Update app.py

app.py

@@ -11,11 +11,24 @@ from scipy.io.wavfile import write as write_wav
 from scipy import signal
 from moviepy.editor import VideoFileClip, AudioFileClip
 from transformers import AutoProcessor, AutoModelForAudioGeneration
+import requests  # Add this line
 
 # Set up logging for better debug tracking
 logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger()
 
+# Download Places365 class labels
+try:
+    logging.info("Downloading Places365 class labels...")
+    url = "http://places2.csail.mit.edu/models_places365/categories_places365.txt"
+    response = requests.get(url)
+    with open("categories_places365.txt", "wb") as f:
+        f.write(response.content)
+    logging.info("Places365 class labels downloaded successfully.")
+except Exception as e:
+    logging.error(f"Error downloading Places365 class labels: {e}")
+    raise
+
 # Load Places365 model for scene detection (on CPU to save GPU memory)
 try:
     logging.info("Loading Places365 model for scene detection...")
@@ -28,7 +41,6 @@ except Exception as e:
     raise
 
 # Load Places365 class labels
-!wget http://places2.csail.mit.edu/models_places365/categories_places365.txt
 with open("categories_places365.txt", "r") as f:
     SCENE_CLASSES = [line.strip().split(" ")[0][3:] for line in f.readlines()]
 
@@ -49,159 +61,4 @@ except Exception as e:
     logging.error(f"Error loading AudioGen/MusicGen models: {e}")
     raise
 
