Update app.py

app.py

@@ -16,204 +16,1051 @@ from flask_cors import CORS
 import numpy as np
 import trimesh
 from transformers import pipeline
-from diffusers import StableDiffusionZero123Pipeline
-import imageio
-from scipy.spatial.transform import Rotation
+from scipy.ndimage import gaussian_filter, uniform_filter, median_filter
+from scipy import interpolate
+import cv2
 
 app = Flask(__name__)
-CORS(app)
+CORS(app)  # Enable CORS for all routes
 
-# Configuration
+# Configure directories
 UPLOAD_FOLDER = '/tmp/uploads'
 RESULTS_FOLDER = '/tmp/results'
 CACHE_DIR = '/tmp/huggingface'
 ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
-VIEW_ANGLES = [(30, 0), (30, 90), (30, 180), (30, 270)]  # (elevation, azimuth)
 
+# Create necessary directories
 os.makedirs(UPLOAD_FOLDER, exist_ok=True)
 os.makedirs(RESULTS_FOLDER, exist_ok=True)
 os.makedirs(CACHE_DIR, exist_ok=True)
 
-# Environment variables for caching
+# 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
+app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 16MB max
 
-# Global models
-view_generator = None
+# Job tracking dictionary
+processing_jobs = {}
+
+# Global model variables
 depth_estimator = None
 model_loaded = False
 model_loading = False
 
-processing_jobs = {}
+# 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
 
-def preprocess_image(image_path, size=256):
-    img = Image.open(image_path).convert("RGB")
-    img = img.resize((size, size), Image.LANCZOS)
-    return img
+# Enhanced image preprocessing with better detail preservation
+def preprocess_image(image_path):
+    with Image.open(image_path) as img:
+        # Keep alpha channel if present
+        has_alpha = img.mode == 'RGBA'
+        
+        # Convert to proper format while preserving alpha
+        if has_alpha:
+            img = img.convert("RGBA")
+        else:
+            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)
+        
+        # Extract alpha channel if present
+        if has_alpha:
+            alpha = img_array[:, :, 3]
+            rgb = img_array[:, :, :3]
+        else:
+            rgb = img_array
+            
+        # Apply adaptive histogram equalization for better contrast on RGB channels only
+        if len(rgb.shape) == 3 and rgb.shape[2] == 3:
+            # Convert to LAB color space for better contrast enhancement
+            lab = cv2.cvtColor(rgb, 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
+            rgb_enhanced = cv2.cvtColor(enhanced_lab, cv2.COLOR_LAB2RGB)
+            
+            # Recombine with alpha if needed
+            if has_alpha:
+                result = np.dstack((rgb_enhanced, alpha))
+                img = Image.fromarray(result, 'RGBA')
+            else:
+                img = Image.fromarray(rgb_enhanced, 'RGB')
+            
+        return img
+            
 
-def load_models():
-    global view_generator, depth_estimator, model_loaded
+def load_model():
+    global depth_estimator, model_loaded, model_loading
+    
     if model_loaded:
-        return
-
+        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:
-        # Load view generator
-        view_generator = StableDiffusionZero123Pipeline.from_pretrained(
-            "stabilityai/stable-zero123-6dof",
-            torch_dtype=torch.float16,
-            cache_dir=CACHE_DIR
-        ).to("cuda" if torch.cuda.is_available() else "cpu")
-
-        # Load depth estimator
+        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="Intel/dpt-hybrid-midas",
+            "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("Models loaded successfully")
+        print(f"Model loaded successfully on {device}")
+        return depth_estimator
+    
     except Exception as e:
-        print(f"Error loading models: {str(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
 
