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, login
from flask_cors import CORS
import numpy as np
import trimesh
from transformers import pipeline, AutoImageProcessor, AutoModelForDepthEstimation
from scipy.ndimage import gaussian_filter
from scipy import interpolate
import cv2

app = Flask(__name__)
CORS(app)

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

os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(RESULTS_FOLDER, exist_ok=True)
os.makedirs(CACHE_DIR, exist_ok=True)

os.environ['HF_HOME'] = CACHE_DIR
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024

# Job tracking
processing_jobs = {}

# Model variables
dpt_estimator = None
depth_anything_model = None
depth_anything_processor = None
model_loaded = False
model_loading = False

TIMEOUT_SECONDS = 240
MAX_DIMENSION = 518

class TimeoutError(Exception):
    pass

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):
    with Image.open(image_path) as img:
        img = img.convert("RGB")
        
        if img.width > MAX_DIMENSION or img.height > MAX_DIMENSION:
            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))
            img = img.resize((new_width, new_height), Image.LANCZOS)
        
        img_array = np.array(img)
        if len(img_array.shape) == 3 and img_array.shape[2] == 3:
            lab = cv2.cvtColor(img_array, cv2.COLOR_RGB2LAB)
            l, a, b = cv2.split(lab)
            clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
            cl = clahe.apply(l)
            enhanced_lab = cv2.merge((cl, a, b))
            img_array = cv2.cvtColor(enhanced_lab, cv2.COLOR_LAB2RGB)
            img = Image.fromarray(img_array)
        
        return img

def load_models():
    global dpt_estimator, depth_anything_model, depth_anything_processor, model_loaded, model_loading
    
    if model_loaded:
        return dpt_estimator, depth_anything_model, depth_anything_processor
    
    if model_loading:
        while model_loading and not model_loaded:
            time.sleep(0.5)
        return dpt_estimator, depth_anything_model, depth_anything_processor
    
    try:
        model_loading = True
        print("Loading models...")
        
        # Authenticate with Hugging Face
        hf_token = os.environ.get('HF_TOKEN')
        if hf_token:
            login(token=hf_token)
            print("Authenticated with Hugging Face token")
        
        # DPT-Large
        dpt_model_name = "Intel/dpt-large"
        max_retries = 3
        retry_delay = 5
        for attempt in range(max_retries):
            try:
                snapshot_download(
                    repo_id=dpt_model_name,
                    cache_dir=CACHE_DIR,
                    resume_download=True,
                    token=hf_token
                )
                break
            except Exception as e:
                if attempt < max_retries - 1:
                    print(f"DPT download attempt {attempt+1} failed: {str(e)}. Retrying...")
                    time.sleep(retry_delay)
                    retry_delay *= 2
                else:
                    raise
        
        dpt_estimator = pipeline(
            "depth-estimation",
            model=dpt_model_name,
            device=-1,
            cache_dir=CACHE_DIR,
            use_fast=True
        )
        print("DPT-Large loaded")
        gc.collect()
        
        # Depth Anything
        da_model_name = "depth-anything/Depth-Anything-V2-Small-hf"
        for attempt in range(max_retries):
            try:
                snapshot_download(
                    repo_id=da_model_name,
                    cache_dir=CACHE_DIR,
                    resume_download=True,
                    token=hf_token
                )
                break
            except Exception as e:
                if attempt < max_retries - 1:
                    print(f"Depth Anything download attempt {attempt+1} failed: {str(e)}. Retrying...")
                    time.sleep(retry_delay)
                    retry_delay *= 2
                else:
                    print(f"Failed to load Depth Anything: {str(e)}. Falling back to DPT-Large only.")
                    depth_anything_model = None
                    depth_anything_processor = None
                    model_loaded = True
                    return dpt_estimator, None, None
        
        depth_anything_processor = AutoImageProcessor.from_pretrained(
            da_model_name,
            cache_dir=CACHE_DIR,
            token=hf_token
        )
        depth_anything_model = AutoModelForDepthEstimation.from_pretrained(
            da_model_name,
            cache_dir=CACHE_DIR,
            token=hf_token
        ).to("cpu")
        
