Recommend LFT. "
if test_values["HbA1c"] > 5.7:
summary += "High HbA1c — prediabetes indication. Recommend glucose check. "
if spo2 < 95:
summary += "Low SpO₂ — suggest retesting with a pulse oximeter. "
summary += " 💡 Tip: This is an AI-based estimate. Please follow up with a lab.
"
html_output += "
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"
html_output += "
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