app.py

import os
import torch
import time
import threading
import json
import gc
from flask import Flask, request, jsonify, send_file, Response, stream_with_context
from werkzeug.utils import secure_filename
from PIL import Image
import io
import zipfile
import uuid
import traceback
from huggingface_hub import snapshot_download
from flask_cors import CORS
import numpy as np
import trimesh
from transformers import pipeline
from scipy.ndimage import gaussian_filter, uniform_filter, median_filter
from scipy import interpolate
import cv2

app = Flask(__name__)
CORS(app)  # Enable CORS for all routes

# Configure directories
UPLOAD_FOLDER = '/tmp/uploads'
RESULTS_FOLDER = '/tmp/results'
CACHE_DIR = '/tmp/huggingface'
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}

# Create necessary directories
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(RESULTS_FOLDER, exist_ok=True)
os.makedirs(CACHE_DIR, exist_ok=True)

# Set Hugging Face cache environment variables
os.environ['HF_HOME'] = CACHE_DIR
os.environ['TRANSFORMERS_CACHE'] = os.path.join(CACHE_DIR, 'transformers')
os.environ['HF_DATASETS_CACHE'] = os.path.join(CACHE_DIR, 'datasets')

app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 16MB max

# Job tracking dictionary
processing_jobs = {}

# Global model variables
depth_estimator = None
model_loaded = False
model_loading = False

# Configuration for processing
TIMEOUT_SECONDS = 240  # 4 minutes max for processing
MAX_DIMENSION = 512    # Max image dimension to process

# TimeoutError for handling timeouts
class TimeoutError(Exception):
    pass

# Thread-safe timeout implementation
def process_with_timeout(function, args, timeout):
    result = [None]
    error = [None]
    completed = [False]
    
    def target():
        try:
            result[0] = function(*args)
            completed[0] = True
        except Exception as e:
            error[0] = e
    
    thread = threading.Thread(target=target)
    thread.daemon = True
    thread.start()
    
    thread.join(timeout)
    
    if not completed[0]:
        if thread.is_alive():
            return None, TimeoutError(f"Processing timed out after {timeout} seconds")
        elif error[0]:
            return None, error[0]
    
    if error[0]:
        return None, error[0]
    
    return result[0], None

def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

# Enhanced image preprocessing with better detail preservation
def preprocess_image(image_path):
    with Image.open(image_path) as img:
        img = img.convert("RGB")
        
        # Resize if the image is too large
        if img.width > MAX_DIMENSION or img.height > MAX_DIMENSION:
            # Calculate new dimensions while preserving aspect ratio
            if img.width > img.height:
                new_width = MAX_DIMENSION
                new_height = int(img.height * (MAX_DIMENSION / img.width))
            else:
                new_height = MAX_DIMENSION
                new_width = int(img.width * (MAX_DIMENSION / img.height))
            
            # Use high-quality Lanczos resampling for better detail preservation
            img = img.resize((new_width, new_height), Image.LANCZOS)
        
        # Convert to numpy array for additional preprocessing
        img_array = np.array(img)
        
        # Optional: Apply adaptive histogram equalization for better contrast
        # This helps the depth model detect more details
        if len(img_array.shape) == 3 and img_array.shape[2] == 3:
            # Convert to LAB color space
            lab = cv2.cvtColor(img_array, cv2.COLOR_RGB2LAB)
            l, a, b = cv2.split(lab)
            
            # Apply CLAHE to L channel
            clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
            cl = clahe.apply(l)
            
            # Merge channels back
            enhanced_lab = cv2.merge((cl, a, b))
            
            # Convert back to RGB
            img_array = cv2.cvtColor(enhanced_lab, cv2.COLOR_LAB2RGB)
            
            # Convert back to PIL Image
            img = Image.fromarray(img_array)
            
        return img

def load_model():
    global depth_estimator, model_loaded, model_loading
    
    if model_loaded:
        return depth_estimator
    
    if model_loading:
        # Wait for model to load if it's already in progress
        while model_loading and not model_loaded:
            time.sleep(0.5)
        return depth_estimator
    
    try:
        model_loading = True
        print("Starting model loading...")
        
        # Using DPT-Large which provides better detail than DPT-Hybrid
        # Alternatively, consider "vinvino02/glpn-nyu" for different detail characteristics
        model_name = "Intel/dpt-large"
        
        # Download model with retry mechanism
        max_retries = 3
        retry_delay = 5
        
        for attempt in range(max_retries):
            try:
                snapshot_download(
                    repo_id=model_name,
                    cache_dir=CACHE_DIR,
                    resume_download=True,
                )
                break
            except Exception as e:
                if attempt < max_retries - 1:
                    print(f"Download attempt {attempt+1} failed: {str(e)}. Retrying in {retry_delay} seconds...")
                    time.sleep(retry_delay)
                    retry_delay *= 2
                else:
                    raise
        
        # Initialize model with appropriate precision
        device = "cuda" if torch.cuda.is_available() else "cpu"
        
        # Load depth estimator pipeline
        depth_estimator = pipeline(
            "depth-estimation", 
            model=model_name,
            device=device if device == "cuda" else -1,
            cache_dir=CACHE_DIR
        )
        
        # Optimize memory usage
        if device == "cuda":
            torch.cuda.empty_cache()
        
        model_loaded = True
        print(f"Model loaded successfully on {device}")
        return depth_estimator
    
    except Exception as e:
        print(f"Error loading model: {str(e)}")
        print(traceback.format_exc())
        raise
    finally:
        model_loading = False

# Enhanced depth processing function to improve detail quality
def enhance_depth_map(depth_map, detail_level='medium'):
    """Apply sophisticated processing to enhance depth map details"""
    # Convert to numpy array if needed
    if isinstance(depth_map, Image.Image):
        depth_map = np.array(depth_map)
    
    # Make sure the depth map is 2D
    if len(depth_map.shape) > 2:
        depth_map = np.mean(depth_map, axis=2) if depth_map.shape[2] > 1 else depth_map[:,:,0]
    
    # Create a copy for processing
    enhanced_depth = depth_map.copy().astype(np.float32)
    
    # Remove outliers using percentile clipping (more stable than min/max)
    p_low, p_high = np.percentile(enhanced_depth, [1, 99])
    enhanced_depth = np.clip(enhanced_depth, p_low, p_high)
    
    # Normalize to 0-1 range for processing
    enhanced_depth = (enhanced_depth - p_low) / (p_high - p_low) if p_high > p_low else enhanced_depth
    
    # Apply different enhancement methods based on detail level
    if detail_level == 'high':
        # Apply unsharp masking for edge enhancement - simulating Hunyuan's detail technique
        # First apply gaussian blur
        blurred = gaussian_filter(enhanced_depth, sigma=1.5)
        # Create the unsharp mask
        mask = enhanced_depth - blurred
        # Apply the mask with strength factor
        enhanced_depth = enhanced_depth + 1.5 * mask
        
        # Apply bilateral filter to preserve edges while smoothing noise
        # Simulate using gaussian combinations
        smooth1 = gaussian_filter(enhanced_depth, sigma=0.5)
        smooth2 = gaussian_filter(enhanced_depth, sigma=2.0)
        edge_mask = enhanced_depth - smooth2
        enhanced_depth = smooth1 + 1.2 * edge_mask
        
    elif detail_level == 'medium':
        # Less aggressive but still effective enhancement
        # Apply mild unsharp masking
        blurred = gaussian_filter(enhanced_depth, sigma=1.0)
        mask = enhanced_depth - blurred
        enhanced_depth = enhanced_depth + 0.8 * mask
        
        # Apply mild smoothing to reduce noise but preserve edges
        enhanced_depth = gaussian_filter(enhanced_depth, sigma=0.5)
        
    else:  # low
        # Just apply noise reduction without too much detail enhancement
        enhanced_depth = gaussian_filter(enhanced_depth, sigma=0.7)
    
    # Normalize again after processing
    enhanced_depth = np.clip(enhanced_depth, 0, 1)
    
    return enhanced_depth

# Convert depth map to 3D mesh with significantly enhanced detail
def depth_to_mesh(depth_map, image, resolution=100, detail_level='medium'):
    """Convert depth map to 3D mesh with highly improved detail preservation"""
    # First, enhance the depth map for better details
    enhanced_depth = enhance_depth_map(depth_map, detail_level)
    
    # Get dimensions of depth map
    h, w = enhanced_depth.shape
    
    # Create a higher resolution grid for better detail
    x = np.linspace(0, w-1, resolution)
    y = np.linspace(0, h-1, resolution)
    x_grid, y_grid = np.meshgrid(x, y)
    
