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 diffusers import ShapEImg2ImgPipeline
from diffusers.utils import export_to_obj
from huggingface_hub import snapshot_download
from flask_cors import CORS
import signal
import functools

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

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

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

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

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

# Job tracking dictionary
processing_jobs = {}

# Global model variable
pipe = None
model_loaded = False
model_loading = False

# Configuration for processing
TIMEOUT_SECONDS = 300  # 5 minutes max for processing
MAX_DIMENSION = 512    # Max image dimension to process

# Timeout handler for long-running processes
class TimeoutError(Exception):
    pass

def timeout_handler(signum, frame):
    raise TimeoutError("Processing timed out")

def with_timeout(timeout):
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            # Set the timeout handler
            signal.signal(signal.SIGALRM, timeout_handler)
            signal.alarm(timeout)
            try:
                result = func(*args, **kwargs)
            finally:
                # Disable the alarm
                signal.alarm(0)
            return result
        return wrapper
    return decorator

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

# Function to preprocess image - resize if needed
def preprocess_image(image_path):
    with Image.open(image_path) as img:
        img = img.convert("RGB")
        # Resize if the image is too large
        if img.width > MAX_DIMENSION or img.height > MAX_DIMENSION:
            # Calculate new dimensions while preserving aspect ratio
            if img.width > img.height:
                new_width = MAX_DIMENSION
                new_height = int(img.height * (MAX_DIMENSION / img.width))
            else:
                new_height = MAX_DIMENSION
                new_width = int(img.width * (MAX_DIMENSION / img.height))
            img = img.resize((new_width, new_height), Image.LANCZOS)
        
        # Convert to RGB and return
        return img

def load_model():
    global pipe, model_loaded, model_loading
    
    if model_loaded:
        return pipe
    
    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 pipe
    
    try:
        model_loading = True
        print("Starting model loading...")
        
        model_name = "openai/shap-e-img2img"
        
        # 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 pipeline with lower precision to save memory
        device = "cuda" if torch.cuda.is_available() else "cpu"
        dtype = torch.float16 if device == "cuda" else torch.float32
        
        pipe = ShapEImg2ImgPipeline.from_pretrained(
            model_name,
            torch_dtype=dtype,
            cache_dir=CACHE_DIR,
        )
        pipe = pipe.to(device)
        
        # Optimize for inference
        if device == "cuda":
            pipe.enable_model_cpu_offload()
        
        model_loaded = True
        print(f"Model loaded successfully on {device}")
        return pipe
    
    except Exception as e:
        print(f"Error loading model: {str(e)}")
        print(traceback.format_exc())
        raise
    finally:
        model_loading = False

@app.route('/health', methods=['GET'])
def health_check():
    return jsonify({
        "status": "healthy", 
        "model": "Shap-E Image to 3D",
        "device": "cuda" if torch.cuda.is_available() else "cpu"
    }), 200

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

@app.route('/convert', methods=['POST'])
def convert_image_to_3d():
    # Check if image is in the request
    if 'image' not in request.files:
        return jsonify({"error": "No image provided"}), 400
    
    file = request.files['image']
    if file.filename == '':
        return jsonify({"error": "No image selected"}), 400
    
    if not allowed_file(file.filename):
        return jsonify({"error": f"File type not allowed. Supported types: {', '.join(ALLOWED_EXTENSIONS)}"}), 400
    
    # Get optional parameters with defaults
    try:
        guidance_scale = float(request.form.get('guidance_scale', 3.0))
        num_inference_steps = int(request.form.get('num_inference_steps', 64))
        output_format = request.form.get('output_format', 'obj').lower()
    except ValueError:
        return jsonify({"error": "Invalid parameter values"}), 400
    
    # Validate parameters
    if guidance_scale < 1.0 or guidance_scale > 5.0:
        return jsonify({"error": "Guidance scale must be between 1.0 and 5.0"}), 400
    
    if num_inference_steps < 32 or num_inference_steps > 128:
        return jsonify({"error": "Number of inference steps must be between 32 and 128"}), 400
    
    # Validate output format
    if output_format not in ['obj', 'glb']:
        return jsonify({"error": "Unsupported output format. Use 'obj' or 'glb'"}), 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': output_format,
        'created_at': time.time()
    }
    
    # Process function with timeout
    @with_timeout(TIMEOUT_SECONDS)
    def process_with_timeout(image, steps, scale, format):
        # Load model
        pipe = load_model()
        processing_jobs[job_id]['progress'] = 30
        
        # Generate 3D model
        return pipe(
            image,
            guidance_scale=scale,
            num_inference_steps=steps,
            output_type="mesh",
        ).images
    
    # Start processing in a separate thread
    def process_image():
        thread = threading.current_thread()
        processing_jobs[job_id]['thread_alive'] = lambda: thread.is_alive()
        
        try:
            # Preprocess image (resize if needed)
            processing_jobs[job_id]['progress'] = 5
            image = preprocess_image(filepath)
            processing_jobs[job_id]['progress'] = 10
            
            # Process image with timeout
            try:
                images = process_with_timeout(image, num_inference_steps, guidance_scale, output_format)
                processing_jobs[job_id]['progress'] = 80
            except TimeoutError:
                processing_jobs[job_id]['status'] = 'error'
                processing_jobs[job_id]['error'] = f"Processing timed out after {TIMEOUT_SECONDS} seconds"
                return
            
            # Export based on requested format
            if output_format == 'obj':
                obj_path = os.path.join(output_dir, "model.obj")
                export_to_obj(images[0], obj_path)
                
                # 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")
                
                processing_jobs[job_id]['result_url'] = f"/download/{job_id}"
                processing_jobs[job_id]['preview_url'] = f"/preview/{job_id}"
                
            elif output_format == 'glb':
                from trimesh import Trimesh
                mesh = images[0]
                vertices = mesh.verts
                faces = mesh.faces
                
                # Create a trimesh object
                trimesh_obj = Trimesh(vertices=vertices, faces=faces)
                
                # Export as GLB
                glb_path = os.path.join(output_dir, "model.glb")
                trimesh_obj.export(glb_path)
                
                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
            
            # Clean up temporary file
            if os.path.exists(filepath):
                os.remove(filepath)
            
            # Force garbage collection to free memory
            gc.collect()
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            
        except Exception as e:
            # Handle errors
            error_details = traceback.format_exc()
            processing_jobs[job_id]['status'] = 'error'
            processing_jobs[job_id]['error'] = f"{str(e)}\n{error_details}"
            print(f"Error processing job {job_id}: {str(e)}")
            print(error_details)
            
            # Clean up on error
            if os.path.exists(filepath):
                os.remove(filepath)
    
    # Start processing thread
    processing_thread = threading.Thread(target=process_image)
    processing_thread.daemon = True
    processing_thread.start()
    
    # Return job ID immediately
    return jsonify({"job_id": job_id}), 202  # 202 Accepted

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

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

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

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

@app.route('/', methods=['GET'])
def index():
    return jsonify({
        "message": "Image to 3D API is running", 
        "endpoints": ["/convert", "/progress/<job_id>", "/download/<job_id>", "/preview/<job_id>"]
    }), 200

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