Update app.py

app.py

@@ -1,17 +1,14 @@
 # Face Detection-Based AI Automation of Lab Tests
-# Streamlit App with OpenCV + rPPG + MediaPipe Integration (Deployable on Hugging Face Spaces)
+# Gradio App with OpenCV + MediaPipe + rPPG Integration for Hugging Face Spaces
 
-import streamlit as st
+import gradio as gr
 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)
+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):
@@ -25,74 +22,44 @@ def estimate_heart_rate(frame, landmarks):
     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)
+# 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
 
-# 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")
+# Main analysis function
+def analyze_face(image):
+    if image is None:
+        return "No image provided", None
 
-col1, col2 = st.columns([1, 2])
+    frame_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    result = face_mesh.process(frame_rgb)
 
-# Left: Webcam and Face Scan
-with col1:
-    st.header("📷 Scan Face")
-    run = st.checkbox("Start Camera")
-    FRAME_WINDOW = st.image([])
-    camera = cv2.VideoCapture(0)
+    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 = {}
-    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")
+        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:
-        st.info("No face scan detected yet.")
+        return "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."
+)
 
-# Footer
-st.markdown("---")
-st.caption("© 2025 FaceLab AI by Sathkrutha Tech Solutions. Built with Streamlit, OpenCV, MediaPipe, and rPPG techniques.")
+demo.launch()