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# Face Detection-Based AI Automation of Lab Tests
# Gradio App with Mobile-Responsive UI and Risk-Level Coloring
import gradio as gr
import cv2
import numpy as np
import mediapipe as mp
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 estimate_heart_rate(frame, landmarks):
h, w, _ = frame.shape
forehead_pts = [landmarks[10], landmarks[338], landmarks[297], landmarks[332]]
mask = np.zeros((h, w), dtype=np.uint8)
pts = np.array([[int(pt.x * w), int(pt.y * h)] for pt in forehead_pts], np.int32)
cv2.fillConvexPoly(mask, pts, 255)
green_channel = cv2.split(frame)[1]
mean_intensity = cv2.mean(green_channel, mask=mask)[0]
heart_rate = int(60 + 30 * np.sin(mean_intensity / 255.0 * np.pi))
return heart_rate
def estimate_spo2_rr(heart_rate):
spo2 = min(100, max(90, 97 + (heart_rate % 5 - 2)))
rr = int(12 + abs(heart_rate % 5 - 2))
return spo2, rr
def get_risk_color(value, normal_range):
low, high = normal_range
if value < low:
return "🔻 LOW"
elif value > high:
return "🔺 HIGH"
else:
return "✅ Normal"
def generate_flags_extended(params):
hb, wbc, platelets, iron, ferritin, tibc, bilirubin, creatinine, tsh, cortisol, fbs, hba1c = params
flags = []
if hb < 13.5:
flags.append("Hemoglobin Low - Possible Anemia")
if wbc < 4.0 or wbc > 11.0:
flags.append("Abnormal WBC Count - Possible Infection")
if platelets < 150:
flags.append("Platelet Drop Risk - Bruising Possible")
if iron < 60:
flags.append("Iron Deficiency Detected")
if ferritin < 30:
flags.append("Low Ferritin - Iron Store Low")
if tibc > 400:
flags.append("High TIBC - Iron Absorption Issue")
if bilirubin > 1.2:
flags.append("Jaundice Detected - Elevated Bilirubin")
if creatinine > 1.2:
flags.append("Kidney Function Concern - High Creatinine")
if tsh < 0.4 or tsh > 4.0:
flags.append("Thyroid Imbalance - Check TSH")
if cortisol < 5 or cortisol > 25:
flags.append("Stress Hormone Abnormality - Cortisol")
if fbs > 110:
flags.append("High Fasting Blood Sugar")
if hba1c > 5.7:
flags.append("Elevated HbA1c - Diabetes Risk")
flags.append("Mood / Stress analysis requires separate behavioral model")
return flags
def analyze_face(image):
if image is None:
return {}, None
frame_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
result = face_mesh.process(frame_rgb)
if result.multi_face_landmarks:
landmarks = result.multi_face_landmarks[0].landmark
heart_rate = estimate_heart_rate(frame_rgb, landmarks)
spo2, rr = estimate_spo2_rr(heart_rate)
hb, wbc, platelets = 12.3, 6.4, 210
iron, ferritin, tibc = 55, 45, 340
bilirubin, creatinine = 1.5, 1.3
tsh, cortisol = 2.5, 18
fbs, hba1c = 120, 6.2
flags = generate_flags_extended([hb, wbc, platelets, iron, ferritin, tibc, bilirubin, creatinine, tsh, cortisol, fbs, hba1c])
sections = {
"🩸 Hematology": [
f"Hemoglobin (Hb): {hb} g/dL - {get_risk_color(hb, (13.5, 17.5))}",
f"WBC Count: {wbc} x10^3/uL - {get_risk_color(wbc, (4.0, 11.0))}",
f"Platelet Count: {platelets} x10^3/uL - {get_risk_color(platelets, (150, 450))}"
],
"🧬 Iron & Liver Panel": [
f"Iron: {iron} µg/dL - {get_risk_color(iron, (60, 170))}",
f"Ferritin: {ferritin} ng/mL - {get_risk_color(ferritin, (30, 300))}",
f"TIBC: {tibc} µg/dL - {get_risk_color(tibc, (250, 400))}",
f"Bilirubin: {bilirubin} mg/dL - {get_risk_color(bilirubin, (0.3, 1.2))}"
],
"🧪 Kidney, Thyroid & Stress": [
f"Creatinine: {creatinine} mg/dL - {get_risk_color(creatinine, (0.6, 1.2))}",
f"TSH: {tsh} µIU/mL - {get_risk_color(tsh, (0.4, 4.0))}",
f"Cortisol: {cortisol} µg/dL - {get_risk_color(cortisol, (5, 25))}"
],
"🧁 Metabolic Panel": [
f"Fasting Blood Sugar: {fbs} mg/dL - {get_risk_color(fbs, (70, 110))}",
f"HbA1c: {hba1c}% - {get_risk_color(hba1c, (4.0, 5.7))}"
],
"❤️ Vital Signs": [
f"SpO2: {spo2}% - {get_risk_color(spo2, (95, 100))}",
f"Heart Rate: {heart_rate} bpm - {get_risk_color(heart_rate, (60, 100))}",
f"Respiratory Rate: {rr} breaths/min - {get_risk_color(rr, (12, 20))}",
"Blood Pressure: Low (simulated)"
],
"⚠️ Risk Flags": flags
}
return sections, frame_rgb
else:
return {"⚠️ Error": ["Face not detected"]}, None
# Mobile-optimized UI with styled labels
demo = gr.Blocks(css="""
@media only screen and (max-width: 768px) {
.gr-block.gr-column { width: 100% !important; }
}
""")
with demo:
gr.Markdown("""
# 🧠 Face-Based AI Lab Test Inference
Upload a clear face image to simulate categorized lab reports with visual grouping.
""")
with gr.Row():
with gr.Column(scale=1):
image_input = gr.Image(type="numpy", label="📸 Upload a Face Image")
submit_btn = gr.Button("🔍 Analyze Now")
with gr.Column(scale=2):
accordion_output = gr.Accordion("📂 Diagnostic Summary", open=True)
with accordion_output:
result_html = gr.HighlightedText(label="📊 Grouped Report", combine_adjacent=True)
result_image = gr.Image(label="🧍 Annotated Face Scan")
def format_report(sections):
lines = []
for title, values in sections.items():
lines.append((f"{title}",))
for item in values:
lines.append((f" - {item}",))
return lines
submit_btn.click(
fn=analyze_face,
inputs=image_input,
outputs=[result_html, result_image],
preprocess=False,
postprocess=False,
_js="(data) => [data]"
).then(
fn=format_report,
inputs=None,
outputs=result_html
)
gr.Markdown("---\n✅ Optimized for Mobile · Risk Indicators: 🔻 Low, 🔺 High, ✅ Normal")
demo.launch()
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