completed

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+rU7m2Ys6xt0nUW7/vGT1M0NPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV
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+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

app.py

@@ -1,251 +1,251 @@
-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],
-    )
+# 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)
+# 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)
-#     """
+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
     
+    '''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)
+    '''
+    # 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")
+    # 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
+    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."
-# )
+# 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()
-# '''
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
 
 
-# import gradio as gr
-# import shutil
-# import os
+'''
+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
+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)
+    # 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")
+    # 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)
+    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)'''

requirements.txt

@@ -1,4 +1,5 @@
 
+gradio>=4.0.0
 accelerate
 diffusers
 invisible_watermark