import os
import gradio as gr
import torch
import numpy as np
from PIL import Image
import trimesh
from diffusers import Zero123Pipeline
import tempfile

# Check if CUDA is available, otherwise use CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# Initialize the pipeline
pipe = Zero123Pipeline.from_pretrained(
    "bennyguo/zero123-xl-diffusers",
    torch_dtype=torch.float16 if device.type == "cuda" else torch.float32,
).to(device)

def image_to_3d(input_image, num_inference_steps=75, guidance_scale=3.0):
    """
    Convert a single image to a 3D model
    """
    # Preprocess image
    if input_image is None:
        return None
    
    input_image = input_image.convert("RGB").resize((256, 256))
    
    # Generate multiple views using Zero123
    images = []
    
    # Generate views from different angles
    for elevation in [0, 30]:
        for azimuth in [0, 90, 180, 270]:
            print(f"Generating view: elevation={elevation}, azimuth={azimuth}")
            with torch.no_grad():
                image = pipe(
                    image=input_image,
                    elevation=elevation,
                    azimuth=azimuth,
                    num_inference_steps=num_inference_steps,
                    guidance_scale=guidance_scale,
                ).images[0]
                images.append(np.array(image))
    
    # Create point cloud from multiple views
    # This is a simplified approach - in production you might want to use a more sophisticated method
    points = []
    for i, img in enumerate(images):
        # Extract depth information (simplified approach)
        gray = np.mean(img, axis=2)
        # Sample points from the image
        h, w = gray.shape
        for y in range(0, h, 4):
            for x in range(0, w, 4):
                depth = gray[y, x] / 255.0  # Normalize depth
                
                # Convert to 3D point based on view angle
                angle_idx = i % 4
                elevation = 0 if i < 4 else 30
                azimuth = angle_idx * 90
                
                # Convert to radians
                elevation_rad = elevation * np.pi / 180
                azimuth_rad = azimuth * np.pi / 180
                
                # Calculate 3D position based on spherical coordinates
                z = depth * np.cos(elevation_rad) * np.cos(azimuth_rad)
                x = depth * np.cos(elevation_rad) * np.sin(azimuth_rad)
                y = depth * np.sin(elevation_rad)
                
                points.append([x, y, z])
    
    # Create a point cloud
    point_cloud = np.array(points)
    
    # Save point cloud to OBJ file
    with tempfile.NamedTemporaryFile(suffix='.obj', delete=False) as tmp_file:
        mesh = trimesh.points.PointCloud(point_cloud)
        mesh.export(tmp_file.name)
        
        # Also export as PLY for better compatibility
        ply_path = tmp_file.name.replace('.obj', '.ply')
        mesh.export(ply_path)
        
        return [tmp_file.name, ply_path]

def process_image(image, num_steps, guidance):
    try:
        model_paths = image_to_3d(image, num_inference_steps=num_steps, guidance_scale=guidance)
        if model_paths:
            return model_paths[0], model_paths[1], "3D model generated successfully!"
        else:
            return None, None, "Failed to process the image."
    except Exception as e:
        return None, None, f"Error: {str(e)}"

# Create Gradio interface
with gr.Blocks(title="Image to 3D Model Converter") as demo:
    gr.Markdown("# Image to 3D Model Converter")
    gr.Markdown("Upload an image to convert it to a 3D model that you can use in Unity or other engines.")
    
    with gr.Row():
        with gr.Column(scale=1):
            input_image = gr.Image(type="pil", label="Input Image")
            num_steps = gr.Slider(minimum=20, maximum=100, value=75, step=5, label="Number of Inference Steps")
            guidance = gr.Slider(minimum=1.0, maximum=7.0, value=3.0, step=0.5, label="Guidance Scale")
            submit_btn = gr.Button("Convert to 3D")
        
        with gr.Column(scale=1):
            obj_file = gr.File(label="OBJ File")
            ply_file = gr.File(label="PLY File")
            output_message = gr.Textbox(label="Output Message")
    
    submit_btn.click(
        fn=process_image,
        inputs=[input_image, num_steps, guidance],
        outputs=[obj_file, ply_file, output_message]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)