# 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)