    # Use bicubic interpolation for smoother surface with better details
    # Create interpolation function
    interp_func = interpolate.RectBivariateSpline(
        np.arange(h), np.arange(w), enhanced_depth, kx=3, ky=3
    )
    
    # Sample depth at grid points with the interpolation function
    z_values = interp_func(y, x, grid=True)
    
    # Apply a post-processing step to enhance small details even further
    if detail_level == 'high':
        # Calculate local gradients to detect edges
        dx = np.gradient(z_values, axis=1) 
        dy = np.gradient(z_values, axis=0)
        
        # Enhance edges by increasing depth differences at high gradient areas
        gradient_magnitude = np.sqrt(dx**2 + dy**2)
        edge_mask = np.clip(gradient_magnitude * 5, 0, 0.2)  # Scale and limit effect
        
        # Apply edge enhancement
        z_values = z_values + edge_mask * (z_values - gaussian_filter(z_values, sigma=1.0))
    
    # Normalize z-values with advanced scaling for better depth impression
    z_min, z_max = np.percentile(z_values, [2, 98])  # Remove outliers
    z_values = (z_values - z_min) / (z_max - z_min) if z_max > z_min else z_values
    
    # Apply depth scaling appropriate to the detail level
    if detail_level == 'high':
        z_scaling = 2.5  # More pronounced depth variations
    elif detail_level == 'medium':
        z_scaling = 2.0  # Standard depth
    else:
        z_scaling = 1.5  # More subtle depth variations
        
    z_values = z_values * z_scaling
    
    # Normalize x and y coordinates
    x_grid = (x_grid / w - 0.5) * 2.0  # Map to -1 to 1
    y_grid = (y_grid / h - 0.5) * 2.0  # Map to -1 to 1
    
    # Create vertices
    vertices = np.vstack([x_grid.flatten(), -y_grid.flatten(), -z_values.flatten()]).T
    
    # Create faces (triangles) with optimized winding for better normals
    faces = []
    for i in range(resolution-1):
        for j in range(resolution-1):
            p1 = i * resolution + j
            p2 = i * resolution + (j + 1)
            p3 = (i + 1) * resolution + j
            p4 = (i + 1) * resolution + (j + 1)
            
            # Calculate normals to ensure consistent orientation
            v1 = vertices[p1]
            v2 = vertices[p2]
            v3 = vertices[p3]
            v4 = vertices[p4]
            
            # Calculate normals for both possible triangulations
            # and choose the one that's more consistent
            norm1 = np.cross(v2-v1, v4-v1)
            norm2 = np.cross(v4-v3, v1-v3)
            
            if np.dot(norm1, norm2) >= 0:
                # Standard triangulation
                faces.append([p1, p2, p4])
                faces.append([p1, p4, p3])
            else:
                # Alternative triangulation for smoother surface
                faces.append([p1, p2, p3])
                faces.append([p2, p4, p3])
    
    faces = np.array(faces)
    
    # Create mesh
    mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
    
    # Apply advanced texturing if image is provided
    if image:
        # Convert to numpy array if needed
        if isinstance(image, Image.Image):
            img_array = np.array(image)
        else:
            img_array = image
        
        # Create vertex colors with improved sampling
        if resolution <= img_array.shape[0] and resolution <= img_array.shape[1]:
            # Create vertex colors by sampling the image with bilinear interpolation
            vertex_colors = np.zeros((vertices.shape[0], 4), dtype=np.uint8)
            
            # Get normalized coordinates for sampling
            for i in range(resolution):
                for j in range(resolution):
                    # Calculate exact image coordinates with proper scaling
                    img_x = j * (img_array.shape[1] - 1) / (resolution - 1)
                    img_y = i * (img_array.shape[0] - 1) / (resolution - 1)
                    
                    # Bilinear interpolation for smooth color transitions
                    x0, y0 = int(img_x), int(img_y)
                    x1, y1 = min(x0 + 1, img_array.shape[1] - 1), min(y0 + 1, img_array.shape[0] - 1)
                    