        model_loaded = True
        print("Depth Anything loaded")
        return dpt_estimator, depth_anything_model, depth_anything_processor
    
    except Exception as e:
        print(f"Error loading models: {str(e)}")
        print(traceback.format_exc())
        raise
    finally:
        model_loading = False

def fuse_depth_maps(dpt_depth, da_depth, detail_level='medium'):
    if isinstance(dpt_depth, Image.Image):
        dpt_depth = np.array(dpt_depth)
    if isinstance(da_depth, torch.Tensor):
        da_depth = da_depth.cpu().numpy()
    if len(dpt_depth.shape) > 2:
        dpt_depth = np.mean(dpt_depth, axis=2)
    if len(da_depth.shape) > 2:
        da_depth = np.mean(da_depth, axis=2)
    
    if dpt_depth.shape != da_depth.shape:
        da_depth = cv2.resize(da_depth, (dpt_depth.shape[1], dpt_depth.shape[0]), interpolation=cv2.INTER_CUBIC)
    
    p_low_dpt, p_high_dpt = np.percentile(dpt_depth, [1, 99])
    p_low_da, p_high_da = np.percentile(da_depth, [1, 99])
    dpt_depth = np.clip((dpt_depth - p_low_dpt) / (p_high_dpt - p_low_dpt), 0, 1) if p_high_dpt > p_low_dpt else dpt_depth
    da_depth = np.clip((da_depth - p_low_da) / (p_high_da - p_low_da), 0, 1) if p_high_da > p_low_da else da_depth
    
    if detail_level == 'high':
        weight_da = 0.7
        edges = cv2.Canny((da_depth * 255).astype(np.uint8), 50, 150)
        edge_mask = (edges > 0).astype(np.float32)
        dpt_weight = gaussian_filter(1 - edge_mask, sigma=1.0)
        da_weight = gaussian_filter(edge_mask, sigma=1.0)
        fused_depth = dpt_weight * dpt_depth + da_weight * da_depth * weight_da + (1 - weight_da) * dpt_depth
    else:
        weight_da = 0.5 if detail_level == 'medium' else 0.3
        fused_depth = (1 - weight_da) * dpt_depth + weight_da * da_depth
    
    fused_depth = np.clip(fused_depth, 0, 1)
    return fused_depth

def enhance_depth_map(depth_map, detail_level='medium'):
    enhanced_depth = depth_map.copy().astype(np.float32)
    p_low, p_high = np.percentile(enhanced_depth, [1, 99])
    enhanced_depth = np.clip(enhanced_depth, p_low, p_high)
    enhanced_depth = (enhanced_depth - p_low) / (p_high - p_low) if p_high > p_low else enhanced_depth
    
    if detail_level == 'high':
        blurred = gaussian_filter(enhanced_depth, sigma=1.5)
        mask = enhanced_depth - blurred
        enhanced_depth = enhanced_depth + 1.5 * mask
        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':
        blurred = gaussian_filter(enhanced_depth, sigma=1.0)
        mask = enhanced_depth - blurred
        enhanced_depth = enhanced_depth + 0.8 * mask
        enhanced_depth = gaussian_filter(enhanced_depth, sigma=0.5)
    else:
        enhanced_depth = gaussian_filter(enhanced_depth, sigma=0.7)
    
    enhanced_depth = np.clip(enhanced_depth, 0, 1)
    return enhanced_depth

def depth_to_mesh(depth_map, image, resolution=100, detail_level='medium'):
    enhanced_depth = enhance_depth_map(depth_map, detail_level)
    h, w = enhanced_depth.shape
    x = np.linspace(0, w-1, resolution)
    y = np.linspace(0, h-1, resolution)
    x_grid, y_grid = np.meshgrid(x, y)
    
    interp_func = interpolate.RectBivariateSpline(
        np.arange(h), np.arange(w), enhanced_depth, kx=3, ky=3
    )
    z_values = interp_func(y, x, grid=True)
    
    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))
    
