Update app.py

app.py

@@ -7,17 +7,40 @@ from tensorflow.keras.models import Sequential
 from tensorflow.keras.optimizers import legacy
 from tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten
 from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler
-from moviepy.editor import VideoFileClip
 
 
 
-def convert_mp4_to_mpg(input_file):
-    if not os.path.exists(input_file):
-        raise FileNotFoundError(f"Input file '{input_file}' not found.")
+def convert_mp4_to_mpg(input_file, output_file):
+    """
+    Convert an MP4 video file to an MPG video file using OpenCV.
 
-    video_clip = VideoFileClip(input_file)
-    video_clip.write_videofile("output.mpg", codec='mpeg2video')
-    video_clip.close()
+    Args:
+    input_file (str): Path to the input MP4 file.
+    output_file (str): Path to the output MPG file.
+
+    Returns:
+    None
+    """
+    cap = cv2.VideoCapture(input_file)
+
+    # Check if the video file was opened successfully
+    if not cap.isOpened():
+        raise Exception(f"Could not open video file: {input_file}")
+
+    # Define the codec and create a VideoWriter object
+    fourcc = cv2.VideoWriter_fourcc(*'mpg2')  # Use 'mpg2' codec for MPG files
+    out = cv2.VideoWriter(output_file, fourcc, 30.0, (int(cap.get(3)), int(cap.get(4))))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        out.write(frame)
+
+    # Release the video objects
+    cap.release()
+    out.release()
 
 def load_video(video_path):
 
@@ -84,7 +107,7 @@ model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['ac
 model.load_weights('model/checkpoint')
 
 def Predict(Video):
-  convert_mp4_to_mpg(Video)
+  convert_mp4_to_mpg(Video,'output.mpg')
   sample = load_data(tf.convert_to_tensor("output.mpg"))
   yhat = model.predict(tf.expand_dims(sample, axis=0))
   decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()