import gradio as gr import os def predict_video(input_video, input_audio=None): global filename, file_size # Use the global keyword to refer to the global variables # Check if the video is available if input_video is None: return [None, "Please upload a video"] filename = input_video.name # Get the uploaded filename file_size = os.path.getsize(input_video.name) # Get the file size in bytes # Loop until a valid video is uploaded if not filename.lower().endswith('.mp4'): return [None, "Error: Please upload an MP4 video file."] if file_size > 16 * 1024 * 1024: return [None, "Error: The upload exceeds file size 16MB. Please upload a smaller file."] """ Processes the uploaded video (replace with your video analysis logic). Args: input_video: The uploaded video file object. input_audio (optional): The uploaded audio file object (MP3). Returns: A list containing the processed video and a message string. """ # Placeholder processing (replace with actual video analysis) message = "**Placeholder:** Video processing not implemented yet." # You can optionally add a progress bar or loading indicator here if input_audio is None: return [input_video, message + " Generated Audio will be used"] return [input_video, message + f" Using uploaded audio: {input_audio.name}"] css = """ #col-container { margin: 0 auto; max-width: 800px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.HTML("""
Instructions:
Step 1: Upload your video.
Step 2: (Optional) Upload an MP3 audio track.
Step 3: We'll analyze the video and suggest explosion timeframes using Azure Cognitive Services (not included yet).
Step 4: Download haptic explosion audio from [link to audio source].
Step 5: Mix the Audio using any app of your choice and master the audio with an AI mastering program (links provided).