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# Face Detection-Based AI Automation of Lab Tests
# Redesigned UI using Gradio Blocks + HTML Cards
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 ("π» LOW", "#FFCCCC") # Red background
elif value > high:
return ("πΊ HIGH", "#FFE680") # Yellow background
else:
return ("β
Normal", "#CCFFCC") # Green background
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 not result.multi_face_landmarks:
return [["Face not detected", "#FFDDDD"]], None
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
def section(title, items):
html = f'<div style="padding:10px;border:1px solid #ccc;border-radius:8px;margin-bottom:10px;background:#f8f9fa;">'
html += f'<h4 style="margin:0 0 10px 0">{title}</h4>'
for label, val, rng in items:
status, bgcolor = get_risk_color(val, rng)
html += f'<div style="padding:6px;margin-bottom:4px;background:{bgcolor};border-radius:4px;">{label}: {val} - {status}</div>'
html += '</div>'
return html
cards = [
section("π©Έ Hematology", [
("Hemoglobin", hb, (13.5, 17.5)),
("WBC Count", wbc, (4.0, 11.0)),
("Platelets", platelets, (150, 450))
]),
section("𧬠Iron & Liver Panel", [
("Iron", iron, (60, 170)),
("Ferritin", ferritin, (30, 300)),
("TIBC", tibc, (250, 400)),
("Bilirubin", bilirubin, (0.3, 1.2))
]),
section("π§ͺ Kidney, Thyroid & Stress", [
("Creatinine", creatinine, (0.6, 1.2)),
("TSH", tsh, (0.4, 4.0)),
("Cortisol", cortisol, (5, 25))
]),
section("π§ Metabolic Panel", [
("Fasting Blood Sugar", fbs, (70, 110)),
("HbA1c", hba1c, (4.0, 5.7))
]),
section("β€οΈ Vital Signs", [
("SpO2", spo2, (95, 100)),
("Heart Rate", heart_rate, (60, 100)),
("Respiratory Rate", rr, (12, 20))
])
]
return cards, frame_rgb
# Gradio App Layout (Custom UI with Cards)
demo = gr.Blocks()
with demo:
gr.Markdown("""
# π§ Face-Based Lab Test AI Report
Upload a face photo to infer health diagnostics with AI-based visual markers.
""")
with gr.Row():
with gr.Column(scale=1):
image_input = gr.Image(type="numpy", label="πΈ Upload Face Image")
submit_btn = gr.Button("π Analyze")
with gr.Column(scale=2):
result_html = gr.HTML(label="π§ͺ Visual Diagnostic Cards")
result_image = gr.Image(label="π· Face Scan Annotated")
def format_html(cards):
return "".join(cards)
submit_btn.click(
fn=analyze_face,
inputs=image_input,
outputs=[result_html, result_image]
).then(
fn=format_html,
inputs=None,
outputs=result_html
)
demo.launch()
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