Delete app_legacy.py

app_legacy.py

@@ -1,105 +0,0 @@
-import os
-import tempfile
-import torch
-import numpy as np
-import gradio as gr
-from PIL import Image
-import cv2
-from diffusers import DiffusionPipeline
-from script import SatelliteModelGenerator
-
-# Initialize models and device
-device = "cuda" if torch.cuda.is_available() else "cpu"
-dtype = torch.bfloat16
-
-repo_id = "black-forest-labs/FLUX.1-dev"
-adapter_id = "jbilcke-hf/flux-satellite"
-
-flux_pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
-flux_pipe.load_lora_weights(adapter_id)
-flux_pipe = flux_pipe.to(device)
-
-def generate_and_process_map(prompt: str) -> str | None:
-    """Generate satellite image from prompt and convert to 3D model."""
-    try:
-        # Set dimensions
-        width = height = 1024
-        
-        # Generate random seed
-        seed = np.random.randint(0, np.iinfo(np.int32).max)
-        
-        # Set random seeds
-        torch.manual_seed(seed)
-        np.random.seed(seed)
-        
-        # Generate satellite image using FLUX
-        generator = torch.Generator(device=device).manual_seed(seed)
-        generated_image = flux_pipe(
-            prompt=prompt,
-            width=width,
-            height=height,
-            num_inference_steps=30,
-            generator=generator,
-            guidance_scale=7.5
-        ).images[0]
-        
-        # Convert PIL Image to OpenCV format
-        cv_image = cv2.cvtColor(np.array(generated_image), cv2.COLOR_RGB2BGR)
-        
-        # Initialize SatelliteModelGenerator
-        generator = SatelliteModelGenerator(building_height=0.09)
-        
-        # Process image
-        print("Segmenting image...")
-        segmented_img = generator.segment_image(cv_image, window_size=5)
-        
-        print("Estimating heights...")
-        height_map = generator.estimate_heights(cv_image, segmented_img)
-        
-        # Generate mesh
-        print("Generating mesh...")
-        mesh = generator.generate_mesh(height_map, cv_image, add_walls=True)
-        
-        # Export to GLB
-        temp_dir = tempfile.mkdtemp()
-        output_path = os.path.join(temp_dir, 'output.glb')
-        mesh.export(output_path)
-        
-        return output_path
-        
-    except Exception as e:
-        print(f"Error during generation: {str(e)}")
-        import traceback
-        traceback.print_exc()
-        return None
-
-# Create Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("# Text to Map")
-    gr.Markdown("Generate 3D maps from text descriptions using FLUX and mesh generation.")
-    
-    with gr.Row():
-        prompt_input = gr.Text(
-            label="Enter your prompt",
-            placeholder="eg. satellite view of downtown Manhattan"
-        )
-    
-    with gr.Row():
-        generate_btn = gr.Button("Generate", variant="primary")
-    
-    with gr.Row():
-        model_output = gr.Model3D(
-            label="Generated 3D Map",
-            clear_color=[0.0, 0.0, 0.0, 0.0],
-        )
-    
-    # Event handler
-    generate_btn.click(
-        fn=generate_and_process_map,
-        inputs=[prompt_input],
-        outputs=[model_output],
-        api_name="generate"
-    )
-
-if __name__ == "__main__":
-    demo.queue().launch()