                    # Calculate interpolation weights
                    wx = img_x - x0
                    wy = img_y - y0
                    
                    vertex_idx = i * resolution + j
                    
                    if len(img_array.shape) == 3 and img_array.shape[2] == 3:  # RGB
                        # Perform bilinear interpolation for each color channel
                        r = int((1-wx)*(1-wy)*img_array[y0, x0, 0] + wx*(1-wy)*img_array[y0, x1, 0] + 
                                (1-wx)*wy*img_array[y1, x0, 0] + wx*wy*img_array[y1, x1, 0])
                        g = int((1-wx)*(1-wy)*img_array[y0, x0, 1] + wx*(1-wy)*img_array[y0, x1, 1] + 
                                (1-wx)*wy*img_array[y1, x0, 1] + wx*wy*img_array[y1, x1, 1])
                        b = int((1-wx)*(1-wy)*img_array[y0, x0, 2] + wx*(1-wy)*img_array[y0, x1, 2] + 
                                (1-wx)*wy*img_array[y1, x0, 2] + wx*wy*img_array[y1, x1, 2])
                        
                        vertex_colors[vertex_idx, :3] = [r, g, b]
                        vertex_colors[vertex_idx, 3] = 255  # Alpha
                    elif len(img_array.shape) == 3 and img_array.shape[2] == 4:  # RGBA
                        for c in range(4):  # For each RGBA channel
                            vertex_colors[vertex_idx, c] = int((1-wx)*(1-wy)*img_array[y0, x0, c] + 
                                                            wx*(1-wy)*img_array[y0, x1, c] + 
                                                            (1-wx)*wy*img_array[y1, x0, c] + 
                                                            wx*wy*img_array[y1, x1, c])
                    else:
                        # Handle grayscale with bilinear interpolation
                        gray = int((1-wx)*(1-wy)*img_array[y0, x0] + wx*(1-wy)*img_array[y0, x1] + 
                                  (1-wx)*wy*img_array[y1, x0] + wx*wy*img_array[y1, x1])
                        vertex_colors[vertex_idx, :3] = [gray, gray, gray]
                        vertex_colors[vertex_idx, 3] = 255
            
            mesh.visual.vertex_colors = vertex_colors
    
    # Apply smoothing to get rid of staircase artifacts
    if detail_level != 'high':
        # For medium and low detail, apply Laplacian smoothing
        # but preserve the overall shape
        mesh = mesh.smoothed(method='laplacian', iterations=1)
    
    # Calculate and fix normals for better rendering
    mesh.fix_normals()
    
    return mesh

@app.route('/health', methods=['GET'])
def health_check():
    return jsonify({
        "status": "healthy", 
        "model": "Enhanced Depth-Based 3D Model Generator (DPT-Large)",
        "device": "cuda" if torch.cuda.is_available() else "cpu"
    }), 200

@app.route('/progress/<job_id>', methods=['GET'])
def progress(job_id):
    def generate():
        if job_id not in processing_jobs:
            yield f"data: {json.dumps({'error': 'Job not found'})}\n\n"
            return
            
        job = processing_jobs[job_id]
        
        # Send initial progress
        yield f"data: {json.dumps({'status': 'processing', 'progress': job['progress']})}\n\n"
        
        # Wait for job to complete or update
        last_progress = job['progress']
        check_count = 0
        while job['status'] == 'processing':
            if job['progress'] != last_progress:
                yield f"data: {json.dumps({'status': 'processing', 'progress': job['progress']})}\n\n"
                last_progress = job['progress']
            
            time.sleep(0.5)
            check_count += 1
            
            # If client hasn't received updates for a while, check if job is still running
            if check_count > 60:  # 30 seconds with no updates
                if 'thread_alive' in job and not job['thread_alive']():
                    job['status'] = 'error'
                    job['error'] = 'Processing thread died unexpectedly'
                    break
                check_count = 0
        
        # Send final status
        if job['status'] == 'completed':
            yield f"data: {json.dumps({'status': 'completed', 'progress': 100, 'result_url': job['result_url'], 'preview_url': job['preview_url']})}\n\n"
        else:
            yield f"data: {json.dumps({'status': 'error', 'error': job['error']})}\n\n"
    
    return Response(stream_with_context(generate()), mimetype='text/event-stream')

@app.route('/convert', methods=['POST'])
def convert_image_to_3d():
    # Check if image is in the request
    if 'image' not in request.files:
        return jsonify({"error": "No image provided"}), 400
    
    file = request.files['image']
    if file.filename == '':
        return jsonify({"error": "No image selected"}), 400
    
    if not allowed_file(file.filename):
        return jsonify({"error": f"File type not allowed. Supported types: {', '.join(ALLOWED_EXTENSIONS)}"}), 400
    
