import torch
import gradio as gr
import numpy as np
from PIL import Image
import trimesh
import cv2
import open3d as o3d

# Critical Model Imports
from transformers import pipeline, AutoFeatureExtractor, AutoModelForImageToImage
from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel
from huggingface_hub import hf_hub_download

class CompleteMeshGenerator:
    def __init__(self):
        # Critical Model Configuration
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
        # Depth Estimation Model
        try:
            self.depth_estimator = pipeline(
                "depth-estimation", 
                model="Intel/dpt-large",
                device=self.device
            )
        except Exception as e:
            print(f"Depth Estimation Model Load Error: {e}")
            self.depth_estimator = None
        
        # Multi-View Generation Setup
        try:
            # Load ControlNet for multi-view generation
            self.controlnet = ControlNetModel.from_pretrained(
                "lllyasviel/control_v11f1p_sd15_depth", 
                torch_dtype=torch.float32
            )
            
            self.multi_view_pipeline = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
                "runwayml/stable-diffusion-v1-5", 
                controlnet=self.controlnet,
                torch_dtype=torch.float32
            ).to(self.device)
        except Exception as e:
            print(f"Multi-View Generation Model Load Error: {e}")
            self.multi_view_pipeline = None

    def generate_depth_map(self, image):
        """
        Advanced Depth Map Generation
        """
        if self.depth_estimator is None:
            raise ValueError("Depth estimation model not loaded")
        
        # Ensure image is in correct format
        if isinstance(image, np.ndarray):
            image = Image.fromarray(image)
        
        # Estimate depth
        depth_result = self.depth_estimator(image)
        depth_map = np.array(depth_result['depth'])
        
        return depth_map

    def generate_multi_view_images(self, input_image, num_views=4):
        """
        Generate Multiple View Images
        """
        if self.multi_view_pipeline is None:
            raise ValueError("Multi-view generation pipeline not loaded")
        
        # Estimate initial depth map
        depth_map = self.generate_depth_map(input_image)
        
        # Convert depth map to PIL Image
        depth_image = Image.fromarray((depth_map * 255).astype(np.uint8))
        
        # View generation parameters
        view_angles = [
            (30, "Side view"),
            (150, "Opposite side"),
            (90, "Top view"),
            (270, "Bottom view")
        ]
        
        multi_view_images = []
        
        for angle, description in view_angles[:num_views]:
            try:
                generated_image = self.multi_view_pipeline(
                    prompt=f"3D object view from {description}",
                    image=input_image,
                    control_image=depth_image,
                    controlnet_conditioning_scale=1.0,
                    rotation=angle,
                    guidance_scale=7.5
                ).images[0]
                
                multi_view_images.append(generated_image)
            except Exception as e:
                print(f"View generation error for angle {angle}: {e}")
        
        return multi_view_images

    def advanced_point_cloud_reconstruction(self, depth_maps):
        """
        Advanced Point Cloud Reconstruction
        """
        point_clouds = []
        
        for depth_map in depth_maps:
            # Create point cloud from depth map
            height, width = depth_map.shape
            x = np.linspace(0, width-1, width)
            y = np.linspace(0, height-1, height)
            xx, yy = np.meshgrid(x, y)
            
            # Convert depth to 3D points
            points_3d = np.column_stack([
                xx.ravel(), 
                yy.ravel(), 
                depth_map.ravel()
            ])
            
            # Create Open3D point cloud
            pcd = o3d.geometry.PointCloud()
            pcd.points = o3d.utility.Vector3dVector(points_3d)
            
            point_clouds.append(pcd)
        
        # Merge point clouds
        merged_pcd = point_clouds[0]
        for pcd in point_clouds[1:]:
            merged_pcd += pcd
        
        return merged_pcd

    def mesh_reconstruction(self, point_cloud):
        """
        Advanced Mesh Reconstruction
        """
        # Poisson surface reconstruction
        mesh, _ = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(point_cloud, depth=9)
        
        # Clean and smooth mesh
        mesh.compute_vertex_normals()
        mesh = mesh.filter_smooth_laplacian(number_of_iterations=10)
        
        return mesh

    def create_3d_model(self, input_image):
        """
        Comprehensive 3D Model Generation Pipeline
        """
        try:
            # Generate multi-view images
            multi_view_images = self.generate_multi_view_images(input_image)
            
            # Extract depth maps
            depth_maps = [np.array(self.generate_depth_map(img)) for img in multi_view_images]
            
            # Advanced point cloud reconstruction
            point_cloud = self.advanced_point_cloud_reconstruction(depth_maps)
            
            # Mesh generation
            mesh = self.mesh_reconstruction(point_cloud)
            
            # Save mesh in multiple formats
            output_path = "reconstructed_3d_model"
            o3d.io.write_triangle_mesh(f"{output_path}.ply", mesh)
            
            # Convert to trimesh for additional formats
            trimesh_mesh = trimesh.Trimesh(
                vertices=np.asarray(mesh.vertices),
                faces=np.asarray(mesh.triangles)
            )
            trimesh_mesh.export(f"{output_path}.obj")
            trimesh_mesh.export(f"{output_path}.stl")
            
            return (
                "3D Model Generated Successfully!", 
                multi_view_images,
                [f"{output_path}.ply", f"{output_path}.obj", f"{output_path}.stl"]
            )
        
        except Exception as e:
            return f"3D Model Generation Error: {str(e)}", None, None

    def create_gradio_interface(self):
        interface = gr.Interface(
            fn=self.create_3d_model,
            inputs=gr.Image(type="pil", label="Upload Image for 3D Reconstruction"),
            outputs=[
                gr.Textbox(label="Generation Status"),
                gr.Gallery(label="Generated Multi-View Images"),
                gr.File(label="Reconstructed 3D Model Files")
            ],
            title="Advanced 3D Model Generator",
            description="""
            Generate comprehensive 3D models from single images using:
            - Multi-view image generation
            - Advanced depth estimation
            - Point cloud reconstruction
            - Mesh generation
            """,
            allow_flagging="never"
        )
        
        return interface

def main():
    mesh_generator = CompleteMeshGenerator()
    interface = mesh_generator.create_gradio_interface()
    interface.launch(debug=True)

if __name__ == "__main__":
    main()