Update README.md

README.md

@@ -127,4 +127,35 @@ def generate_crossover_video(video1_path, video2_path, output_path):
 generate_crossover_video("path/to/mario_video1.mp4", "path/to/mario_video2.mp4", "path/to/output_crossover_video.mp4")
 combine_mario_videos("path/to/mario_video1.mp4", "path/to/mario_video2.mp4", "path/to/final_output_video.mp4")
 
+# Load the model and processor
+model = VideoGPT.from_pretrained("huggingface/video-gpt")  # Replace with an actual pre-trained model
+processor = VideoProcessor.from_pretrained("huggingface/video-gpt")  # Replace with an actual pre-trained processor
+
+import torch
+from transformers import VideoGPT, VideoProcessor  # Note: These are hypothetical models
+from moviepy.editor import VideoFileClip, concatenate_videoclips
+
+def generate_crossover_video(video1_path, video2_path, output_path):
+    # Load and process the input videos
+    video1 = processor(video1_path)
+    video2 = processor(video2_path)
+
+    # Generate a crossover video
+    with torch.no_grad():
+        crossover_video = model.generate(video1, video2)
+
+    # Save the generated video
+    crossover_video.save(output_path)
+
+    def combine_minecraft_videos(video1_path, video2_path, output_path):
+    clip1 = VideoFileClip(video1_path).subclip(0, 10)  # Take the first 10 seconds of video1
+    clip2 = VideoFileClip(video2_path).subclip(0, 10)  # Take the first 10 seconds of video2
+
+    final_clip = concatenate_videoclips([clip1, clip2])
+    final_clip.write_videofile(output_path, codec="libx264")
+
+    # Example usage
+generate_crossover_video("path/to/minecraft_video1.mp4", "path/to/minecraft_video2.mp4", "path/to/output_crossover_video.mp4")
+combine_minecraft_videos("path/to/minecraft_video1.mp4", "path/to/minecraft_video2.mp4", "path/to/final_output_video.mp4")
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference