| import gradio as gr | |
| from datasets import load_dataset | |
| from transformers import pipeline | |
| # Load the dataset | |
| dataset = load_dataset("nyuuzyou/klingai") | |
| # Load your model (replace 'path-to-your-model' with the actual model path or identifier) | |
| model = pipeline('image-to-video', model='path-to-your-model') | |
| def generate_video(index): | |
| # Access the sample at the given index | |
| sample = dataset["train"][index] | |
| # Extract the image URL or path from the sample | |
| image_url = sample["resource"]["url"] # Adjust the key based on the dataset's structure | |
| # Implement your model's logic to generate video from the image | |
| # For now, we'll return a placeholder path | |
| video_path = "generated_video.mp4" # Replace with actual video generation logic | |
| return video_path | |
| # Set up the Gradio interface | |
| iface = gr.Interface(fn=generate_video, inputs="number", outputs="video") | |
| # Launch the interface | |
| iface.launch() |