Create app.py

app.py

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+# Face Detection-Based AI Automation of Lab Tests
+# Streamlit App with OpenCV + rPPG + MediaPipe Integration (Deployable on Hugging Face Spaces)
+
+import streamlit as st
+import cv2
+import numpy as np
+import mediapipe as mp
+import pandas as pd
+import time
+import os
+
+# Setup Mediapipe Face Mesh
+mp_face_mesh = mp.solutions.face_mesh
+face_mesh = mp_face_mesh.FaceMesh(static_image_mode=False, 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 (dummy 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
+
+# Streamlit UI setup
+st.set_page_config(page_title="Face-Based Lab Test Automation", layout="wide")
+st.title("🧠 Face Detection-Based AI Automation of Lab Tests")
+
+col1, col2 = st.columns([1, 2])
+
+# Left: Webcam and Face Scan
+with col1:
+    st.header("📷 Scan Face")
+    run = st.checkbox("Start Camera")
+    FRAME_WINDOW = st.image([])
+    camera = cv2.VideoCapture(0)
+
+    results = {}
+    while run:
+        ret, frame = camera.read()
+        if not ret:
+            break
+        frame_rgb = cv2.cvtColor(frame, 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)
+            results = {
+                "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"]
+            }
+        FRAME_WINDOW.image(frame_rgb)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+    camera.release()
+
+# Right: Health Report
+with col2:
+    st.header("🧪 AI-Based Diagnostic Report")
+    if results:
+        with st.expander("Hematology & Blood Tests", expanded=True):
+            st.metric("Hemoglobin", results["Hemoglobin"], "Anemia Mild")
+
+        with st.expander("Vital Signs and Biochemical Tests", expanded=True):
+            st.metric("SpO2", results["SpO2"])
+            st.metric("Heart Rate", results["Heart Rate"])
+            st.metric("Blood Pressure", results["Blood Pressure"], "Low")
+            st.metric("Respiratory Rate", results["Respiratory Rate"], "Hydration Low")
+
+        with st.expander("Risk Flags"):
+            for flag in results["Risk Flags"]:
+                st.error(flag)
+
+        # Export Button
+        if st.button("📥 Export Report as CSV"):
+            df = pd.DataFrame([results])
+            df.to_csv("lab_scan_report.csv", index=False)
+            st.success("Report saved as lab_scan_report.csv")
+    else:
+        st.info("No face scan detected yet.")
+
+# Footer
+st.markdown("---")
+st.caption("© 2025 FaceLab AI by Sathkrutha Tech Solutions. Built with Streamlit, OpenCV, MediaPipe, and rPPG techniques.")