    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
    z_scaling = 2.5 if detail_level == 'high' else 2.0 if detail_level == 'medium' else 1.5
    z_values = z_values * z_scaling
    
    x_grid = (x_grid / w - 0.5) * 2.0
    y_grid = (y_grid / h - 0.5) * 2.0
    vertices = np.vstack([x_grid.flatten(), -y_grid.flatten(), -z_values.flatten()]).T
    
    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)
            v1 = vertices[p1]
            v2 = vertices[p2]
            v3 = vertices[p3]
            v4 = vertices[p4]
            norm1 = np.cross(v2-v1, v4-v1)
            norm2 = np.cross(v4-v3, v1-v3)
            if np.dot(norm1, norm2) >= 0:
                faces.append([p1, p2, p4])
                faces.append([p1, p4, p3])
            else:
                faces.append([p1, p2, p3])
                faces.append([p2, p4, p3])
    
    faces = np.array(faces)
    mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
    
    if image:
        img_array = np.array(image)
        vertex_colors = np.zeros((vertices.shape[0], 4), dtype=np.uint8)
        for i in range(resolution):
            for j in range(resolution):
                img_x = j * (img_array.shape[1] - 1) / (resolution - 1)
                img_y = i * (img_array.shape[0] - 1) / (resolution - 1)
                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)
                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:
                    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
                elif len(img_array.shape) == 3 and img_array.shape[2] == 4:
                    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:
                    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
    
    if detail_level != 'high':
        mesh = mesh.smoothed(method='laplacian', iterations=1)
    mesh.fix_normals()
    return mesh

@app.route('/health', methods=['GET'])
def health_check():
    return jsonify({
        "status": "healthy",
        "model": "DPT-Large + Depth Anything",
        "device": "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]
        yield f"data: {json.dumps({'status': 'processing', 'progress': job['progress']})}\n\n"
        
        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 check_count > 60:
                if 'thread_alive' in job and not job['thread_alive']():
                    job['status'] = 'error'
                    job['error'] = 'Processing thread died unexpectedly'
                    break
                check_count = 0
        
        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():
    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
    
    try:
        mesh_resolution = min(int(request.form.get('mesh_resolution', 100)), 150)
        output_format = request.form.get('output_format', 'glb').lower()
        detail_level = request.form.get('detail_level', 'medium').lower()
        texture_quality = request.form.get('texture_quality', 'medium').lower()
    except ValueError:
        return jsonify({"error": "Invalid parameter values"}), 400
    
    if output_format not in ['obj', 'glb']:
        return jsonify({"error": "Unsupported output format. Use 'obj' or 'glb'"}), 400
    
    if detail_level == 'high':
        mesh_resolution = min(int(mesh_resolution * 1.5), 150)
    elif detail_level == 'low':
        mesh_resolution = max(int(mesh_resolution * 0.7), 50)
    
    job_id = str(uuid.uuid4())
    output_dir = os.path.join(RESULTS_FOLDER, job_id)
    os.makedirs(output_dir, exist_ok=True)
    
    filename = secure_filename(file.filename)
    filepath = os.path.join(app.config['UPLOAD_FOLDER'], f"{job_id}_{filename}")
    file.save(filepath)
    
    processing_jobs[job_id] = {
        'status': 'processing',
        'progress': 0,
        'result_url': None,
        'preview_url': None,
        'error': None,
        'output_format': output_format,
        'created_at': time.time()
    }
    
    def process_image():
        thread = threading.current_thread()
        processing_jobs[job_id]['thread_alive'] = lambda: thread.is_alive()
        
        try:
            processing_jobs[job_id]['progress'] = 5
            image = preprocess_image(filepath)
            processing_jobs[job_id]['progress'] = 10
            
            try:
                dpt_model, da_model, da_processor = load_models()
                processing_jobs[job_id]['progress'] = 30
            except Exception as e:
                processing_jobs[job_id]['status'] = 'error'
                processing_jobs[job_id]['error'] = f"Error loading models: {str(e)}"
                return
            
            try:
                def estimate_depth():
                    with torch.no_grad():
                        # DPT-Large
                        dpt_result = dpt_model(image)
                        dpt_depth = dpt_result["depth"]
                        
