Update app.py

app.py

@@ -6,14 +6,16 @@ from tensorflow.keras.applications.efficientnet_v2 import preprocess_input
 from PIL import Image
 import cv2
 from streamlit_webrtc import webrtc_streamer, VideoProcessorBase
-
+import av
 
 # Load your emotion classification model
 def load_model(model_path, weights_path):
+    st.write("Loading model...")
     with open(model_path, 'r') as json_file:
         model_json = json_file.read()
     loaded_model = tf.keras.models.model_from_json(model_json)
     loaded_model.load_weights(weights_path)
+    st.write("Model loaded successfully")
     return loaded_model
 
 # Function to preprocess image for prediction
@@ -26,9 +28,11 @@ def preprocess_image(img, target_size=(224, 224)):
 
 # Function to predict emotion of the image
 def predict_emotion_single_image(img, model):
+    st.write("Predicting emotion...")
     preprocessed_img = preprocess_image(img)
     predictions = model.predict(preprocessed_img)
     predicted_class = np.argmax(predictions, axis=1)[0]
+    st.write(f"Prediction: {predicted_class}")
     return predicted_class
 
 # Video processor class for real-time emotion detection
@@ -40,20 +44,13 @@ class VideoProcessor(VideoProcessorBase):
 
     def recv(self, frame):
         img = frame.to_ndarray(format="bgr24")
-        
-        # Convert the frame to grayscale
         gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        
-        # Detect faces in the grayscale frame
         faces = self.face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-        
-        # Draw rectangles around the faces and predict emotions
         for (x, y, w, h) in faces:
             face_img = img[y:y + h, x:x + w]
             predicted_class = predict_emotion_single_image(Image.fromarray(cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB)), self.model)
             cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
             cv2.putText(img, self.class_names[predicted_class], (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
-        
         return av.VideoFrame.from_ndarray(img, format="bgr24")
 
 # Main Streamlit function
@@ -78,7 +75,7 @@ def main():
             img = Image.open(single_image)
             predicted_emotion = predict_emotion_single_image(img, model)
             st.image(img, caption="Uploaded Image", use_column_width=True)
-            st.write("Predicted Emotion for Single Image:", class_names[predicted_emotion])
+            st.write("Predicted Emotion for Single Image:", ['Ahegao', 'Angry', 'Happy', 'Neutral', 'Sad', 'Surprise'][predicted_emotion])
 
 if __name__ == "__main__":
     main()