Update app.py

app.py

@@ -6,6 +6,10 @@ import os
 from keras.models import load_model
 from PIL import Image
 import tempfile
+import pyttsx3  # Import pyttsx3 for text-to-speech
+
+# Initialize the pyttsx3 engine
+engine = pyttsx3.init()
 
 # Larger title
 st.markdown("<h1 style='text-align: center;'>Emotion Detection with Face Recognition</h1>", unsafe_allow_html=True)
@@ -35,7 +39,7 @@ face_recognizer = cv2.face.LBPHFaceRecognizer_create()
 def load_known_faces():
     folder_path = "known_faces"  # Place your folder with known faces here
     for image_name in os.listdir(folder_path):
-        if image_name.endswith(('.jpg', '.jpeg', '.png')):
+        if image_name.endswith(('.jpg', '.jpeg', '.png')):  # Read only image files
             image_path = os.path.join(folder_path, image_name)
             image = cv2.imread(image_path)
             gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
@@ -84,6 +88,10 @@ def process_frame(frame):
         # Format the result text as "Name is feeling Emotion"
         result_text = f"{name} is feeling {emotion}"
 
+        # Announce the detected emotion with voice output
+        engine.say(result_text)
+        engine.runAndWait()
+
         # Draw bounding box and label on the frame
         cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
         cv2.putText(frame, result_text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
@@ -112,15 +120,9 @@ def video_feed(video_source):
 upload_choice = st.sidebar.radio("Choose input source", ["Upload Image", "Upload Video", "Camera"])
 
 if upload_choice == "Camera":
-    # Use Streamlit's built-in camera input widget for capturing images from the webcam
-    image = st.camera_input("Take a picture")
-
-    if image is not None:
-        # Convert the image to a numpy array
-        frame = np.array(Image.open(image))
-        frame, result_text = process_frame(frame)
-        st.image(frame, caption='Processed Image', use_column_width=True)
-        st.markdown(f"<h3 style='text-align: center;'>{result_text}</h3>", unsafe_allow_html=True)
+    # Access camera
+    video_source = cv2.VideoCapture(0)
+    video_feed(video_source)
 
 elif upload_choice == "Upload Image":
     uploaded_image = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg", "gif"])