-def generate_novel_views(image, num_views=4):
-    views = []
-    for elevation, azimuth in VIEW_ANGLES:
-        result = view_generator(
-            image,
-            num_inference_steps=50,
-            elevation=elevation,
-            azimuth=azimuth,
-            guidance_scale=3.0
-        ).images[0]
-        views.append((result, (elevation, azimuth)))
-    return views
-
-def depth_to_pointcloud(depth_map, pose, fov=60):
-    h, w = depth_map.shape
-    f = w / (2 * np.tan(np.radians(fov/2)))
-    
-    xx, yy = np.meshgrid(np.arange(w), np.arange(h))
-    x = (xx - w/2) * depth_map / f
-    y = (yy - h/2) * depth_map / f
-    z = depth_map
-    
-    points = np.vstack((x.flatten(), y.flatten(), z.flatten())).T
-    
-    # Apply pose transformation
-    rot = Rotation.from_euler('zyx', [pose[1], pose[0], 0], degrees=True)
-    points = rot.apply(points)
-    
-    return points
-
-def create_mesh_from_pointcloud(points, image):
-    pcd = trimesh.PointCloud(points)
-    scene = pcd.scene()
-    mesh = scene.delaunay_3d.triangulate_pcd(pcd)
-    mesh.visual.vertex_colors = image.resize((mesh.vertices.shape[0], 3))
+# 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 complete 3D model with all sides"""
+    # 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
+    interp_func = interpolate.RectBivariateSpline(
+        np.arange(h), np.arange(w), enhanced_depth, kx=3, ky=3
+    )
+    
+    # Sample depth at grid points
+    z_values = interp_func(y, x, grid=True)
+    
+    # Process enhancement as in original code
+    if detail_level == 'high':
+        dx = np.gradient(z_values, axis=1) 
+        dy = np.gradient(z_values, axis=0)
+        gradient_magnitude = np.sqrt(dx**2 + dy**2)
+        edge_mask = np.clip(gradient_magnitude * 5, 0, 0.2)
+        z_values = z_values + edge_mask * (z_values - gaussian_filter(z_values, sigma=1.0))
+    
+    # Normalize z-values with advanced scaling
+    z_min, z_max = np.percentile(z_values, [2, 98])
+    z_values = (z_values - z_min) / (z_max - z_min) if z_max > z_min else z_values
+    
+    # Apply depth scaling
+    if detail_level == 'high':
+        z_scaling = 2.5
+    elif detail_level == 'medium':
+        z_scaling = 2.0
+    else:
+        z_scaling = 1.5
+        
+    z_values = z_values * z_scaling
+    
+    # Normalize coordinates for front face
+    x_grid_front = (x_grid / w - 0.5) * 2.0
+    y_grid_front = (y_grid / h - 0.5) * 2.0
+    
+    # Create all vertices (front, back, and sides)
+    vertices = []
+    
+    # Front face vertices
+    front_vertices = np.vstack([x_grid_front.flatten(), -y_grid_front.flatten(), -z_values.flatten()]).T
+    vertices.append(front_vertices)
+    
+    # Back face vertices (mirrored from front face)
+    back_depth = 1.0  # Constant thickness for the model
+    back_vertices = np.vstack([x_grid_front.flatten(), -y_grid_front.flatten(), -z_values.flatten() - back_depth]).T
+    vertices.append(back_vertices)
+    
+    # Create side vertices (top, bottom, left, right)
+    # For simplicity, we use a grid mapping for sides
+    top_vertices = []
+    bottom_vertices = []
+    left_vertices = []
+    right_vertices = []
+    
+    # Create sides by connecting front and back faces
+    for i in range(resolution):
+        # Top edge
+        for j in range(resolution):
+            if i == 0:
+                top_vertices.append(front_vertices[i * resolution + j])
+                top_vertices.append(back_vertices[i * resolution + j])
+            # Bottom edge
+            if i == resolution - 1:
+                bottom_vertices.append(front_vertices[i * resolution + j])
+                bottom_vertices.append(back_vertices[i * resolution + j])
+            # Left edge
+            if j == 0:
+                left_vertices.append(front_vertices[i * resolution + j])
+                left_vertices.append(back_vertices[i * resolution + j])
+            # Right edge
+            if j == resolution - 1:
+                right_vertices.append(front_vertices[i * resolution + j])
+                right_vertices.append(back_vertices[i * resolution + j])
+    
+    # Combine all vertices
+    all_vertices = np.vstack([
+        front_vertices, 
+        back_vertices, 
+        np.array(top_vertices),
+        np.array(bottom_vertices),
+        np.array(left_vertices),
+        np.array(right_vertices)
+    ])
+    
+    # Create faces (triangles)
+    faces = []
+    
+    # Front face triangles
+    front_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 for consistent orientation
+            v1 = front_vertices[p1]
+            v2 = front_vertices[p2]
+            v3 = front_vertices[p3]
+            v4 = front_vertices[p4]
+            
+            norm1 = np.cross(v2-v1, v4-v1)
+            norm2 = np.cross(v4-v3, v1-v3)
+            
+            if np.dot(norm1, norm2) >= 0:
+                front_faces.append([p1, p2, p4])
+                front_faces.append([p1, p4, p3])
+            else:
+                front_faces.append([p1, p2, p3])
+                front_faces.append([p2, p4, p3])
+    
+    # Back face triangles (note: reversed winding order for correct normals)
+    back_offset = resolution * resolution  # Offset for back face vertices
+    back_faces = []
+    for i in range(resolution-1):
+        for j in range(resolution-1):
+            p1 = back_offset + i * resolution + j
+            p2 = back_offset + i * resolution + (j + 1)
+            p3 = back_offset + (i + 1) * resolution + j
+            p4 = back_offset + (i + 1) * resolution + (j + 1)
+            
+            # Reverse winding order compared to front face
+            back_faces.append([p1, p4, p2])
+            back_faces.append([p1, p3, p4])
+    
+    # Side faces (connecting front and back)
+    side_faces = []
+    
+    # Add faces for sides (top, bottom, left, right)
+    side_offset = 2 * resolution * resolution  # Offset after front and back
+    
+    # Top side
+    top_count = len(top_vertices)
+    for i in range(0, top_count - 2, 2):
+        side_faces.append([side_offset + i, side_offset + i + 1, side_offset + i + 3])
+        side_faces.append([side_offset + i, side_offset + i + 3, side_offset + i + 2])
+    
+    # Bottom side
+    bottom_offset = side_offset + top_count
+    bottom_count = len(bottom_vertices)
+    for i in range(0, bottom_count - 2, 2):
+        side_faces.append([bottom_offset + i, bottom_offset + i + 3, bottom_offset + i + 1])
+        side_faces.append([bottom_offset + i, bottom_offset + i + 2, bottom_offset + i + 3])
+    
+    # Left side
+    left_offset = bottom_offset + bottom_count
+    left_count = len(left_vertices)
+    for i in range(0, left_count - 2, 2):
+        side_faces.append([left_offset + i, left_offset + i + 1, left_offset + i + 3])
+        side_faces.append([left_offset + i, left_offset + i + 3, left_offset + i + 2])
+    
+    # Right side
+    right_offset = left_offset + left_count
+    right_count = len(right_vertices)
+    for i in range(0, right_count - 2, 2):
+        side_faces.append([right_offset + i, right_offset + i + 3, right_offset + i + 1])
+        side_faces.append([right_offset + i, right_offset + i + 2, right_offset + i + 3])
+    
+    # Combine all faces
+    faces = np.array(front_faces + back_faces + side_faces)
+    
+    # Create mesh
+    mesh = trimesh.Trimesh(vertices=all_vertices, faces=faces)
+    
+    # Apply texturing if image is provided
+    if image:
+        # Handle RGBA properly to ensure transparency is maintained
+        img_array = np.array(image)
+        
+        # Check if image has alpha channel
+        has_alpha = len(img_array.shape) == 3 and img_array.shape[2] == 4
+        
+        # Create vertex colors with transparency support
+        vertex_colors = np.zeros((all_vertices.shape[0], 4), dtype=np.uint8)
+        
+        # Fill with default color (will be overridden for front face)
+        vertex_colors[:, :3] = [200, 200, 200]  # Light gray default
+        vertex_colors[:, 3] = 255  # Fully opaque
+        
+        # Front face texture (sample from image)
+        for i in range(resolution):
+            for j in range(resolution):
+                # Calculate image coordinates
+                img_x = j * (img_array.shape[1] - 1) / (resolution - 1)
+                img_y = i * (img_array.shape[0] - 1) / (resolution - 1)
+                
+                # Bilinear interpolation setup
+                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)
+                
+                # Interpolation weights
+                wx = img_x - x0
+                wy = img_y - y0
+                
+                vertex_idx = i * resolution + j
+                
+                if has_alpha:
+                    # Handle RGBA with bilinear interpolation
+                    for c in range(4):
+                        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 RGB (no alpha)
+                    for c in range(3):
+                        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])
+                    vertex_colors[vertex_idx, 3] = 255  # Fully opaque
+        
+        # Apply simpler texturing to back face
+        back_face_start = resolution * resolution
+        back_face_color = [180, 180, 180, 255]  # Slightly darker gray
+        vertex_colors[back_face_start:back_face_start + (resolution * resolution)] = back_face_color
+        
+        mesh.visual.vertex_colors = vertex_colors
+    
+    # Apply smoothing to get rid of staircase artifacts
+    if detail_level != 'high':
+        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": "Invalid file type"}), 400
-
+        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,
-        'error': 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 input image
-            img = preprocess_image(filepath)
-            processing_jobs[job_id]['progress'] = 20
-
-            # Generate novel views
-            views = generate_novel_views(img)
-            processing_jobs[job_id]['progress'] = 40
-
-            # Process each view
-            all_points = []
-            for view_img, pose in views:
-                # Estimate depth
-                depth_result = depth_estimator(view_img)
-                depth_map = np.array(depth_result["depth"])
+            # 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
                 
-                # Convert to point cloud
-                points = depth_to_pointcloud(depth_map, pose)
-                all_points.append(points)
-                processing_jobs[job_id]['progress'] += 10
+                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
 
-            # Combine point clouds
-            combined_points = np.vstack(all_points)
-            processing_jobs[job_id]['progress'] = 80
+@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
 
-            # Create mesh
-            mesh = create_mesh_from_pointcloud(combined_points, img)
+@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)
             
-            # Export
-            obj_path = os.path.join(output_dir, "model.obj")
-            mesh.export(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]['result_url'] = f"/download/{job_id}"
             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'] = str(e)
-        finally:
+            processing_jobs[job_id]['error'] = f"Error during processing: {str(e)}"
+            
+            # Clean up on error
             if os.path.exists(filepath):
                 os.remove(filepath)
-            gc.collect()
-            torch.cuda.empty_cache()
-
-    thread = threading.Thread(target=process_image)
-    thread.start()
-    return jsonify({"job_id": job_id}), 202
+    
+    # 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('/download/<job_id>')
-def download_model(job_id):
+@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": "Job not complete"}), 404
+        return jsonify({"error": "Model not found or processing not complete"}), 404
     
-    obj_path = os.path.join(RESULTS_FOLDER, job_id, "model.obj")
-    return send_file(obj_path, as_attachment=True)
-
-@app.route('/progress/<job_id>')
-def get_progress(job_id):
-    job = processing_jobs.get(job_id, {})
-    return jsonify({
-        'status': job.get('status'),
-        'progress': job.get('progress'),
-        'error': job.get('error')
-    })
+    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__':
-    load_models()
-    app.run(host='0.0.0.0', port=7860)
+    # 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)