Update app.py

app.py

@@ -5,21 +5,23 @@ from transformers import pipeline
 # Load the dataset
 dataset = load_dataset("nyuuzyou/klingai")
 
-# For demonstration, let's access the first item in the dataset
-sample = dataset["train"][0]
-image_path = sample["image"]  # Adjust the key based on the dataset's structure
-
 # 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(image):
+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="image", outputs="video")
+iface = gr.Interface(fn=generate_video, inputs="number", outputs="video")
 
 # Launch the interface
-iface.launch()
+iface.launch()