backend/server.py

import os
import sys
import json
import base64
import tempfile
import shutil
import time
import socket
import sqlite3
import hashlib
import hmac
import queue
import threading
import uuid
import subprocess
import requests

# Automatically include local venv site-packages if present
venv_site = os.path.join(os.path.dirname(__file__), '..', 'venv', 'lib', f'python{sys.version_info.major}.{sys.version_info.minor}', 'site-packages')
if os.path.exists(venv_site) and venv_site not in sys.path:
    sys.path.insert(0, os.path.abspath(venv_site))

def sanitize_and_ensure_transparent_subject(img_path, client=None):
    """
    Verifies if an image has a clean transparent background for 3D generation.
    If the image lacks transparency (alpha < 10%), performs an automatic center-weighted
    crop fallback and re-preprocesses it to isolate the central subject.
    """
    try:
        from PIL import Image
        import numpy as np
    except ImportError as ie:
        print(f"[Backend Preprocessing] Pillow or numpy not installed: {ie}. Skipping advanced transparency sanitation.")
        return img_path

    try:
        if not os.path.exists(img_path):
            return img_path

        img = Image.open(img_path).convert('RGBA')
        width, height = img.size
        
        # Calculate alpha coverage
        alpha_channel = np.array(img.split()[3])
        transparent_ratio = np.mean(alpha_channel < 30)
        
        print(f"[Backend Preprocessing] Alpha transparency ratio: {transparent_ratio * 100:.2f}%")
        
        # If image is > 90% solid (less than 10% transparency), remote RMBG failed
        if transparent_ratio < 0.10:
            print("[Backend Preprocessing] Solid image detected (RMBG failed or no transparency). Applying smart center-crop fallback...")
            
            # Crop central 80% to eliminate edge distractions (pillows, beds, frames)
            crop_margin_w = int(width * 0.10)
            crop_margin_h = int(height * 0.10)
            cropped_img = img.crop((crop_margin_w, crop_margin_h, width - crop_margin_w, height - crop_margin_h))
            
            # Save cropped temporary file
            cropped_temp_path = img_path.replace(".png", "_cropped_fallback.png").replace(".jpg", "_cropped_fallback.png")
            cropped_img.save(cropped_temp_path, "PNG")
            
            # Try re-running remote preprocess_image on the cropped subject
            if client:
                try:
                    from gradio_client import handle_file
                    res = client.predict(handle_file(cropped_temp_path), True, api_name="/preprocess_image")
                    path_val = res.get('path') if isinstance(res, dict) else res
                    if path_val and os.path.exists(path_val):
                        img = Image.open(path_val).convert('RGBA')
                        print("[Backend Preprocessing] Re-preprocessing with central focus succeeded!")
                    else:
                        img = cropped_img
                except Exception as e:
                    print(f"[Backend Preprocessing] Re-preprocessing fallback warning: {e}")
                    img = cropped_img
            else:
                img = cropped_img

        # Scale subject down slightly so it occupies ~80% of the canvas with generous margins (prevents border distortions)
        target_size = int(max_dim * 0.82)
        ratio = min(target_size / img.size[0], target_size / img.size[1])
        new_w = max(1, int(img.size[0] * ratio))
        new_h = max(1, int(img.size[1] * ratio))
        img_resized = img.resize((new_w, new_h), Image.Resampling.LANCZOS)

        # Pad and center on a square transparent canvas with margin
        square_canvas = Image.new('RGBA', (max_dim, max_dim), (0, 0, 0, 0))
        offset_x = (max_dim - new_w) // 2
        offset_y = (max_dim - new_h) // 2
        square_canvas.paste(img_resized, (offset_x, offset_y), img_resized)
        
        final_1024 = square_canvas.resize((1024, 1024), Image.Resampling.LANCZOS)
        out_path = img_path.replace(".png", "_preprocessed_clean.png").replace(".jpg", "_preprocessed_clean.png")
        if out_path == img_path:
            out_path = img_path + "_clean.png"
        final_1024.save(out_path, "PNG")
        print(f"[Backend Preprocessing] Clean 1024x1024 padded transparent image prepared: {out_path}")
        return out_path
    except Exception as err:
        print(f"[Backend Preprocessing] Exception in sanitize_and_ensure_transparent_subject: {err}")
        return img_path

# Set a generous timeout (5 minutes) to allow sleeping Hugging Face Spaces to wake up
socket.setdefaulttimeout(300)

# Check if persistent volume is mounted on Hugging Face (/data)
PERSISTENT_DIR = '/data' if (os.path.exists('/data') and os.path.isdir('/data')) else None

def get_db_path():
    if PERSISTENT_DIR:
        return os.path.join(PERSISTENT_DIR, 'users.db')
    return os.path.join(os.path.dirname(__file__), 'data', 'users.db')

def get_generated_dir(subfolder, username=None):
    if PERSISTENT_DIR:
        base = os.path.join(PERSISTENT_DIR, 'generated', subfolder)
    else:
        base = os.path.join(os.path.dirname(__file__), 'generated', subfolder)
    
    if username:
        return os.path.join(base, username)
    return base

def calculate_3d_cost(resolution, texture_size):
    cost = 5
    try:
        res_val = int(resolution)
        if res_val >= 1536:
            cost += 2
    except ValueError:
        if str(resolution) == '1536':
            cost += 2

    try:
        tex_val = int(texture_size)
        if tex_val >= 4096:
            cost += 3
    except ValueError:
        pass
    return cost

def get_db_connection():
    """
    Returns a thread-safe SQLite connection configured with WAL (Write-Ahead Logging)
    and a generous timeout to support simultaneous concurrent database access across multiple worker threads.
    """
    db_path = get_db_path()
    conn = sqlite3.connect(db_path, timeout=30.0)
    conn.execute("PRAGMA journal_mode=WAL;")
    conn.execute("PRAGMA synchronous=NORMAL;")
    return conn

def init_db():
    db_path = get_db_path()
    os.makedirs(os.path.dirname(db_path), exist_ok=True)
    conn = get_db_connection()
    cursor = conn.cursor()
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS users (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            username TEXT UNIQUE NOT NULL,
            password_hash TEXT NOT NULL,
            salt TEXT NOT NULL,
            created_at REAL NOT NULL
        )
    ''')
    
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS jobs (
            id TEXT PRIMARY KEY,
            username TEXT NOT NULL,
            type TEXT NOT NULL,
            status TEXT NOT NULL,
            progress INTEGER DEFAULT 0,
            message TEXT,
            result TEXT,
            created_at REAL NOT NULL,
            updated_at REAL NOT NULL
        )
    ''')
    
    # Migration checks for user profile columns
    cursor.execute("PRAGMA table_info(users)")
    columns = [row[1] for row in cursor.fetchall()]
    if 'credits' not in columns:
        cursor.execute("ALTER TABLE users ADD COLUMN credits INTEGER DEFAULT 9999")
    if 'nick' not in columns:
        cursor.execute("ALTER TABLE users ADD COLUMN nick TEXT")
    if 'full_name' not in columns:
        cursor.execute("ALTER TABLE users ADD COLUMN full_name TEXT")
    if 'avatar' not in columns:
        cursor.execute("ALTER TABLE users ADD COLUMN avatar TEXT")
    if 'email' not in columns:
        cursor.execute("ALTER TABLE users ADD COLUMN email TEXT")

    # Ensure all users (new, existing, and guests) receive 9999 credits for testing
    cursor.execute("UPDATE users SET credits = 9999 WHERE credits < 9999 OR credits IS NULL")
        
    conn.commit()
    conn.close()




job_queue = queue.Queue()
ACTIVE_JOBS = {}

def update_job_status(job_id, status, progress=None, message=None, result=None):
    if job_id not in ACTIVE_JOBS:
        ACTIVE_JOBS[job_id] = {
            "status": status,
            "progress": progress or 0,
            "message": message or "",
            "result": result
        }
    else:
        ACTIVE_JOBS[job_id]["status"] = status
        if progress is not None:
            ACTIVE_JOBS[job_id]["progress"] = progress
        if message is not None:
            ACTIVE_JOBS[job_id]["message"] = message
        if result is not None:
            ACTIVE_JOBS[job_id]["result"] = result

    try:
        conn = get_db_connection()
        cursor = conn.cursor()
        
        now = time.time()
        updates = [("status", status), ("updated_at", now)]
        if progress is not None:
            updates.append(("progress", progress))
        if message is not None:
            updates.append(("message", message))
        if result is not None:
            if isinstance(result, (dict, list)):
                result_str = json.dumps(result)
            else:
                result_str = str(result)
            updates.append(("result", result_str))
            
        set_clause = ", ".join([f"{col} = ?" for col, _ in updates])
        values = [val for _, val in updates]
        values.append(job_id)
        
        cursor.execute(f"UPDATE jobs SET {set_clause} WHERE id = ?", values)
        conn.commit()
        conn.close()
    except Exception as e:
        print(f"[Backend Error updating job status] job={job_id} error={e}")

def get_supabase_headers():
    url = os.environ.get("SUPABASE_URL", "https://hskkswijqervbpibwvfh.supabase.co")
    key = os.environ.get("SUPABASE_SERVICE_ROLE_KEY") or os.environ.get("SUPABASE_ANON_KEY") or "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Imhza2tzd2lqcWVydmJwaWJ3dmZoIiwicm9sZSI6ImFub24iLCJpYXQiOjE3ODU4MzMyNDgsImV4cCI6MjEwMTQwOTI0OH0.MTHzcMyVwtq58QNHz5S-J3L6U2G-lySbyl3enA8buWU"
    return url, {
        "apikey": key,
        "Authorization": f"Bearer {key}",
        "Content-Type": "application/json"
    }

def get_user_credits(username):
    if not username:
        return 300
    try:
        supabase_url, headers = get_supabase_headers()
        res = requests.get(f"{supabase_url}/rest/v1/profiles?username=eq.{username}&select=credits", headers=headers, timeout=5)
        if res.status_code == 200:
            data = res.json()
            if data and len(data) > 0:
                sb_credits = data[0].get("credits")
                if sb_credits is not None:
                    try:
                        conn = sqlite3.connect(get_db_path())
                        cursor = conn.cursor()
                        cursor.execute("INSERT OR IGNORE INTO users (username, created_at, credits) VALUES (?, ?, ?)", (username, time.time(), int(sb_credits)))
                        cursor.execute("UPDATE users SET credits = ? WHERE username = ?", (int(sb_credits), username))
                        conn.commit()
                        conn.close()
                    except Exception:
                        pass
                    return int(sb_credits)
    except Exception as e:
        print(f"[Backend Supabase Credits Check Notice] {e}")

    try:
        conn = sqlite3.connect(get_db_path())
        cursor = conn.cursor()
        cursor.execute("SELECT credits FROM users WHERE username = ?", (username,))
        row = cursor.fetchone()
        conn.close()
        if row and row[0] is not None:
            return int(row[0])
    except Exception:
        pass
    
    return 300

def deduct_user_credits(username, amount):
    if not username:
        return 300
    current_credits = get_user_credits(username)
    new_credits = max(0, current_credits - amount)
    
    try:
        supabase_url, headers = get_supabase_headers()
        headers["Prefer"] = "return=minimal"
        requests.patch(f"{supabase_url}/rest/v1/profiles?username=eq.{username}", json={"credits": new_credits}, headers=headers, timeout=5)
    except Exception as e:
        print(f"[Backend Supabase Deduct Credits Notice] {e}")

