Delete app.py

app.py

@@ -1,214 +0,0 @@
-import torch
-import gradio as gr
-from diffusers import ShapEPipeline, ShapEImg2ImgPipeline
-from diffusers.utils import export_to_gif
-import os
-from huggingface_hub import HfApi, login
-from PIL import Image
-import numpy as np
-import gc
-
-# Force CPU usage
-device = "cpu"
-torch.set_num_threads(4)
-print(f"Using device: {device}")
-
-def validate_token(token):
-    try:
-        login(token=token)
-        return True
-    except Exception as e:
-        print(f"Token validation error: {str(e)}")
-        return False
-
-def generate_3d_from_text(prompt, token, guidance_scale=7.0, export_format="obj", progress=gr.Progress()):
-    try:
-        if not validate_token(token):
-            return gr.update(value="Invalid Hugging Face token"), None, None
-            
-        print(f"Starting generation: {prompt}")
-        progress(0.1, desc="Loading model...")
-        
-        pipe = ShapEPipeline.from_pretrained(
-            "openai/shap-e",
-            torch_dtype=torch.float32,
-            token=token,
-            revision="main",
-            low_cpu_mem_usage=True
-        )
-        
-        os.makedirs("outputs", exist_ok=True)
-        safe_prompt = "".join(x for x in prompt if x.isalnum() or x in (" ", "-", "_"))
-        base_filename = f"outputs/{safe_prompt}"
-        
-        try:
-            progress(0.3, desc="Creating 3D model...")
-            with torch.no_grad():
-                output = pipe(
-                    prompt,
-                    guidance_scale=min(guidance_scale, 10.0),
-                    num_inference_steps=16
-                )
-                
-                progress(0.5, desc="Creating GIF...")
-                gif_path = export_to_gif(output.images, f"{base_filename}.gif")
-                
-                progress(0.7, desc="Creating 3D mesh...")
-                mesh_output = pipe(
-                    prompt,
-                    guidance_scale=min(guidance_scale, 10.0),
-                    num_inference_steps=16,
-                    output_type="mesh"
-                )
-            
-            progress(0.9, desc="Saving files...")
-            output_path = f"{base_filename}.{export_format}"
-            mesh_output.meshes[0].export(output_path)
-            
-            del pipe
-            del output
-            del mesh_output
-            gc.collect()
-            
-            print(f"Generation completed: {output_path}")
-            progress(1.0, desc="Completed!")
-            return gr.update(value="Generation successful!"), gr.update(value=gif_path), gr.update(value=output_path)
-            
-        except Exception as model_error:
-            error_msg = f"Model execution error: {str(model_error)}"
-            print(error_msg)
-            return gr.update(value=error_msg), None, None
-        
-    except Exception as e:
-        error_msg = f"General error: {str(e)}"
-        print(error_msg)
-        return gr.update(value=error_msg), None, None
-
-def generate_3d_from_image(image, token, guidance_scale=7.0, export_format="obj", progress=gr.Progress()):
-    try:
-        if not validate_token(token):
-            return gr.update(value="Invalid Hugging Face token"), None, None
-            
-        print("Starting image to 3D generation")
-        progress(0.1, desc="Loading model...")
-        
-        pipe = ShapEImg2ImgPipeline.from_pretrained(
-            "openai/shap-e-img2img",
-            torch_dtype=torch.float32,
-            token=token,
-            revision="main",
-            low_cpu_mem_usage=True
-        )
-        
-        os.makedirs("outputs", exist_ok=True)
-        
-        import time
-        timestamp = int(time.time())
-        base_filename = f"outputs/image_to_3d_{timestamp}"
-        
-        try:
-            progress(0.3, desc="Preparing image...")
-            if isinstance(image, str):
-                image = Image.open(image)
-            elif isinstance(image, np.ndarray):
-                image = Image.fromarray(image)
-                
-            image = image.resize((128, 128))
-            
-            progress(0.5, desc="Creating 3D model...")
