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
import cv2
from transformers import AutoModel, AutoProcessor  # For TripoSR
from u2net import U2NET  # For background removal; install from https://github.com/xuebinqin/U-2-Net
import torchvision.transforms as T

app = Flask(__name__)
CORS(app)

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

# Create 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
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
processing_jobs = {}

# Global model variables
u2net_model = None
triposr_model = None
triposr_processor = None
model_loaded = False
model_loading = False

# Configuration
TIMEOUT_SECONDS = 240  # 4 minutes max
MAX_DIMENSION = 512    # Max image dimension

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")
        
        # Resize if too large
        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)
        
        # Apply adaptive histogram equalization
        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 remove_background(image):
    global u2net_model
    if u2net_model is None:
        u2net_model = U2NET()
        u2net_model.load_state_dict(torch.load('u2net.pth', map_location='cpu'))
        u2net_model.eval()
        u2net_model.to('cpu')
    
    img_array = np.array(image)
    img_tensor = T.ToTensor()(image.resize((320, 320))).unsqueeze(0)
    
    with torch.no_grad():
        d1, *_ = u2net_model(img_tensor)
        pred = d1[:, 0, :, :]
        pred = (pred - pred.min()) / (pred.max() - pred.min())
        mask = (pred > 0.5).float().squeeze().numpy()
    
    mask_img = Image.fromarray((mask * 255).astype('uint8')).resize(image.size)
    mask_array = np.array(mask_img)[:, :, np.newaxis] / 255
    result = img_array * mask_array + (1 - mask_array) * 255  # White background
    return Image.fromarray(result.astype('uint8'))

def load_model():
    global triposr_model, triposr_processor, model_loaded, model_loading
    
    if model_loaded:
        return triposr_model, triposr_processor
    
    if model_loading:
        while model_loading and not model_loaded:
            time.sleep(0.5)
        return triposr_model, triposr_processor
    
    try:
        model_loading = True
        print("Loading TripoSR model...")
        
        model_name = "stabilityai/TripoSR"
        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...")
                    time.sleep(retry_delay)
                    retry_delay *= 2
                else:
                    raise
        
        triposr_processor = AutoProcessor.from_pretrained(model_name, cache_dir=CACHE_DIR)
        triposr_model = AutoModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
        triposr_model.to('cpu')
        
        model_loaded = True
        print("TripoSR model loaded successfully on CPU")
        return triposr_model, triposr_processor
    
    except Exception as e:
        print(f"Error loading model: {str(e)}")
        print(traceback.format_exc())
        raise
    finally:
        model_loading = False

def optimize_mesh(mesh, detail_level='medium'):
    # Simplify mesh based on detail level
    if detail_level == 'high':
        target_faces = 50000
    elif detail_level == 'medium':
        target_faces = 30000
    else:
        target_faces = 15000
    
    if len(mesh.faces) > target_faces:
        mesh = mesh.simplify_quadric_decimation(target_faces)
    
    # Fix normals
    mesh.fix_normals()
    return mesh

@app.route('/health', methods=['GET'])
def health_check():
    return jsonify({
        "status": "healthy",
        "model": "TripoSR 3D Model Generator",
        "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: {', '.join(ALLOWED_EXTENSIONS)}"}), 400
    
    try:
        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: 'obj' or 'glb'"}), 400
    
    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:
            # Preprocess image
            processing_jobs[job_id]['progress'] = 5
            image = preprocess_image(filepath)
            processing_jobs[job_id]['progress'] = 10
            
            # Remove background
            processing_jobs[job_id]['progress'] = 20
            clean_image = remove_background(image)
            processing_jobs[job_id]['progress'] = 30
            
            # Load TripoSR model
            try:
                model, processor = load_model()
                processing_jobs[job_id]['progress'] = 40
            except Exception as e:
                processing_jobs[job_id]['status'] = 'error'
                processing_jobs[job_id]['error'] = f"Error loading model: {str(e)}"
                return
            
            # Generate 3D model
            try:
                def generate_3d():
                    inputs = processor(images=clean_image, return_tensors="pt").to('cpu')
                    with torch.no_grad():
                        outputs = model(**inputs)
                    mesh = outputs.mesh  # TripoSR outputs a trimesh object
                    return mesh
                
                mesh, error = process_with_timeout(generate_3d, [], 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'] = 70
                
                # Optimize mesh
                mesh = optimize_mesh(mesh, 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 model
            try:
                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 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)
            
            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": "Model 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": "TripoSR Image to 3D API",
        "endpoints": [
            "/convert",
            "/progress/<job_id>",
            "/download/<job_id>",
            "/preview/<job_id>",
            "/model-info/<job_id>"
        ],
        "parameters": {
            "output_format": "obj or glb",
            "detail_level": "low, medium, or high - controls mesh density",
            "texture_quality": "low, medium, or high - controls texture quality"
        },
        "description": "Creates full 3D models from 2D images with background removal"
    }), 200

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