    try:
        conn = sqlite3.connect(get_db_path())
        cursor = conn.cursor()
        cursor.execute("INSERT OR IGNORE INTO users (username, created_at, credits) VALUES (?, ?, ?)", (username, time.time(), new_credits))
        cursor.execute("UPDATE users SET credits = ? WHERE username = ?", (new_credits, username))
        conn.commit()
        conn.close()
    except Exception:
        pass
def upload_to_supabase_storage(file_path, destination_path, bucket_name=None):
    try:
        if not bucket_name:
            bucket_name = os.environ.get("SUPABASE_BUCKET", "creations")

        if not file_path or not os.path.exists(file_path):
            return None
        supabase_url, headers = get_supabase_headers()
        
        with open(file_path, "rb") as f:
            file_bytes = f.read()

        upload_headers = dict(headers)
        upload_headers["x-upsert"] = "true"
        
        if destination_path.endswith(".png"):
            upload_headers["Content-Type"] = "image/png"
        elif destination_path.endswith(".jpg") or destination_path.endswith(".jpeg"):
            upload_headers["Content-Type"] = "image/jpeg"
        elif destination_path.endswith(".glb"):
            upload_headers["Content-Type"] = "model/gltf-binary"
        elif destination_path.endswith(".fbx"):
            upload_headers["Content-Type"] = "application/octet-stream"

        upload_url = f"{supabase_url}/storage/v1/object/{bucket_name}/{destination_path}"
        res = requests.post(upload_url, headers=upload_headers, data=file_bytes, timeout=30)
        if res.status_code in (200, 201):
            public_url = f"{supabase_url}/storage/v1/object/public/{bucket_name}/{destination_path}"
            print(f"[Supabase Storage] Successfully uploaded {destination_path} -> {public_url}")
            return public_url
        else:
            print(f"[Supabase Storage Upload Warning] HTTP {res.status_code}: {res.text}")
    except Exception as e:
        print(f"[Supabase Storage Upload Exception] {e}")
    return None

def save_model_to_supabase(username, name, glb_url, fbx_url=None, preview_url=None, prompt=""):
    try:
        supabase_url, headers = get_supabase_headers()
        payload = {
            "username": username or "guest",
            "name": name,
            "prompt": prompt or "",
            "glb_url": glb_url,
            "fbx_url": fbx_url or glb_url,
            "preview_url": preview_url or glb_url,
            "created_at": time.time()
        }
        res = requests.post(f"{supabase_url}/rest/v1/models", json=payload, headers=headers, timeout=5)
        if res.status_code in (200, 201):
            print(f"[Supabase Models Table] Saved model entry for {name}")
        else:
            print(f"[Supabase Models Table Warning] HTTP {res.status_code}: {res.text}")
    except Exception as e:
        print(f"[Supabase Models Table Exception] {e}")


def refund_credits(username, amount):
    try:
        conn = get_db_connection()
        cursor = conn.cursor()
        cursor.execute("UPDATE users SET credits = credits + ? WHERE username = ?", (amount, username))
        conn.commit()
        conn.close()
        print(f"[Backend] Successfully refunded {amount} credits to user {username}")
    except Exception as e:
        print(f"[Backend Error refunding credits] user={username} error={e}")

def sanitize_and_ensure_transparent_subject(img_path, client=None):
    try:
        if not img_path or not os.path.exists(img_path):
            return img_path
            
        from PIL import Image
        img = Image.open(img_path)
        img = img.convert('RGBA')
        
        max_dim = max(img.width, img.height)
        if max_dim > 2048:
            scale = 2048 / max_dim
            new_w = int(img.width * scale)
            new_h = int(img.height * scale)
            img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
            
        with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as out_f:
            img.save(out_f.name, format='PNG')
            return out_f.name
    except Exception as e:
        print(f"[Backend Preprocessing Notice] sanitize_and_ensure_transparent_subject notice: {e}")
        return img_path

def execute_job_3d(job_id, username, params):
    temp_img_path = None
    client = None
    try:
        update_job_status(job_id, 'processing', progress=10, message="Iniciando generación de malla 3D...")
        
        version = params.get('version', 'v2')
        image_data_b64 = params.get('image') # Base64 encoded image
        hf_token = params.get('token', '')
        seed = float(params.get('seed', 0))
        resolution = params.get('resolution', '1024')
        decimation_target = int(params.get('decimation_target', 300000))
        texture_size = int(params.get('texture_size', 2048))
        ss_guidance = float(params.get('ss_guidance', 7.5))
        ss_steps = int(params.get('ss_steps', 12))
        slat_guidance = float(params.get('slat_guidance', 3.0))
        slat_steps = int(params.get('slat_steps', 12))
        auto_optimize = params.get('auto_optimize', False)
        quad_target_faces = int(params.get('quad_target_faces', 60000))
        prompt = params.get('prompt', '')
        remesh_method = params.get('remeshMethod', 'cleanup')

        if not image_data_b64:
            raise Exception("No image data provided")

        if isinstance(image_data_b64, str) and ('generated_images' in image_data_b64 or 'generated_models' in image_data_b64):
            clean_url = image_data_b64.split('?')[0]
            parts = [p for p in clean_url.split('/') if p]
            
            disk_path = None
            if len(parts) >= 3 and parts[-3] == 'generated_images':
                disk_path = os.path.join(get_generated_dir('images', parts[-2]), parts[-1])
            elif len(parts) >= 3 and parts[-3] == 'generated_models':
                disk_path = os.path.join(get_generated_dir('models', parts[-2]), parts[-1])
            else:
                rel_path = clean_url.lstrip('/')
                if os.path.exists(rel_path):
                    disk_path = rel_path

            if disk_path and os.path.exists(disk_path):
                with open(disk_path, 'rb') as f:
                    image_bytes = f.read()
            else:
                raise Exception(f"No se encontró la imagen en el servidor: {clean_url}")
        elif isinstance(image_data_b64, str) and os.path.exists(image_data_b64):
            with open(image_data_b64, 'rb') as f:
                image_bytes = f.read()
        else:
            if ',' in image_data_b64:
                image_data_b64 = image_data_b64.split(',')[1]
            image_bytes = base64.b64decode(image_data_b64)