                        # Depth Anything (if loaded)
                        if da_model and da_processor:
                            inputs = da_processor(images=image, return_tensors="pt")
                            inputs = {k: v.to("cpu") for k, v in inputs.items()}
                            outputs = da_model(**inputs)
                            da_depth = outputs.predicted_depth.squeeze()
                            da_depth = torch.nn.functional.interpolate(
                                da_depth.unsqueeze(0).unsqueeze(0),
                                size=(image.height, image.width),
                                mode='bicubic',
                                align_corners=False
                            ).squeeze()
                            fused_depth = fuse_depth_maps(dpt_depth, da_depth, detail_level)
                        else:
                            fused_depth = np.array(dpt_depth) if isinstance(dpt_depth, Image.Image) else dpt_depth
                            if len(fused_depth.shape) > 2:
                                fused_depth = np.mean(fused_depth, axis=2)
                            p_low, p_high = np.percentile(fused_depth, [1, 99])
                            fused_depth = np.clip((fused_depth - p_low) / (p_high - p_low), 0, 1) if p_high > p_low else fused_depth
                        
                        return fused_depth
                
                fused_depth, 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
                mesh_resolution_int = int(mesh_resolution)
                mesh = depth_to_mesh(fused_depth, image, resolution=mesh_resolution_int, detail_level=detail_level)
                processing_jobs[job_id]['progress'] = 80
                
                if output_format == 'obj':
                    obj_path = os.path.join(output_dir, "model.obj")
                    mesh.export(
                        obj_path,
                        file_type='obj',
                        include_normals=True,
                        include_texture=True
                    )
                    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")
                        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':
                    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}"
                
                processing_jobs[job_id]['status'] = 'completed'
                processing_jobs[job_id]['progress'] = 100
                print(f"Job {job_id} completed")
            
            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
            
            if os.path.exists(filepath):
                os.remove(filepath)
            gc.collect()
            
        except Exception as e:
            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)
            if os.path.exists(filepath):
                os.remove(filepath)
    
    processing_thread = threading.Thread(target=process_image)
    processing_thread.daemon = True
    processing_thread.start()
    
    return jsonify({"job_id": job_id}), 202

@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
    
    output_dir = os.path.join(RESULTS_FOLDER, job_id)
    output_format = processing_jobs[job_id].get('output_format', 'glb')
    
    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_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
    
    output_dir = os.path.join(RESULTS_FOLDER, job_id)
    output_format = processing_jobs[job_id].get('output_format', 'glb')
    
    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_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": "File not found"}), 404

def cleanup_old_jobs():
    current_time = time.time()
    job_ids_to_remove = []
    
    for job_id, job_data in processing_jobs.items():
        if job_data['status'] == 'completed' and (current_time - job_data.get('created_at', 0)) > 3600:
            job_ids_to_remove.append(job_id)
        elif job_data['status'] == 'error' and (current_time - job_data.get('created_at', 0)) > 1800:
            job_ids_to_remove.append(job_id)
    
    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)}")
        
        if job_id in processing_jobs:
            del processing_jobs[job_id]
    
    threading.Timer(300, cleanup_old_jobs).start()

@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
    
    output_dir = os.path.join(RESULTS_FOLDER, job_id)
    model_stats = {}
    
    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_path = os.path.join(output_dir, "model.glb")
        if os.path.exists(glb_path):
            model_stats['model_size'] = os.path.getsize(glb_path)
    
    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": "Image to 3D API (DPT-Large + Depth Anything)",
        "endpoints": [
            "/convert",
            "/progress/<job_id>",
            "/download/<job_id>",
            "/preview/<job_id>",
            "/model-info/<job_id>"
        ],
        "parameters": {
            "mesh_resolution": "Integer (50-150)",
            "output_format": "obj or glb",
            "detail_level": "low, medium, or high",
            "texture_quality": "low, medium, or high"
        },
        "description": "Creates high-quality 3D models from 2D images using DPT-Large and Depth Anything."
    }), 200

if __name__ == '__main__':
    cleanup_old_jobs()
    port = int(os.environ.get('PORT', 7860))
    app.run(host='0.0.0.0', port=port)