test live detection

app.py

@@ -13,19 +13,23 @@ face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_fronta
 emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
 
 # Define the predict_emotion function
-def predict_emotion(frame):
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
-    for (x, y, w, h) in faces:
-        face = gray[y:y+h, x:x+w]
-        face = cv2.resize(face, (48, 48))
-        face = np.expand_dims(face, axis=-1)
-        face = np.expand_dims(face, axis=0)
-        prediction = model.predict(face)
-        emotion = emotions[np.argmax(prediction)]
-        cv2.putText(frame, emotion, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
-    return frame
+def predict_emotion(vid):
+	cap = cv2.VideoCapture(vid)
+	# while True:
+	ret, frame = cap.read()
+	if ret:
+	    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+	    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+	    for (x, y, w, h) in faces:
+	        face = gray[y:y+h, x:x+w]
+	        face = cv2.resize(face, (48, 48))
+	        face = np.expand_dims(face, axis=-1)
+	        face = np.expand_dims(face, axis=0)
+	        prediction = model.predict(face)
+	        emotion = emotions[np.argmax(prediction)]
+	        cv2.putText(frame, emotion, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+	        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
+	    return frame
 
 # Start the video capture and emotion detection
 # cap = cv2.VideoCapture(0)
@@ -40,10 +44,10 @@ def predict_emotion(frame):
 # cv2.destroyAllWindows()
 
 
-input_image = gr.inputs.Image(source = "webcam", label="Your Face")
+input_image = gr.inputs.Video(source = "webcam", label="Your Face")
 # video = gr.inputs.Video(source = "webcam" )
 
-output_image = gr.inputs.Image(type = "numpy", label="Detected Emotion" )
+output_image = gr.inputs.Image( label="Detected Emotion" )