        # Save to temporary file
        with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_img:
            temp_img.write(image_bytes)
            temp_img_path = temp_img.name

        # Setup Connection options
        connect_options = {}
        current_token = os.environ.get('HF_TOKEN', '').strip()
        hf_token_clean = str(hf_token).strip() if hf_token else ''
        if hf_token_clean in ('null', 'undefined'):
            hf_token_clean = ''
        
        is_hf_space = 'SPACE_ID' in os.environ
        if is_hf_space:
            token_to_use = hf_token_clean if hf_token_clean else current_token
        else:
            token_to_use = current_token
        if token_to_use == 'PON_TU_TOKEN_AQUI':
            token_to_use = ''
        token_to_use = token_to_use.strip()
        
        if token_to_use:
            connect_options['token'] = token_to_use
        else:
            raise Exception("Falta el Token de Hugging Face. Por favor, asegúrate de que esté configurado.")

        target_space = os.environ.get('HF_SPACE', 'LogicalTrue/TRELLIS.2')
        
        update_job_status(job_id, 'processing', progress=20, message="Verificando estado del servidor de IA...")
        stage = get_space_status(target_space, token_to_use)
        if stage == "PAUSED":
            raise Exception(f"El Space '{target_space}' está PAUSADO.")
        elif stage in ("STOPPED", "ERROR"):
            raise Exception(f"El Space '{target_space}' está APAGADO o tiene un ERROR (Estado: {stage}).")
        elif stage == "SLEEPING":
            update_job_status(job_id, 'processing', progress=25, message="Despertando servidor de IA (esto demora 2-3 minutos)...")
        
        update_job_status(job_id, 'processing', progress=30, message="Conectando al servidor de IA...")
        client = Client(target_space, **connect_options)
        
        try:
            client.predict(api_name="/start_session")
        except Exception as se:
            print(f"[Backend] Remote session initialization warning: {se}")

        # Preprocessing & Background Removal Pipeline
        preprocessed_img_path = temp_img_path
        try:
            update_job_status(job_id, 'processing', progress=40, message="Removiendo fondo de imagen (Pre-procesamiento)...")
            preprocess_result = client.predict(handle_file(temp_img_path), True, api_name="/preprocess_image")
            path_val = preprocess_result.get('path') if isinstance(preprocess_result, dict) else preprocess_result
            if path_val and os.path.exists(str(path_val)):
                preprocessed_img_path = str(path_val)
        except Exception as pe:
            print(f"[Backend] Primary Trellis /preprocess_image failed or missing argument: {pe}. Trying RMBG-1.4 fallback...")
            try:
                rmbg_client = Client("briaai/BRIA-RMBG-1.4", **connect_options)
                rmbg_res = rmbg_client.predict(handle_file(temp_img_path), api_name="/rmbg")
                path_val = rmbg_res.get('path') if isinstance(rmbg_res, dict) else rmbg_res
                if path_val and os.path.exists(str(path_val)):
                    preprocessed_img_path = str(path_val)
                    print("[Backend] RMBG-1.4 dedicated background removal succeeded!")
            except Exception as rmbg_err:
                print(f"[Backend] Dedicated RMBG-1.4 fallback failed: {rmbg_err}")

        # Sanitize and ensure transparent subject padding for 3D reconstruction
        preprocessed_img_path = sanitize_and_ensure_transparent_subject(preprocessed_img_path, client)

        update_job_status(job_id, 'processing', progress=50, message="Construyendo representación 3D (Inferencia de IA)...")
        job = client.submit(
            handle_file(preprocessed_img_path),
            seed,
            resolution,
            ss_guidance,
            0.7,
            ss_steps,
            5.0,
            slat_guidance,
            0.5,
            slat_steps,
            3.0,
            1.0,
            0.0,
            12,
            3.0,
            api_name="/image_to_3d"
        )
        job.result()

        update_job_status(job_id, 'processing', progress=75, message="Extrayendo texturas PBR y generando archivo GLB...")
        extract_job = client.submit(
            decimation_target,
            texture_size,
            api_name="/extract_glb"
        )
        extract_result = extract_job.result()

        if hasattr(extract_result, 'data') and extract_result.data and len(extract_result.data) >= 2:
            gltf_file = extract_result.data[0]
            glb_file = extract_result.data[1]
        elif isinstance(extract_result, (list, tuple)) and len(extract_result) >= 2:
            gltf_file = extract_result[0]
            glb_file = extract_result[1]
        else:
            raise Exception("extract_glb no retornó los archivos esperados.")

        output_dir = get_generated_dir("models", username)
        os.makedirs(output_dir, exist_ok=True)

        gltf_local_path = gltf_file.get('path') if isinstance(gltf_file, dict) else (gltf_file if isinstance(gltf_file, str) else None)
        glb_local_path = glb_file.get('path') if isinstance(glb_file, dict) else (glb_file if isinstance(glb_file, str) else None)

        filename = f"model_{int(time.time())}.glb"
        dest_path = os.path.join(output_dir, filename)

        if gltf_local_path and os.path.exists(gltf_local_path):
            shutil.copy(gltf_local_path, dest_path)
            
            gltf_url = f"/generated_models/{username}/{filename}"
            glb_url = f"/generated_models/{username}/{filename}"
            fbx_filename = filename.replace(".glb", ".fbx")
            fbx_dest_path = os.path.join(output_dir, fbx_filename)

            # 1. Process FBX returned directly from Trellis.2 Space
            if glb_local_path and os.path.exists(glb_local_path) and glb_local_path.lower().endswith('.fbx'):
                shutil.copy(glb_local_path, fbx_dest_path)
                fbx_url = f"/generated_models/{username}/{fbx_filename}"
                print(f"[Backend Pipeline] ✓ Received pre-converted FBX directly from Trellis Space: {fbx_filename}")
            elif os.path.exists(fbx_dest_path):
                fbx_url = f"/generated_models/{username}/{fbx_filename}"
            else:
                fbx_url = None

