Delete apps/old-gradio_app.py

apps/old-gradio_app.py

@@ -1,177 +0,0 @@
-import os
-import sys
-import subprocess
-import gradio as gr
-import torch
-import random
-
-# Add the project root directory to the Python path
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-
-from src.controlnet_image_generator.infer import infer
-
-def run_setup_script():
-    setup_script = os.path.join(os.path.dirname(__file__), "gradio_app", "setup_scripts.py")
-    try:
-        result = subprocess.run(["python", setup_script], capture_output=True, text=True, check=True)
-        return result.stdout
-    except subprocess.CalledProcessError as e:
-        print(f"Setup script failed with error: {e.stderr}")
-        return f"Setup script failed: {e.stderr}"
-
-def run_inference(
-    input_image,
-    prompt,
-    negative_prompt,
-    num_steps,
-    seed,
-    width,
-    height,
-    guidance_scale,
-    controlnet_conditioning_scale,
-    use_random_seed=False,
-):
-    config_path = "configs/model_ckpts.yaml"
-    
-    if use_random_seed:
-        seed = random.randint(0, 2 ** 32)
-    
-    try:
-        result = infer(
-            config_path=config_path,
-            input_image=input_image,
-            image_url=None,
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            num_steps=num_steps,
-            seed=seed,
-            width=width,
-            height=height,
-            guidance_scale=guidance_scale,
-            controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-        )
-        result = list(result)[0]
-        return result, "Inference completed successfully"
-    except Exception as e:
-        return [], f"Error during inference: {str(e)}"
-
-def stop_app():
-    """Function to stop the Gradio app."""
-    try:
-        gr.Interface.close_all()  # Attempt to close all running Gradio interfaces
-        return "Application stopped successfully."
-    except Exception as e:
-        return f"Error stopping application: {str(e)}"
-
-def create_gui():
-    cuscustom_css = open("apps/gradio_app/static/style.css").read()
-    with gr.Blocks(css=cuscustom_css) as demo:
-        gr.Markdown("# ControlNet Image Generation with Pose Detection")
-        
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(type="filepath", label="Input Image")
-                prompt = gr.Textbox(
-                    label="Prompt",
-                    value="a man is doing yoga"
-                )
-                negative_prompt = gr.Textbox(
-                    label="Negative Prompt",
-                    value="monochrome, lowres, bad anatomy, worst quality, low quality"
-                )
-                
-                with gr.Row():
-                    width = gr.Slider(
-                        minimum=256,
-                        maximum=1024,
-                        value=512,
-                        step=64,
-                        label="Width"
-                    )
-                    height = gr.Slider(
-                        minimum=256,
-                        maximum=1024,
-                        value=512,
-                        step=64,
-                        label="Height"
-                    )
-                
-                with gr.Accordion("Advanced Settings", open=False):
-                    num_steps = gr.Slider(
-                        minimum=1,
-                        maximum=100,
-                        value=30,
-                        step=1,
-                        label="Number of Inference Steps"
-                    )
-                    use_random_seed = gr.Checkbox(label="Use Random Seed", value=False)
-                    seed = gr.Slider(
-                        minimum=0,
-                        maximum=2**32,
-                        value=42,
-                        step=1,
-                        label="Random Seed",
-                        visible=True
-                    )
-                    
-                    guidance_scale = gr.Slider(
-                        minimum=1.0,
-                        maximum=20.0,
-                        value=7.5,
-                        step=0.1,
-                        label="Guidance Scale"
-                    )
-                    controlnet_conditioning_scale = gr.Slider(
-                        minimum=0.0,
-                        maximum=1.0,
-                        value=1.0,
-                        step=0.1,
-                        label="ControlNet Conditioning Scale"
-                    )  
-            
-            with gr.Column():
-                output_images = gr.Image(label="Generated Images")
-                output_message = gr.Textbox(label="Status")
-                
-                # with gr.Row():
-                submit_button = gr.Button("Generate Images", elem_classes="submit-btn")
-                stop_button = gr.Button("Stop Application", elem_classes="stop-btn")
-
-        def update_seed_visibility(use_random):
-            return gr.update(visible=not use_random)
-        
-        use_random_seed.change(
-            fn=update_seed_visibility,
-            inputs=use_random_seed,
-            outputs=seed
-        )
-        
-        submit_button.click(
-            fn=run_inference,
-            inputs=[
-                input_image,
-                prompt,
-                negative_prompt,
-                num_steps,
-                seed,
-                width,
-                height,
-                guidance_scale,
-                controlnet_conditioning_scale,
-                use_random_seed,
-            ],
-            outputs=[output_images, output_message]
-        )
-        
-        stop_button.click(
-            fn=stop_app,
-            inputs=[],
-            outputs=[output_message]
-        )
-
-    return demo
-
-if __name__ == "__main__":
-    run_setup_script()
-    demo = create_gui()
-    demo.launch(share=True)