# 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()