-            with torch.no_grad():
-                output = pipe(
-                    image=image,
-                    guidance_scale=min(guidance_scale, 10.0),
-                    num_inference_steps=16
-                )
-                
-                progress(0.7, desc="Creating GIF...")
-                gif_path = export_to_gif(output.images, f"{base_filename}.gif")
-                
-                progress(0.8, desc="Creating 3D mesh...")
-                mesh_output = pipe(
-                    image=image,
-                    guidance_scale=min(guidance_scale, 10.0),
-                    num_inference_steps=16,
-                    output_type="mesh"
-                )
-            
-            progress(0.9, desc="Saving files...")
-            output_path = f"{base_filename}.{export_format}"
-            mesh_output.meshes[0].export(output_path)
-            
-            del pipe
-            del output
-            del mesh_output
-            gc.collect()
-            
-            print(f"Generation completed: {output_path}")
-            progress(1.0, desc="Completed!")
-            return gr.update(value="Generation successful!"), gr.update(value=gif_path), gr.update(value=output_path)
-            
-        except Exception as model_error:
-            error_msg = f"Model execution error: {str(model_error)}"
-            print(error_msg)
-            return gr.update(value=error_msg), None, None
-        
-    except Exception as e:
-        error_msg = f"General error: {str(e)}"
-        print(error_msg)
-        return gr.update(value=error_msg), None, None
-
-with gr.Blocks(theme=gr.themes.Soft()) as interface:
-    gr.Markdown("# SORA-3D - Text/Image to 3D Model Generator")
-    gr.Markdown("Create 3D models from text or image input. You need a Hugging Face token to use this app.")
-    gr.Markdown("""
-    > **Important Notes**: 
-    > - Processing time may be longer on CPU
-    > - Keep guidance scale under 10 for faster results
-    > - Number of steps is fixed at 16
-    > - Image size is optimized for quality/speed
-    """)
-    
-    with gr.Tab("Text → 3D"):
-        with gr.Row():
-            with gr.Column():
-                text_input = gr.Textbox(label="Enter description for 3D model", scale=2)
-                text_token = gr.Textbox(label="Hugging Face Token", type="password", scale=2)
-                with gr.Row():
-                    text_guidance = gr.Slider(minimum=1, maximum=10, value=7, label="Guidance Scale", scale=1)
-                    text_format = gr.Radio(["obj", "glb"], label="Export Format", value="obj", scale=1)
-                text_button = gr.Button("Generate", variant="primary")
-            
-            with gr.Column():
-                text_status = gr.Textbox(label="Status", interactive=False)
-                text_preview = gr.Image(label="3D Preview (GIF)", interactive=False)
-                text_file = gr.File(label="3D Model File")
-    
-    with gr.Tab("Image → 3D"):
-        with gr.Row():
-            with gr.Column():
-                image_input = gr.Image(label="Image to convert to 3D", type="pil", scale=2)
-                image_token = gr.Textbox(label="Hugging Face Token", type="password", scale=2)
-                with gr.Row():
-                    image_guidance = gr.Slider(minimum=1, maximum=10, value=7, label="Guidance Scale", scale=1)
-                    image_format = gr.Radio(["obj", "glb"], label="Export Format", value="obj", scale=1)
-                image_button = gr.Button("Generate", variant="primary")
-            
-            with gr.Column():
-                image_status = gr.Textbox(label="Status", interactive=False)
-                image_preview = gr.Image(label="3D Preview (GIF)", interactive=False)
-                image_file = gr.File(label="3D Model File")
-    
-    text_button.click(
-        generate_3d_from_text,
-        inputs=[text_input, text_token, text_guidance, text_format],
-        outputs=[text_status, text_preview, text_file]
-    )
-    
-    image_button.click(
-        generate_3d_from_image,
-        inputs=[image_input, image_token, image_guidance, image_format],
-        outputs=[image_status, image_preview, image_file]
-    )
-
-if __name__ == "__main__":
-    interface.launch()