            # Safely release AI client memory
            if client:
                try:
                    client.close()
                except Exception:
                    pass
                client = None
            import gc
            gc.collect()

        else:
            gltf_url = gltf_file.get('url') if isinstance(gltf_file, dict) else gltf_local_path
            glb_url = glb_file.get('url') if isinstance(glb_file, dict) else glb_local_path
            fbx_url = None

        update_job_status(job_id, 'processing', progress=95, message="Clasificando especie del modelo...")
        detected_category = classify_species(image_bytes, prompt, token_to_use)

        if gltf_local_path and os.path.exists(gltf_local_path):
            metadata_path = dest_path.replace(".glb", ".json")
            try:
                with open(metadata_path, "w", encoding="utf-8") as meta_f:
                    json.dump({
                        "detectedCategory": detected_category,
                        "prompt": prompt,
                        "timestamp": time.time()
                    }, meta_f, indent=2)
            except Exception as me:
                print(f"[Backend] Metadata warning: {me}")
            # Sync model file and metadata to Supabase Storage and database
            try:
                bucket_name = os.environ.get("SUPABASE_BUCKET", "creations")
                sb_glb_url = upload_to_supabase_storage(dest_path, f"{username}/{filename}", bucket_name)
                sb_fbx_url = upload_to_supabase_storage(fbx_dest_path, f"{username}/{fbx_filename}", bucket_name) if (fbx_url and os.path.exists(fbx_dest_path)) else None
                save_model_to_supabase(username, filename, sb_glb_url or glb_url, fbx_url=sb_fbx_url or fbx_url, prompt=prompt)
            except Exception as sb_sync_err:
                print(f"[Backend Supabase Sync Warning] {sb_sync_err}")


        # Sync model record to Supabase models table
        try:
            supabase_url = "https://hskkswijqervbpibwvfh.supabase.co"
            supabase_key = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Imhza2tzd2lqcWVydmJwaWJ3dmZoIiwicm9sZSI6ImFub24iLCJpYXQiOjE3ODU4MzMyNDgsImV4cCI6MjEwMTQwOTI0OH0.MTHzcMyVwtq58QNHz5S-J3L6U2G-lySbyl3enA8buWU"
            headers = {
                "apikey": supabase_key,
                "Authorization": f"Bearer {supabase_key}",
                "Content-Type": "application/json",
                "Prefer": "return=minimal"
            }
            user_res = requests.get(f"{supabase_url}/rest/v1/profiles?username=eq.{username}&select=id", headers=headers, timeout=3)
            user_uuid = None
            if user_res.status_code == 200:
                u_data = user_res.json()
                if u_data and len(u_data) > 0:
                    user_uuid = u_data[0].get("id")

            model_payload = {
                "user_id": user_uuid,
                "name": filename,
                "prompt": prompt,
                "glb_url": glb_url,
                "fbx_url": fbx_url,
                "preview_url": glb_url,
                "status": "completed"
            }
            requests.post(f"{supabase_url}/rest/v1/models", json=model_payload, headers=headers, timeout=3)
            print(f"[Backend Supabase Models] ✓ Saved model {filename} to Supabase models table.")
        except Exception as sb_mod_err:
            print(f"[Backend Supabase Models Notice] {sb_mod_err}")

        result_payload = {
            "gltfUrl": gltf_url,
            "glbUrl": glb_url,
            "fbxUrl": fbx_url,
            "detectedCategory": detected_category
        }
        
        update_job_status(job_id, 'completed', progress=100, message="Generación 3D completada con éxito.", result=result_payload)
        
    except Exception as ex:
        print(f"[Backend Job Error] job={job_id} error={ex}")
        update_job_status(job_id, 'failed', message=f"Fallo en la generación: {str(ex)}")
        # Refund credits
        refund_credits(username, params.get('cost', 5))
    finally:
        if temp_img_path:
            try:
                os.unlink(temp_img_path)
            except:
                pass
        if client:
            try:
                client.close()
            except:
                pass

def execute_job_2d(job_id, username, params):
    from gradio_client import Client as GradioClient
    try:
        update_job_status(job_id, 'processing', progress=20, message="Conectando al servidor FLUX de imágenes...")
        prompt = params.get('prompt', '')
        hf_token = params.get('token', '')

        current_token = os.environ.get('HF_TOKEN', '').strip()
        hf_token_clean = str(hf_token).strip() if hf_token else ''
        if hf_token_clean in ('null', 'undefined'):
            hf_token_clean = ''
        
        is_hf_space = 'SPACE_ID' in os.environ
        if is_hf_space:
            token_to_use = hf_token_clean if hf_token_clean else current_token
        else:
            token_to_use = current_token
        if token_to_use == 'PON_TU_TOKEN_AQUI':
            token_to_use = ''
        token_to_use = token_to_use.strip()

        connect_options = {}
        if token_to_use:
            connect_options['token'] = token_to_use
        else:
            raise Exception("Falta el Token de Hugging Face.")

        spaces_to_try = [
            "black-forest-labs/FLUX.1-schnell",
            "multimodalart/FLUX.1-schnell"
        ]

        success = False
        response_data = None
        last_error = None

        for space_name in spaces_to_try:
            client = None
            try:
                update_job_status(job_id, 'processing', progress=40, message=f"Generando imagen vía {space_name}...")
                client = GradioClient(space_name, **connect_options)
                result = client.predict(
                    prompt=prompt,
                    seed=0,
                    randomize_seed=True,
                    width=1024,
                    height=1024,
                    num_inference_steps=4,
                    api_name="/infer"
                )

                if isinstance(result, (list, tuple)) and len(result) > 0:
                    img_local_path = result[0]
                elif isinstance(result, dict) and 'path' in result:
                    img_local_path = result['path']
                else:
                    img_local_path = result

                if img_local_path and os.path.exists(img_local_path):
                    with open(img_local_path, "rb") as img_file:
                        img_bytes = img_file.read()
                    
                    images_dir = get_generated_dir("images", username)
                    os.makedirs(images_dir, exist_ok=True)
                    img_filename = f"image_{int(time.time())}.png"
                    img_dest_path = os.path.join(images_dir, img_filename)
                    with open(img_dest_path, "wb") as f:
                        f.write(img_bytes)

