Update app.py

app.py

@@ -1,49 +1,75 @@
-import cv2
 import streamlit as st
+import cv2
+import mediapipe as mp
 import numpy as np
-from some_model_library import load_model, predict_stress
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from PIL import Image
 
-import os
-import subprocess
+# Load the pre-trained model
+@st.cache_resource
+def load_stress_model():
+    return load_model("stress_model.h5")  # Replace with your actual model file
 
-# Install groq dynamically
-try:
-    import groq
-except ImportError:
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "groq==0.13.1"])
+model = load_stress_model()
 
+# Mediapipe face detection setup
+mp_face_mesh = mp.solutions.face_mesh
+face_mesh = mp_face_mesh.FaceMesh(min_detection_confidence=0.5, min_tracking_confidence=0.5)
 
+# Streamlit app UI
+st.title("Real-Time Stress Detection")
+st.write("This app detects stress levels in real-time using your webcam.")
 
-# Load pre-trained model
-model = load_model('path_to_your_model')
+# Start webcam
+run_camera = st.button("Start Webcam")
 
-# Streamlit interface
-st.title("Real-Time Stress Level Detection")
-st.text("Using your laptop camera to detect stress levels in real-time.")
+if run_camera:
+    st.write("Press 'q' to stop the camera.")
+    stframe = st.empty()  # Placeholder for video frames
 
-# Initialize webcam
-stframe = st.empty()
-cap = cv2.VideoCapture(0)
+    cap = cv2.VideoCapture(0)  # Open webcam
 
-if not cap.isOpened():
-    st.error("Could not access the camera.")
-else:
-    while True:
+    while cap.isOpened():
         ret, frame = cap.read()
         if not ret:
-            st.error("Failed to read from camera.")
+            st.write("Failed to grab frame.")
             break
-        
-        # Process the frame (resize, detect face, etc.)
-        processed_frame = preprocess_frame(frame)
-        
-        # Predict stress level
-        stress_level = predict_stress(model, processed_frame)
-        
-        # Display results
+
+        # Flip and process the frame
+        frame = cv2.flip(frame, 1)
+        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        results = face_mesh.process(rgb_frame)
+
+        # Draw face mesh
+        if results.multi_face_landmarks:
+            for face_landmarks in results.multi_face_landmarks:
+                mp.solutions.drawing_utils.draw_landmarks(
+                    image=frame,
+                    landmark_list=face_landmarks,
+                    connections=mp_face_mesh.FACEMESH_TESSELATION,
+                    landmark_drawing_spec=None,
+                    connection_drawing_spec=mp.solutions.drawing_styles.get_default_face_mesh_tesselation_style()
+                )
+
+        # Resize for model prediction
+        img_resized = cv2.resize(rgb_frame, (224, 224)) / 255.0
+        img_resized = np.expand_dims(img_resized, axis=0)
+
+        # Model prediction
+        prediction = model.predict(img_resized)
+        stress_level = np.argmax(prediction)
+
+        # Display stress level
+        cv2.putText(frame, f"Stress Level: {stress_level}", (10, 30),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
+
+        # Stream to Streamlit
         stframe.image(frame, channels="BGR")
-        st.metric("Stress Level", stress_level)
+
+        # Break on 'q' key
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
 
     cap.release()
     cv2.destroyAllWindows()
-