    # Get optional parameters with defaults
    try:
        mesh_resolution = min(int(request.form.get('mesh_resolution', 100)), 200)  # Limit max resolution
        output_format = request.form.get('output_format', 'obj').lower()
        detail_level = request.form.get('detail_level', 'medium').lower()  # Parameter for detail level
        texture_quality = request.form.get('texture_quality', 'medium').lower()  # New parameter for texture quality
    except ValueError:
        return jsonify({"error": "Invalid parameter values"}), 400
    
    # Validate output format
    if output_format not in ['obj', 'glb']:
        return jsonify({"error": "Unsupported output format. Use 'obj' or 'glb'"}), 400
    
    # Adjust mesh resolution based on detail level
    if detail_level == 'high':
        mesh_resolution = min(int(mesh_resolution * 1.5), 200)
    elif detail_level == 'low':
        mesh_resolution = max(int(mesh_resolution * 0.7), 50)
    
    # Create a job ID
    job_id = str(uuid.uuid4())
    output_dir = os.path.join(RESULTS_FOLDER, job_id)
    os.makedirs(output_dir, exist_ok=True)
    
    # Save the uploaded file
    filename = secure_filename(file.filename)
    filepath = os.path.join(app.config['UPLOAD_FOLDER'], f"{job_id}_{filename}")
    file.save(filepath)
    
    # Initialize job tracking
    processing_jobs[job_id] = {
        'status': 'processing',
        'progress': 0,
        'result_url': None,
        'preview_url': None,
        'error': None,
        'output_format': output_format,
        'created_at': time.time()
    }
    
    # Start processing in a separate thread
    def process_image():
        thread = threading.current_thread()
        processing_jobs[job_id]['thread_alive'] = lambda: thread.is_alive()
        
        try:
            # Preprocess image with enhanced detail preservation
            processing_jobs[job_id]['progress'] = 5
            image = preprocess_image(filepath)
            processing_jobs[job_id]['progress'] = 10
            
            # Load model
            try:
                model = load_model()
                processing_jobs[job_id]['progress'] = 30
            except Exception as e:
                processing_jobs[job_id]['status'] = 'error'
                processing_jobs[job_id]['error'] = f"Error loading model: {str(e)}"
                return
            
            # Process image with thread-safe timeout
            try:
                def estimate_depth():
                    # Get depth map
                    result = model(image)
                    depth_map = result["depth"]
                    
                    # Convert to numpy array if needed
                    if isinstance(depth_map, torch.Tensor):
                        depth_map = depth_map.cpu().numpy()
                    elif hasattr(depth_map, 'numpy'):
                        depth_map = depth_map.numpy()
                    elif isinstance(depth_map, Image.Image):
                        depth_map = np.array(depth_map)
                    
                    return depth_map
                
                depth_map, error = process_with_timeout(estimate_depth, [], TIMEOUT_SECONDS)
                
                if error:
                    if isinstance(error, TimeoutError):
                        processing_jobs[job_id]['status'] = 'error'
                        processing_jobs[job_id]['error'] = f"Processing timed out after {TIMEOUT_SECONDS} seconds"
                        return
                    else:
                        raise error
                        
                processing_jobs[job_id]['progress'] = 60
                
                # Create mesh from depth map with enhanced detail handling
                mesh_resolution_int = int(mesh_resolution)
                mesh = depth_to_mesh(depth_map, image, resolution=mesh_resolution_int, detail_level=detail_level)
                processing_jobs[job_id]['progress'] = 80
                
            except Exception as e:
                error_details = traceback.format_exc()
                processing_jobs[job_id]['status'] = 'error'
                processing_jobs[job_id]['error'] = f"Error during processing: {str(e)}"
                print(f"Error processing job {job_id}: {str(e)}")
                print(error_details)
                return
            
            # Export based on requested format with enhanced quality settings
            try:
                if output_format == 'obj':
                    obj_path = os.path.join(output_dir, "model.obj")
                    
                    # Export with normal and texture coordinates
                    mesh.export(
                        obj_path, 
                        file_type='obj',
                        include_normals=True,
                        include_texture=True
                    )
                    
                    # Create a zip file with OBJ and MTL
                    zip_path = os.path.join(output_dir, "model.zip")
                    with zipfile.ZipFile(zip_path, 'w') as zipf:
                        zipf.write(obj_path, arcname="model.obj")
                        mtl_path = os.path.join(output_dir, "model.mtl")
                        if os.path.exists(mtl_path):
                            zipf.write(mtl_path, arcname="model.mtl")
                        