                    # Upload 2D image to Supabase Storage bucket 'creations'
                    bucket_name = os.environ.get("SUPABASE_BUCKET", "creations")
                    sb_img_url = upload_to_supabase_storage(img_dest_path, f"{username}/{img_filename}", bucket_name)
                    save_model_to_supabase(username, img_filename, sb_img_url or f"/generated_images/{username}/{img_filename}", prompt=prompt)


                    img_b64 = base64.b64encode(img_bytes).decode('utf-8')
                    response_data = {
                        "image": f"data:image/png;base64,{img_b64}",
                        "imageUrl": sb_img_url or f"/generated_images/{username}/{img_filename}",
                        "model_used": space_name
                    }
                    success = True
                    break

                else:
                    raise Exception(f"La ruta devuelta no existe: {img_local_path}")
            except Exception as ex:
                last_error = str(ex)
            finally:
                if client:
                    try:
                        client.close()
                    except:
                        pass

        if success and response_data:
            update_job_status(job_id, 'completed', progress=100, message="Generación de imagen completada.", result=response_data)
        else:
            raise Exception(f"Fallaron todos los Spaces de FLUX. Último error: {last_error}")

    except Exception as ex:
        print(f"[Backend Job Error] job={job_id} error={ex}")
        update_job_status(job_id, 'failed', message=f"Error al generar imagen 2D: {str(ex)}")
        refund_credits(username, params.get('cost', 1))

def background_worker(worker_id):
    print(f"[Backend Background Worker #{worker_id}] Starting worker thread...")
    while True:
        try:
            job = job_queue.get()
            if job is None:
                break
                
            job_id = job["id"]
            username = job["username"]
            job_type = job["type"]
            params = job["params"]
            
            print(f"[Backend Background Worker #{worker_id}] Processing job={job_id} user={username} type={job_type}")
            if job_type == '3d':
                execute_job_3d(job_id, username, params)
            elif job_type == '2d':
                execute_job_2d(job_id, username, params)
                
            job_queue.task_done()
        except Exception as we:
            print(f"[Backend Background Worker #{worker_id} Exception] {we}")
            time.sleep(1)

# Spawn a pool of worker threads for parallel job processing
NUM_WORKER_THREADS = 4
worker_threads = []
for i in range(NUM_WORKER_THREADS):
    t = threading.Thread(target=background_worker, args=(i + 1,), daemon=True)
    t.start()
    worker_threads.append(t)

from http.server import SimpleHTTPRequestHandler, ThreadingHTTPServer
# pyrefly: ignore [missing-import]
from gradio_client import Client, handle_file

if hasattr(sys.stdout, "reconfigure"):
    sys.stdout.reconfigure(encoding="utf-8")

# Simple environment loader to avoid external dependencies
def load_dotenv():
    env_path = os.path.join(os.path.dirname(__file__), '.env')
    if os.path.exists(env_path):
        with open(env_path, 'r', encoding='utf-8') as f:
            for line in f:
                line = line.strip()
                if line and not line.startswith('#') and '=' in line:
                    key, val = line.split('=', 1)
                    key_str = key.strip()
                    val_str = val.strip()
                    
                    is_hf_space = 'SPACE_ID' in os.environ
                    current_val = os.environ.get(key_str, '').strip()
                    
                    # Update/overwrite if:
                    # 1. Variable not already set in environment
                    # 2. Or current value is empty/placeholder
                    # 3. Or we are running locally (not HF Spaces)
                    if (key_str not in os.environ or 
                        current_val in ('', 'PON_TU_TOKEN_AQUI', 'null', 'undefined') or 
                        not is_hf_space):
                        # Avoid overwriting a valid token in the environment with a placeholder from .env
                        if not (val_str == 'PON_TU_TOKEN_AQUI' and current_val.startswith('hf_')):
                            os.environ[key_str] = val_str

# Initialize configuration
load_dotenv()
HF_TOKEN = os.environ.get('HF_TOKEN', '')
HF_SPACE = os.environ.get('HF_SPACE', 'LogicalTrue/TRELLIS.2')

# Hugging Face Spaces always runs on port 7860
if 'SPACE_ID' in os.environ:
    PORT = 7860
    print(f"[Backend] Running inside Hugging Face Space. Forcing PORT to {PORT}")
else:
    PORT = int(os.environ.get('PORT', '8000'))

if not HF_TOKEN or HF_TOKEN == 'PON_TU_TOKEN_AQUI':
    print("\n[⚠️ WARNING] HF_TOKEN is not configured or has default placeholder value in .env file.")
    print("Please open the '.env' file and insert your Hugging Face Token (hf_...) to access your private Space.\n")

def get_space_status(space_id, token=None):
    """
    Checks the current status of a Hugging Face Space.
    Returns the stage string, e.g. 'RUNNING', 'SLEEPING', 'PAUSED', 'STOPPED', 'ERROR', or 'UNKNOWN'.
    """
    import requests
    url = f"https://huggingface.co/api/spaces/{space_id}"
    headers = {}
    if token:
        headers["Authorization"] = f"Bearer {token}"
    try:
        r = requests.get(url, headers=headers, timeout=5)
        if r.status_code == 200:
            data = r.json()
            runtime = data.get("runtime", {})
            stage = runtime.get("stage", "UNKNOWN").upper()
            return stage
        else:
            print(f"[Space Status] Failed to fetch status for {space_id}: HTTP {r.status_code}")
            return "UNKNOWN"
    except Exception as e:
        print(f"[Space Status] Error checking status for {space_id}: {e}")
        return "UNKNOWN"

def classify_species(image_bytes, prompt_text, hf_token):
    p = prompt_text.lower() if prompt_text else ""
    
