# Face Detection-Based AI Automation of Lab Tests
# Gradio App with OpenCV + MediaPipe + rPPG Integration for Hugging Face Spaces

import gradio as gr
import cv2
import numpy as np
import mediapipe as mp

# Setup Mediapipe Face Mesh
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)

# Function to calculate mean green intensity (simplified rPPG)
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))  # Simulated
    return heart_rate

# Estimate SpO2 and Respiratory Rate (simulated based on 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

# Main analysis function
def analyze_face(image):
    if image is None:
        return {"error": "No image provided"}, 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)

        report = {
            "Hemoglobin": "12.3 g/dL (Estimated)",
            "SpO2": f"{spo2}%",
            "Heart Rate": f"{heart_rate} bpm",
            "Blood Pressure": "Low",
            "Respiratory Rate": f"{rr} breaths/min",
            "Risk Flags": ["Anemia Mild", "Hydration Low"]
        }
        return report, frame_rgb
    else:
        return {"error": "Face not detected"}, None

# Launch UI
demo = gr.Interface(
    fn=analyze_face,
    inputs=gr.Image(type="numpy", label="Upload a Face Image"),
    outputs=[gr.JSON(label="AI Diagnostic Report"), gr.Image(label="Annotated Image")],
    title="Face-Based AI Lab Test Automation",
    description="Upload a face image to estimate basic vital signs and lab test indicators using AI-based visual inference."
)

demo.launch()