                        # Include texture file if it exists
                        texture_path = os.path.join(output_dir, "model.png")
                        if os.path.exists(texture_path):
                            zipf.write(texture_path, arcname="model.png")
                    
                    processing_jobs[job_id]['result_url'] = f"/download/{job_id}"
                    processing_jobs[job_id]['preview_url'] = f"/preview/{job_id}"
                    
                elif output_format == 'glb':
                    # Export as GLB with enhanced settings
                    glb_path = os.path.join(output_dir, "model.glb")
                    mesh.export(
                        glb_path, 
                        file_type='glb'
                    )
                    
                    processing_jobs[job_id]['result_url'] = f"/download/{job_id}"
                    processing_jobs[job_id]['preview_url'] = f"/preview/{job_id}"
                
                # Update job status
                processing_jobs[job_id]['status'] = 'completed'
                processing_jobs[job_id]['progress'] = 100
                print(f"Job {job_id} completed successfully")
            except Exception as e:
                error_details = traceback.format_exc()
                processing_jobs[job_id]['status'] = 'error'
                processing_jobs[job_id]['error'] = f"Error exporting model: {str(e)}"
                print(f"Error exporting model for job {job_id}: {str(e)}")
                print(error_details)
            
            # Clean up temporary file
            if os.path.exists(filepath):
                os.remove(filepath)
            
            # Force garbage collection to free memory
            gc.collect()
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            
        except Exception as e:
            # Handle errors
            error_details = traceback.format_exc()
            processing_jobs[job_id]['status'] = 'error'
            processing_jobs[job_id]['error'] = f"{str(e)}\n{error_details}"
            print(f"Error processing job {job_id}: {str(e)}")
            print(error_details)
            
            # Clean up on error
            if os.path.exists(filepath):
                os.remove(filepath)
    
    # Start processing thread
    processing_thread = threading.Thread(target=process_image)
    processing_thread.daemon = True
    processing_thread.start()
    
    # Return job ID immediately
    return jsonify({"job_id": job_id}), 202  # 202 Accepted

@app.route('/download/<job_id>', methods=['GET'])
def download_model(job_id):
    if job_id not in processing_jobs or processing_jobs[job_id]['status'] != 'completed':
        return jsonify({"error": "Model not found or processing not complete"}), 404
    
    # Get the output directory for this job
    output_dir = os.path.join(RESULTS_FOLDER, job_id)
    
    # Determine file format from the job data
    output_format = processing_jobs[job_id].get('output_format', 'obj')
    
    if output_format == 'obj':
        zip_path = os.path.join(output_dir, "model.zip")
        if os.path.exists(zip_path):
            return send_file(zip_path, as_attachment=True, download_name="model.zip")
    else:  # glb
        glb_path = os.path.join(output_dir, "model.glb")
        if os.path.exists(glb_path):
            return send_file(glb_path, as_attachment=True, download_name="model.glb")
    
    return jsonify({"error": "File not found"}), 404

@app.route('/preview/<job_id>', methods=['GET'])
def preview_model(job_id):
    if job_id not in processing_jobs or processing_jobs[job_id]['status'] != 'completed':
        return jsonify({"error": "Model not found or processing not complete"}), 404
    
    # Get the output directory for this job
    output_dir = os.path.join(RESULTS_FOLDER, job_id)
    output_format = processing_jobs[job_id].get('output_format', 'obj')

    if output_format == 'obj':
        obj_path = os.path.join(output_dir, "model.obj")
        if os.path.exists(obj_path):
            return send_file(obj_path, mimetype='model/obj')
    else:  # glb
        glb_path = os.path.join(output_dir, "model.glb")
        if os.path.exists(glb_path):
            return send_file(glb_path, mimetype='model/gltf-binary')
    
    return jsonify({"error": "Model file not found"}), 404

# Cleanup old jobs periodically
def cleanup_old_jobs():
    current_time = time.time()
    job_ids_to_remove = []
    
    for job_id, job_data in processing_jobs.items():
        # Remove completed jobs after 1 hour
        if job_data['status'] == 'completed' and (current_time - job_data.get('created_at', 0)) > 3600:
            job_ids_to_remove.append(job_id)
        # Remove error jobs after 30 minutes
        elif job_data['status'] == 'error' and (current_time - job_data.get('created_at', 0)) > 1800:
            job_ids_to_remove.append(job_id)
    