    # Spider / Insect keywords
    spider_words = ["spider", "araña", "aracnido", "arachnid", "tarantula", "insect", "insecto", "crab", "cangrejo", "scorpion", "escorpion", "bug"]
    if any(w in p for w in spider_words):
        print(f"[Classifier] Detected 'unsupported' category from prompt: '{prompt_text}'")
        return "unsupported"
        
    # Quadruped keywords
    quad_words = ["horse", "caballo", "dog", "perro", "cat", "gato", "wolf", "lobo", "lion", "leon", "tiger", "tigre", "cow", "vaca", "sheep", "oveja", "pig", "cerdo", "fox", "zorro", "deer", "ciervo", "bear", "oso", "quadruped", "cuadrupedo", "animal", "camel", "camello", "elephant", "elefante", "giraffe", "jirafa"]
    if any(w in p for w in quad_words):
        print(f"[Classifier] Detected 'local_quadruped' category from prompt: '{prompt_text}'")
        return "local_quadruped"

    # Humanoid keywords
    humanoid_words = ["human", "humano", "man", "hombre", "woman", "mujer", "boy", "chico", "girl", "chica", "character", "personaje", "soldier", "soldado", "warrior", "guerrero", "wizard", "mago", "hero", "heroe", "knight", "caballero", "robot", "biped", "bipedo", "alien", "cyborg", "golem"]
    if any(w in p for w in humanoid_words):
        print(f"[Classifier] Detected 'ai' category from prompt: '{prompt_text}'")
        return "ai"

    # 2. Image classification fallback via CLIP on HF
    if not hf_token or hf_token in ('null', 'undefined'):
        print("[Classifier] No HF Token for image classification. Defaulting to 'ai'.")
        return "ai"
        
    try:
        import requests
        headers = {"Authorization": f"Bearer {hf_token}"}
        api_url = "https://api-inference.hf.co/models/openai/clip-vit-large-patch14"
        img_b64 = base64.b64encode(image_bytes).decode('utf-8')
        
        payload = {
            "image": img_b64,
            "parameters": {
                "candidate_labels": [
                    "a bipedal humanoid character or person",
                    "a four-legged animal or quadruped",
                    "a spider or multi-legged insect",
                    "an object, prop or static furniture"
                ]
            }
        }
        
        print("[Classifier] Querying CLIP zero-shot classification on Hugging Face...")
        response = requests.post(api_url, headers=headers, json=payload, timeout=8)
        if response.status_code == 200:
            res_data = response.json()
            if isinstance(res_data, list) and len(res_data) > 0:
                best_label = res_data[0].get("label", "")
                score = res_data[0].get("score", 0.0)
                print(f"[Classifier] CLIP result: {best_label} (score: {score:.3f})")
                
                if "bipedal" in best_label:
                    return "ai"
                elif "four-legged" in best_label:
                    return "local_quadruped"
                elif "spider" in best_label:
                    return "unsupported"
                else:
                    return "unsupported"
        else:
            print(f"[Classifier Notice] HF CLIP endpoint status {response.status_code}. Defaulting to 'ai'.")
    except Exception as e:
        print(f"[Classifier] Image classification failed: {e}")
        
    return "ai"

class F23DHTTPRequestHandler(SimpleHTTPRequestHandler):
    def translate_path(self, path):
        import urllib
        path = urllib.parse.unquote(path)
        path = path.split('?', 1)[0]
        path = path.split('#', 1)[0]
        
        if path == '/' or path == '':
            return os.path.join(os.path.dirname(__file__), '..', 'frontend', 'index.html')
            
        parts = [p for p in path.split('/') if p]
        
        if parts:
            if parts[0] == 'generated_images':
                subpath = os.path.join(*parts[1:]) if len(parts) > 1 else ''
                return os.path.join(get_generated_dir('images'), subpath)
            elif parts[0] == 'generated_models':
                subpath = os.path.join(*parts[1:]) if len(parts) > 1 else ''
                return os.path.join(get_generated_dir('models'), subpath)
            elif parts[0] in ('app.js', 'styles.css', 'index.html'):
                return os.path.join(os.path.dirname(__file__), '..', 'frontend', parts[0])
                
        return os.path.join(os.path.dirname(__file__), '..', 'frontend', *parts)

    def end_headers(self):
        self.send_header('Access-Control-Allow-Origin', '*')
        self.send_header('Access-Control-Allow-Methods', 'GET, POST, OPTIONS')
        self.send_header('Access-Control-Allow-Headers', 'Content-Type, Authorization')
        self.send_header('Cache-Control', 'no-store, no-cache, must-revalidate, max-age=0')
        self.send_header('Pragma', 'no-cache')
        self.send_header('Expires', '0')
        super().end_headers()

    def do_OPTIONS(self):
        self.send_response(200, "OK")
        self.end_headers()

    def get_logged_in_user(self):
        # 1. Check X-Session-User header (most reliable for cross-domain/HTTPS SPA)
        custom_header = self.headers.get('X-Session-User')
        if custom_header and custom_header.strip() and custom_header.strip() not in ('undefined', 'null', 'guest'):
            return custom_header.strip()

        cookie_header = self.headers.get('Cookie', '')
        if cookie_header:
            cookies = {}
            for item in cookie_header.split(';'):
                item = item.strip()
                if '=' in item:
                    k, v = item.split('=', 1)
                    cookies[k.strip()] = v.strip()
            user = cookies.get('session_user')
            if user and user not in ('undefined', 'null'):
                return user


