Update app.py

app.py

@@ -1,165 +1,4 @@
-# import gradio as gr
-# import numpy as np
-# import random
-
-# # import spaces #[uncomment to use ZeroGPU]
-# from diffusers import DiffusionPipeline
-# import torch
-
-# device = "cuda" if torch.cuda.is_available() else "cpu"
-# model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-# if torch.cuda.is_available():
-#     torch_dtype = torch.float16
-# else:
-#     torch_dtype = torch.float32
-
-# pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-# pipe = pipe.to(device)
-
-# MAX_SEED = np.iinfo(np.int32).max
-# MAX_IMAGE_SIZE = 1024
-
-
-# # @spaces.GPU #[uncomment to use ZeroGPU]
-# def infer(
-#     prompt,
-#     negative_prompt,
-#     seed,
-#     randomize_seed,
-#     width,
-#     height,
-#     guidance_scale,
-#     num_inference_steps,
-#     progress=gr.Progress(track_tqdm=True),
-# ):
-#     if randomize_seed:
-#         seed = random.randint(0, MAX_SEED)
-
-#     generator = torch.Generator().manual_seed(seed)
-
-#     image = pipe(
-#         prompt=prompt,
-#         negative_prompt=negative_prompt,
-#         guidance_scale=guidance_scale,
-#         num_inference_steps=num_inference_steps,
-#         width=width,
-#         height=height,
-#         generator=generator,
-#     ).images[0]
-
-#     return image, seed
-
-
-# examples = [
-#     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-#     "An astronaut riding a green horse",
-#     "A delicious ceviche cheesecake slice",
-# ]
-
-# css = """
-# #col-container {
-#     margin: 0 auto;
-#     max-width: 640px;
-# }
-# """
-
-# with gr.Blocks(css=css) as demo:
-#     with gr.Column(elem_id="col-container"):
-#         gr.Markdown(" # Text-to-Image Gradio Template")
-
-#         with gr.Row():
-#             prompt = gr.Text(
-#                 label="Prompt",
-#                 show_label=False,
-#                 max_lines=1,
-#                 placeholder="Enter your prompt",
-#                 container=False,
-#             )
-
-#             run_button = gr.Button("Run", scale=0, variant="primary")
-
-#         result = gr.Image(label="Result", show_label=False)
-
-#         with gr.Accordion("Advanced Settings", open=False):
-#             negative_prompt = gr.Text(
-#                 label="Negative prompt",
-#                 max_lines=1,
-#                 placeholder="Enter a negative prompt",
-#                 visible=False,
-#             )
-
-#             seed = gr.Slider(
-#                 label="Seed",
-#                 minimum=0,
-#                 maximum=MAX_SEED,
-#                 step=1,
-#                 value=0,
-#             )
-
-#             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-#             with gr.Row():
-#                 width = gr.Slider(
-#                     label="Width",
-#                     minimum=256,
-#                     maximum=MAX_IMAGE_SIZE,
-#                     step=32,
-#                     value=1024,  # Replace with defaults that work for your model
-#                 )
-
-#                 height = gr.Slider(
-#                     label="Height",
-#                     minimum=256,
-#                     maximum=MAX_IMAGE_SIZE,
-#                     step=32,
-#                     value=1024,  # Replace with defaults that work for your model
-#                 )
-
-#             with gr.Row():
-#                 guidance_scale = gr.Slider(
-#                     label="Guidance scale",
-#                     minimum=0.0,
-#                     maximum=10.0,
-#                     step=0.1,
-#                     value=0.0,  # Replace with defaults that work for your model
-#                 )
-
-#                 num_inference_steps = gr.Slider(
-#                     label="Number of inference steps",
-#                     minimum=1,
-#                     maximum=50,
-#                     step=1,
-#                     value=2,  # Replace with defaults that work for your model
-#                 )
-
-#         gr.Examples(examples=examples, inputs=[prompt])
-#     gr.on(
-#         triggers=[run_button.click, prompt.submit],
-#         fn=infer,
-#         inputs=[
-#             prompt,
-#             negative_prompt,
-#             seed,
-#             randomize_seed,
-#             width,
-#             height,
-#             guidance_scale,
-#             num_inference_steps,
-#         ],
-#         outputs=[result, seed],
-#     )
-
-# if __name__ == "__main__":
-#     demo.launch(share=True)
-
-
-import torch
-print(f"Is CUDA available: {torch.cuda.is_available()}")
-# True
-print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
-# Tesla T4
-
+import spaces
 import gradio as gr
 import shutil
 import os
@@ -181,7 +20,15 @@ except ImportError:
 
 from PromptTrack import PromptTracker
 tracker = PromptTracker()
+
+
+@spaces.GPU(duration=300)
 def process_video(video_path, prompt):
+    import torch
+    print(f"Is CUDA available: {torch.cuda.is_available()}")
+    # True
+    print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
+    # Tesla T4
     detection_threshold=0.3
     track_thresh=0.4
     match_thresh=1