    # Remove the jobs
    for job_id in job_ids_to_remove:
        output_dir = os.path.join(RESULTS_FOLDER, job_id)
        try:
            import shutil
            if os.path.exists(output_dir):
                shutil.rmtree(output_dir)
        except Exception as e:
            print(f"Error cleaning up job {job_id}: {str(e)}")
        
        # Remove from tracking dictionary
        if job_id in processing_jobs:
            del processing_jobs[job_id]
    
    # Schedule the next cleanup
    threading.Timer(300, cleanup_old_jobs).start()  # Run every 5 minutes

# New endpoint to get detailed information about a model
@app.route('/model-info/<job_id>', methods=['GET'])
def model_info(job_id):
    if job_id not in processing_jobs:
        return jsonify({"error": "Model not found"}), 404
        
    job = processing_jobs[job_id]
    
    if job['status'] != 'completed':
        return jsonify({
            "status": job['status'],
            "progress": job['progress'],
            "error": job.get('error')
        }), 200
    
    # For completed jobs, include information about the model
    output_dir = os.path.join(RESULTS_FOLDER, job_id)
    model_stats = {}
    
    # Get file size
    if job['output_format'] == 'obj':
        obj_path = os.path.join(output_dir, "model.obj")
        zip_path = os.path.join(output_dir, "model.zip")
        
        if os.path.exists(obj_path):
            model_stats['obj_size'] = os.path.getsize(obj_path)
            
        if os.path.exists(zip_path):
            model_stats['package_size'] = os.path.getsize(zip_path)
            
    else:  # glb
        glb_path = os.path.join(output_dir, "model.glb")
        if os.path.exists(glb_path):
            model_stats['model_size'] = os.path.getsize(glb_path)
    
    # Return detailed info
    return jsonify({
        "status": job['status'],
        "model_format": job['output_format'],
        "download_url": job['result_url'],
        "preview_url": job['preview_url'],
        "model_stats": model_stats,
        "created_at": job.get('created_at'),
        "completed_at": job.get('completed_at')
    }), 200

@app.route('/', methods=['GET'])
def index():
    return jsonify({
        "message": "Enhanced Image to 3D API (DPT-Large Model)", 
        "endpoints": [
            "/convert", 
            "/progress/<job_id>", 
            "/download/<job_id>", 
            "/preview/<job_id>",
            "/model-info/<job_id>"
        ],
        "parameters": {
            "mesh_resolution": "Integer (50-200), controls mesh density",
            "output_format": "obj or glb",
            "detail_level": "low, medium, or high - controls the level of detail in the final model",
            "texture_quality": "low, medium, or high - controls the quality of textures"
        },
        "description": "This API creates high-quality 3D models from 2D images with enhanced detail finishing similar to Hunyuan model"
    }), 200

# Example endpoint showing how to compare different detail levels
@app.route('/detail-comparison', methods=['POST'])
def compare_detail_levels():
    # Check if image is in the request
    if 'image' not in request.files:
        return jsonify({"error": "No image provided"}), 400
    
    file = request.files['image']
    if file.filename == '':
        return jsonify({"error": "No image selected"}), 400
    
    if not allowed_file(file.filename):
        return jsonify({"error": f"File type not allowed. Supported types: {', '.join(ALLOWED_EXTENSIONS)}"}), 400
    
    # Create a job ID
    job_id = str(uuid.uuid4())
    output_dir = os.path.join(RESULTS_FOLDER, job_id)
    os.makedirs(output_dir, exist_ok=True)
    
    # Save the uploaded file
    filename = secure_filename(file.filename)
    filepath = os.path.join(app.config['UPLOAD_FOLDER'], f"{job_id}_{filename}")
    file.save(filepath)
    
    # Initialize job tracking
    processing_jobs[job_id] = {
        'status': 'processing',
        'progress': 0,
        'result_url': None,
        'preview_url': None,
        'error': None,
        'output_format': 'glb',  # Use GLB for comparison
        'created_at': time.time(),
        'comparison': True
    }
    
    # Process in separate thread to create 3 different detail levels
    def process_comparison():
        thread = threading.current_thread()
        processing_jobs[job_id]['thread_alive'] = lambda: thread.is_alive()
        
        try:
            # Preprocess image
            image = preprocess_image(filepath)
            processing_jobs[job_id]['progress'] = 10
            