-# Function to classify a frame using Places365
-def classify_frame(frame):
-    try:
-        preprocess = transforms.Compose([
-            transforms.Resize(128),  # Smaller resolution
-            transforms.CenterCrop(128),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-        ])
-        img = Image.fromarray(frame)
-        img = preprocess(img).unsqueeze(0)
-        with torch.no_grad():
-            output = places365(img.to("cpu"))  # Ensure inference on CPU
-        probabilities = F.softmax(output, dim=1)
-        _, predicted = torch.max(probabilities, 1)
-        predicted_index = predicted.item()
-
-        # Ensure the predicted index is within the range of SCENE_CLASSES
-        if predicted_index >= len(SCENE_CLASSES) or predicted_index < 0:
-            logging.warning(f"Predicted class index {predicted_index} is out of range. Defaulting to 'nature'.")
-            return "nature"  # Default scene type
-
-        scene_type = SCENE_CLASSES[predicted_index]
-        logging.info(f"Predicted scene: {scene_type}")
-        return scene_type
-    except Exception as e:
-        logging.error(f"Error classifying frame: {e}")
-        raise
-
-# Function to analyze video content and return the scene type using Places365
-def analyze_video(video_path):
-    try:
-        logging.info(f"Analyzing video: {video_path}")
-        clip = VideoFileClip(video_path)
-        frame = clip.get_frame(0)  # Get the first frame
-        frame = Image.fromarray(frame)  # Convert to PIL image
-        frame = np.array(frame.resize((128, 128)))  # Resize to reduce memory usage
-
-        # Classify the frame using Places365
-        scene_type = classify_frame(frame)
-        logging.info(f"Scene type detected: {scene_type}")
-        return scene_type
-    except Exception as e:
-        logging.error(f"Error analyzing video: {e}")
-        raise
-
-# Function to generate audio using AudioGen Medium
-def generate_audio_audiogen(scene, duration=10):
-    try:
-        logging.info(f"Generating audio for scene: {scene} using AudioGen Medium...")
-        inputs = audiogen_processor(
-            text=[f"Ambient sounds of {scene}"],
-            padding=True,
-            return_tensors="pt",
-        ).to(audiogen_model.device)  # Move inputs to the same device as the model
-        with torch.no_grad():
-            audio = audiogen_model.generate(**inputs, max_new_tokens=duration * 50)  # Adjust tokens for duration
-        audio = audio.cpu().numpy().squeeze()
-        audio_path = "generated_audio_audiogen.wav"
-        write_wav(audio_path, 16000, audio)  # Save as WAV file
-        logging.info(f"Audio generated and saved to: {audio_path}")
-        return audio_path
-    except Exception as e:
-        logging.error(f"Error generating audio with AudioGen Medium: {e}")
-        raise
-
-# Function to generate music using MusicGen Medium
-def generate_music_musicgen(scene, duration=10):
-    try:
-        logging.info(f"Generating music for scene: {scene} using MusicGen Medium...")
-        inputs = musicgen_processor(
-            text=[f"Calm music for {scene}"],
-            padding=True,
-            return_tensors="pt",
-        ).to(musicgen_model.device)  # Move inputs to the same device as the model
-        with torch.no_grad():
-            music = musicgen_model.generate(**inputs, max_new_tokens=duration * 50)  # Adjust tokens for duration
-        music = music.cpu().numpy().squeeze()
-        music_path = "generated_music_musicgen.wav"
-        write_wav(music_path, 16000, music)  # Save as WAV file
-        logging.info(f"Music generated and saved to: {music_path}")
-        return music_path
-    except Exception as e:
-        logging.error(f"Error generating music with MusicGen Medium: {e}")
-        raise
-
-# Function to merge audio and video into a final video file using moviepy
-def merge_audio_video(video_path, audio_path, output_path="output.mp4"):
-    try:
-        logging.info("Merging audio and video using moviepy...")
-        video_clip = VideoFileClip(video_path)
-        audio_clip = AudioFileClip(audio_path)
-        final_clip = video_clip.set_audio(audio_clip)
-        final_clip.write_videofile(output_path, codec="libx264", audio_codec="aac")
-        logging.info(f"Final video saved to: {output_path}")
-        return output_path
-    except Exception as e:
-        logging.error(f"Error merging audio and video: {e}")
-        return None
-
-# Main processing function to handle video upload, scene analysis, and video output
-def process_video(video_path, progress=gr.Progress()):
-    try:
-        progress(0.1, desc="Starting video processing...")
-        logging.info("Starting video processing...")
-
-        # Analyze the video to determine the scene type
-        progress(0.3, desc="Analyzing video...")
-        scene_type = analyze_video(video_path)
-
-        # Generate audio using AudioGen Medium
-        progress(0.5, desc="Generating audio...")
-        audio_path = generate_audio_audiogen(scene_type, duration=10)
-
-        # Generate music using MusicGen Medium
-        progress(0.7, desc="Generating music...")
-        music_path = generate_music_musicgen(scene_type, duration=10)
-
-        # Merge the generated audio with the video and output the final video
-        progress(0.9, desc="Merging audio and video...")
-        output_path = merge_audio_video(video_path, music_path)
-        if not output_path:
-            return "Error: Failed to merge audio and video.", "Logs: Merge failed."
-
-        logging.info("Video processing completed successfully.")
-        return output_path, "Logs: Processing completed."
-    except Exception as e:
-        logging.error(f"Error in process_video: {e}")
-        return f"An error occurred during processing: {e}", f"Logs: {e}"
-
-# Gradio UI for video upload
-def gradio_interface(video_file, progress=gr.Progress()):
-    try:
-        progress(0.1, desc="Starting video processing...")
-        logging.info("Gradio interface triggered.")
-        output_video, logs = process_video(video_file, progress)
-        return output_video, logs
-    except Exception as e:
-        logging.error(f"Error in Gradio interface: {e}")
-        return f"An error occurred: {e}", f"Logs: {e}"
-
-# Launch Gradio app
-try:
-    logging.info("Launching Gradio app...")
-    interface = gr.Interface(
-        fn=gradio_interface,
-        inputs=[gr.Video(label="Upload Video")],
-        outputs=[gr.Video(label="Output Video with Generated Audio"), gr.Textbox(label="Logs", lines=10)],
-        title="Video to Video with Generated Audio and Music",
-        description="Upload a video, and this app will analyze it and generate matching audio and music using AudioGen Medium and MusicGen Medium."
-    )
-    interface.queue()  # Enable queue for long-running tasks
-    interface.launch(share=True)  # Launch the app
-except Exception as e:
-    logging.error(f"Error launching Gradio app: {e}")
-    raise
+# Rest of the code remains the same...