import gradio as gr def greet(name): return "Hello " + name + "!!" demo = gr.Interface(fn=greet, inputs="text", outputs="text") demo.launch() import os from moviepy.editor import VideoFileClip, concatenate_videoclips from transformers import VideoProcessor, VideoModel # Replace with actual imports if needed # Function to load the Hugging Face model and processor def load_model(model_name): try: model = VideoModel.from_pretrained(model_name) processor = VideoProcessor.from_pretrained(model_name) return model, processor except Exception as e: print(f"Error loading model: {e}") return None, None # Function to generate the crossover video def generate_crossover_video(video1_path, video2_path, output_path): if not os.path.isfile(video1_path): print(f"Error: {video1_path} does not exist.") return print(f"Processing Video 1: {video1_path}") if not os.path.isfile(video2_path): print(f"Error: {video2_path} does not exist.") return print(f"Processing Video 2: {video2_path}") # Load video clips clip1 = VideoFileClip(video1_path) clip2 = VideoFileClip(video2_path) # For now, just concatenating the clips directly final_clip = concatenate_videoclips([clip1, clip2]) # Write the output video file final_clip.write_videofile(output_path, codec='libx264') print(f"Crossover video saved to {output_path}") def main(): video1_path = input("Enter the path to the first video: ") video2_path = input("Enter the path to the second video: ") output_path = input("Enter the output file path (e.g., crossover_output.mp4): ") model_name = "your_model_name" # Replace with the actual model name you want to use model, processor = load_model(model_name) if model is None or processor is None: print("Model loading failed. Exiting the application.") return generate_crossover_video(video1_path, video2_path, output_path) if __name__ == "__main__": main()