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PromptTrack

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# 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 gradio as gr
import shutil
import os
import subprocess
import sys
# Run the .bat file before launching the app
"""try:
    import PromptTrack
except ImportError:
    print("PromptTrack not found. Installing...")
    subprocess.run([sys.executable, "-m", "pip", "install", 
                    "--index-url", "https://test.pypi.org/simple/", 
                    "--extra-index-url", "https://pypi.org/simple/", 
                    "PromptTrack"], check=True)
    subprocess.run([sys.executable, "-m", "pip", "install", 
                    "--no-deps", "bytetracker"], check=True)
    import PromptTrack  # Retry import after installation


from PromptTrack import PromptTracker
tracker = PromptTracker()"""
def process_video(video_path, prompt):
    detection_threshold=0.3
    track_thresh=0.4
    match_thresh=1
    max_time_lost=float("inf")
    nbr_frames_fixing=800
    output_video = video_path.split('mp4')[0]+"_with_id.mp4"  # Placeholder for processed video
    output_file = video_path.split('mp4')[0]+"_mot_.json"    # Tracking result
    output_file_2 = video_path.split('mp4')[0]+"_object_detection.json"    # detection results
    video_file = video_path
    """tracker.detect_objects(video_file, prompt=prompt, nms_threshold=0.8, detection_threshold=detection_threshold, detector="OWL-VITV2")
    tracker.process_mot(video_file, fixed_parc=True, track_thresh=track_thresh, match_thresh=match_thresh, frame_rate=25, max_time_lost=max_time_lost, nbr_frames_fixing=nbr_frames_fixing)
    tracker.read_video_with_mot(video_file, fps=25)"""
    
    
    output_video = "output.mp4"  # Placeholder for processed video
    output_file = "output.txt"    # Placeholder for generated file
    
    
    # Copy the input video to simulate processing
    shutil.copy(video_path.name, output_video)
    
    # Create an output text file with the prompt content
    with open(output_file, "w") as f:
        f.write(f"User Prompt: {prompt}\n")
    
    return output_video, output_file

# Define Gradio interface
iface = gr.Interface(
    fn=process_video,
    inputs=[gr.File(label="Upload Video"), gr.Textbox(placeholder="Enter your prompt")],
    outputs=[gr.Video(), gr.File(label="Generated File")],
    title="Video Processing App",
    description="Upload a video and enter a prompt. The app will return the processed video and a generated file."
)


# Launch the app
if __name__ == "__main__":
    iface.launch(share=True)


'''
import gradio as gr
import shutil
import os

def process_video(video, prompt):
    output_video = "output.mp4"  # Placeholder for processed video
    output_file = "output.txt"    # Placeholder for generated file
    
    # Copy the input video to simulate processing
    shutil.copy(video.name, output_video)
    
    # Create an output text file with the prompt content
    with open(output_file, "w") as f:
        f.write(f"User Prompt: {prompt}\n")
    
    return output_video, output_file

# Define Gradio interface
iface = gr.Interface(
    fn=process_video,
    inputs=[gr.File(label="Upload Video"), gr.Textbox(placeholder="Enter your prompt")],
    outputs=[gr.Video(), gr.File(label="Generated File")],
    title="Video Processing App",
    description="Upload a video and enter a prompt. The app will return the processed video and a generated file."
)

# Launch the app
if __name__ == "__main__":
    iface.launch(share=True)'''