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import gradio as gr |
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import cv2 |
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import numpy as np |
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import mediapipe as mp |
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mp_face_mesh = mp.solutions.face_mesh |
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face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1, refine_landmarks=True, min_detection_confidence=0.5) |
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def estimate_heart_rate(frame, landmarks): |
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h, w, _ = frame.shape |
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forehead_pts = [landmarks[10], landmarks[338], landmarks[297], landmarks[332]] |
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mask = np.zeros((h, w), dtype=np.uint8) |
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pts = np.array([[int(pt.x * w), int(pt.y * h)] for pt in forehead_pts], np.int32) |
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cv2.fillConvexPoly(mask, pts, 255) |
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green_channel = cv2.split(frame)[1] |
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mean_intensity = cv2.mean(green_channel, mask=mask)[0] |
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heart_rate = int(60 + 30 * np.sin(mean_intensity / 255.0 * np.pi)) |
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return heart_rate |
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def estimate_spo2_rr(heart_rate): |
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spo2 = min(100, max(90, 97 + (heart_rate % 5 - 2))) |
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rr = int(12 + abs(heart_rate % 5 - 2)) |
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return spo2, rr |
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def get_risk_color(value, normal_range): |
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low, high = normal_range |
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if value < low: |
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return ("🔻 LOW", "#FFCCCC") |
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elif value > high: |
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return ("🔺 HIGH", "#FFE680") |
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else: |
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return ("✅ Normal", "#CCFFCC") |
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def analyze_face(image): |
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if image is None: |
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return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None |
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frame_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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result = face_mesh.process(frame_rgb) |
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if not result.multi_face_landmarks: |
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return "<div style='color:red;'>⚠️ Error: Face not detected.</div>", None |
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landmarks = result.multi_face_landmarks[0].landmark |
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heart_rate = estimate_heart_rate(frame_rgb, landmarks) |
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spo2, rr = estimate_spo2_rr(heart_rate) |
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hb, wbc, platelets = 12.3, 6.4, 210 |
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iron, ferritin, tibc = 55, 45, 340 |
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bilirubin, creatinine = 1.5, 1.3 |
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tsh, cortisol = 2.5, 18 |
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fbs, hba1c = 120, 6.2 |
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def section(title, items): |
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html = f'<div style="padding:10px;border:1px solid #ccc;border-radius:8px;margin-bottom:10px;background:#f8f9fa;">' |
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html += f'<h4 style="margin:0 0 10px 0">{title}</h4>' |
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for label, val, rng in items: |
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status, bgcolor = get_risk_color(val, rng) |
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html += f'<div style="padding:6px;margin-bottom:4px;background:{bgcolor};border-radius:4px;">{label}: {val} - {status}</div>' |
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html += '</div>' |
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return html |
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report = "".join([ |
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section("🩸 Hematology", [("Hemoglobin", hb, (13.5, 17.5)), ("WBC Count", wbc, (4.0, 11.0)), ("Platelets", platelets, (150, 450))]), |
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section("🧬 Iron & Liver Panel", [("Iron", iron, (60, 170)), ("Ferritin", ferritin, (30, 300)), ("TIBC", tibc, (250, 400)), ("Bilirubin", bilirubin, (0.3, 1.2))]), |
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section("🧪 Kidney, Thyroid & Stress", [("Creatinine", creatinine, (0.6, 1.2)), ("TSH", tsh, (0.4, 4.0)), ("Cortisol", cortisol, (5, 25))]), |
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section("🧁 Metabolic Panel", [("Fasting Blood Sugar", fbs, (70, 110)), ("HbA1c", hba1c, (4.0, 5.7))]), |
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section("❤️ Vital Signs", [("SpO2", spo2, (95, 100)), ("Heart Rate", heart_rate, (60, 100)), ("Respiratory Rate", rr, (12, 20))]) |
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]) |
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return report, frame_rgb |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown(""" |
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# 🧠 Face-Based Lab Test AI Report |
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Upload a face photo to infer health diagnostics with AI-based visual markers. |
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""") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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image_input = gr.Image(type="numpy", label="📸 Upload Face Image") |
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submit_btn = gr.Button("🔍 Analyze") |
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with gr.Column(scale=2): |
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result_html = gr.HTML(label="🧪 Visual Diagnostic Cards") |
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result_image = gr.Image(label="📷 Face Scan Annotated") |
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submit_btn.click( |
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fn=analyze_face, |
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inputs=image_input, |
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outputs=[result_html, result_image] |
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) |
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demo.launch() |
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