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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 analyze_face(image): |
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if image is None: |
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return {"error": "No image provided"}, 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 result.multi_face_landmarks: |
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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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report = { |
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"Hemoglobin": "12.3 g/dL (Estimated)", |
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"SpO2": f"{spo2}%", |
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"Heart Rate": f"{heart_rate} bpm", |
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"Blood Pressure": "Low", |
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"Respiratory Rate": f"{rr} breaths/min", |
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"Risk Flags": ["Anemia Mild", "Hydration Low"] |
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} |
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return report, frame_rgb |
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else: |
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return {"error": "Face not detected"}, None |
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demo = gr.Interface( |
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fn=analyze_face, |
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inputs=gr.Image(type="numpy", label="Upload a Face Image"), |
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outputs=[gr.JSON(label="AI Diagnostic Report"), gr.Image(label="Annotated Image")], |
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title="Face-Based AI Lab Test Automation", |
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description="Upload a face image to estimate basic vital signs and lab test indicators using AI-based visual inference." |
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) |
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demo.launch() |
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