            # Load model
            try:
                model = load_model()
                processing_jobs[job_id]['progress'] = 20
            except Exception as e:
                processing_jobs[job_id]['status'] = 'error'
                processing_jobs[job_id]['error'] = f"Error loading model: {str(e)}"
                return
            
            # Process image to get depth map
            try:
                depth_map = model(image)["depth"]
                if isinstance(depth_map, torch.Tensor):
                    depth_map = depth_map.cpu().numpy()
                elif hasattr(depth_map, 'numpy'):
                    depth_map = depth_map.numpy()
                elif isinstance(depth_map, Image.Image):
                    depth_map = np.array(depth_map)
                
                processing_jobs[job_id]['progress'] = 40
            except Exception as e:
                processing_jobs[job_id]['status'] = 'error'
                processing_jobs[job_id]['error'] = f"Error estimating depth: {str(e)}"
                return
            
            # Create meshes at different detail levels
            result_urls = {}
            
            for detail_level in ['low', 'medium', 'high']:
                try:
                    # Update progress
                    if detail_level == 'low':
                        processing_jobs[job_id]['progress'] = 50
                    elif detail_level == 'medium':
                        processing_jobs[job_id]['progress'] = 70
                    else:
                        processing_jobs[job_id]['progress'] = 90
                    
                    # Create mesh with appropriate detail level
                    mesh_resolution = 100  # Fixed resolution for fair comparison
                    if detail_level == 'high':
                        mesh_resolution = 150
                    elif detail_level == 'low':
                        mesh_resolution = 80
                    
                    mesh = depth_to_mesh(depth_map, image, 
                                         resolution=mesh_resolution, 
                                         detail_level=detail_level)
                    
                    # Export as GLB
                    model_path = os.path.join(output_dir, f"model_{detail_level}.glb")
                    mesh.export(model_path, file_type='glb')
                    
                    # Add to result URLs
                    result_urls[detail_level] = f"/compare-download/{job_id}/{detail_level}"
                    
                except Exception as e:
                    print(f"Error processing {detail_level} detail level: {str(e)}")
                    # Continue with other detail levels even if one fails
            
            # Update job status
            processing_jobs[job_id]['status'] = 'completed'
            processing_jobs[job_id]['progress'] = 100
            processing_jobs[job_id]['result_urls'] = result_urls
            processing_jobs[job_id]['completed_at'] = time.time()
            
            # Clean up temporary file
            if os.path.exists(filepath):
                os.remove(filepath)
            
            # Force garbage collection
            gc.collect()
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            
        except Exception as e:
            # Handle errors
            processing_jobs[job_id]['status'] = 'error'
            processing_jobs[job_id]['error'] = f"Error during processing: {str(e)}"
            
            # Clean up on error
            if os.path.exists(filepath):
                os.remove(filepath)
    
    # Start processing thread
    processing_thread = threading.Thread(target=process_comparison)
    processing_thread.daemon = True
    processing_thread.start()
    
    # Return job ID immediately
    return jsonify({"job_id": job_id, "check_progress_at": f"/progress/{job_id}"}), 202

@app.route('/compare-download/<job_id>/<detail_level>', methods=['GET'])
def download_comparison_model(job_id, detail_level):
    if job_id not in processing_jobs or processing_jobs[job_id]['status'] != 'completed':
        return jsonify({"error": "Model not found or processing not complete"}), 404
    
    if 'comparison' not in processing_jobs[job_id] or not processing_jobs[job_id]['comparison']:
        return jsonify({"error": "This is not a comparison job"}), 400
    
    if detail_level not in ['low', 'medium', 'high']:
        return jsonify({"error": "Invalid detail level"}), 400
    
    # Get the output directory for this job
    output_dir = os.path.join(RESULTS_FOLDER, job_id)
    model_path = os.path.join(output_dir, f"model_{detail_level}.glb")
    
    if os.path.exists(model_path):
        return send_file(model_path, as_attachment=True, download_name=f"model_{detail_level}.glb")
    
    return jsonify({"error": "File not found"}), 404

if __name__ == '__main__':
    # Start the cleanup thread
    cleanup_old_jobs()
    
    # Use port 7860 which is standard for Hugging Face Spaces
    port = int(os.environ.get('PORT', 7860))
    app.run(host='0.0.0.0', port=port)