        # Automatic guest fallback so 3D model generation works out of the box
        guest_user = 'guest'
        try:
            db_path = get_db_path()
            conn = sqlite3.connect(db_path)
            cursor = conn.cursor()
            cursor.execute("SELECT credits FROM users WHERE username = ?", (guest_user,))
            row = cursor.fetchone()
            if not row:
                cursor.execute("INSERT INTO users (username, password_hash, salt, created_at, credits) VALUES (?, ?, ?, ?, ?)",
                               (guest_user, 'guest_hash', 'guest_salt', time.time(), 300))
            else:
                cursor.execute("UPDATE users SET credits = 300 WHERE username = ?", (guest_user,))
            conn.commit()
            conn.close()

        except Exception as e:
            print(f"[Backend Guest Fallback Warning] {e}")

        return guest_user



    def handle_job_status(self):
        try:
            import urllib.parse
            parsed_path = urllib.parse.urlparse(self.path)
            query_params = urllib.parse.parse_qs(parsed_path.query)
            job_id = query_params.get('job_id', [None])[0]

            if not job_id:
                self.send_error_response(400, "Falta job_id")
                return

            if job_id in ACTIVE_JOBS:
                job_info = ACTIVE_JOBS[job_id]
                response_data = {
                    "success": True,
                    "job_id": job_id,
                    "status": job_info["status"],
                    "progress": job_info["progress"],
                    "message": job_info["message"],
                    "result": job_info.get("result")
                }
                self.send_response(200)
                self.send_header('Content-Type', 'application/json')
                self.end_headers()
                self.wfile.write(json.dumps(response_data).encode('utf-8'))
                return

            db_path = get_db_path()
            conn = sqlite3.connect(db_path)
            cursor = conn.cursor()
            cursor.execute("SELECT id, status, progress, message, result FROM jobs WHERE id = ?", (job_id,))
            row = cursor.fetchone()
            conn.close()

            if not row:
                self.send_error_response(404, "Trabajo no encontrado")
                return

            j_id, status, progress, message, result_raw = row
            result_data = None
            if result_raw:
                try:
                    result_data = json.loads(result_raw)
                except Exception:
                    result_data = result_raw

            response_data = {
                "success": True,
                "job_id": j_id,
                "status": status,
                "progress": progress,
                "message": message,
                "result": result_data
            }

            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps(response_data).encode('utf-8'))

        except Exception as e:
            print(f"[Backend Error in handle_job_status] {e}")
            self.send_error_response(500, str(e))

    def handle_user_active_job(self):
        try:
            username = self.get_logged_in_user()
            if not username:
                self.send_error_response(401, "No has iniciado sesión")
                return

            db_path = get_db_path()
            conn = sqlite3.connect(db_path)
            cursor = conn.cursor()
            cursor.execute("SELECT id, status, progress, message, result FROM jobs WHERE username = ? AND status IN ('pending', 'processing') ORDER BY created_at DESC LIMIT 1", (username,))
            row = cursor.fetchone()
            conn.close()

            if not row:
                self.send_response(200)
                self.send_header('Content-Type', 'application/json')
                self.end_headers()
                self.wfile.write(json.dumps({"has_active_job": False}).encode('utf-8'))
                return

            j_id, status, progress, message, result_raw = row
            result_data = None
            if result_raw:
                try:
                    result_data = json.loads(result_raw)
                except Exception:
                    result_data = result_raw

            response_data = {
                "has_active_job": True,
                "job_id": j_id,
                "status": status,
                "progress": progress,
                "message": message,
                "result": result_data
            }

            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps(response_data).encode('utf-8'))

        except Exception as e:
            print(f"[Backend Error in handle_user_active_job] {e}")
            self.send_error_response(500, str(e))

    def handle_get_gallery(self):
        try:
            username = self.get_logged_in_user() or "guest"
            models_list = []

            # 1. Query Supabase models table
            try:
                supabase_url, headers = get_supabase_headers()
                sb_res = requests.get(f"{supabase_url}/rest/v1/models?select=*&order=created_at.desc&limit=50", headers=headers, timeout=5)
                if sb_res.status_code == 200:
                    sb_models = sb_res.json()
                    for m in sb_models:
                        glb = m.get("output_glb_url") or m.get("glb_url") or ""
                        fbx = m.get("output_fbx_url") or m.get("fbx_url") or ""
                        img = m.get("input_image_url") or m.get("preview_url") or glb
                        m_name = m.get("name") or m.get("title") or "Modelo"

                        if glb or fbx or img:
                            models_list.append({
                                "id": m.get("id") or m_name,
                                "name": m_name,
                                "prompt": m.get("prompt", ""),
                                "glbUrl": glb,
                                "gltfUrl": glb,
                                "fbxUrl": fbx if fbx else None,
                                "previewUrl": img,
                                "createdAt": m.get("created_at") or time.time()
                            })
            except Exception as sb_err:
                print(f"[Backend Gallery Supabase Notice] {sb_err}")

            # 2. Query Supabase Storage buckets directly
            try:
                supabase_url, headers = get_supabase_headers()
                for bucket in ("creations", "models-3d"):
                    st_res = requests.post(f"{supabase_url}/storage/v1/object/list/{bucket}", json={"prefix": f"{username}/"}, headers=headers, timeout=5)
                    if st_res.status_code == 200:
                        objects = st_res.json()
                        for obj in objects:
                            obj_name = obj.get("name")
                            if obj_name and obj_name.endswith(('.glb', '.png', '.jpg', '.jpeg', '.fbx')):
                                pub_url = f"{supabase_url}/storage/v1/object/public/{bucket}/{username}/{obj_name}"
                                if not any(x.get("name") == obj_name or x.get("id") == obj_name for x in models_list):
                                    models_list.append({
                                        "id": obj_name,
                                        "name": obj_name,
                                        "prompt": "",
                                        "glbUrl": pub_url,
                                        "gltfUrl": pub_url,
                                        "fbxUrl": pub_url if obj_name.endswith('.fbx') else None,
                                        "previewUrl": pub_url,
                                        "createdAt": time.time()
                                    })
            except Exception as st_err:
                print(f"[Backend Gallery Storage Notice] {st_err}")

            # 3. Fallback scan local generated_models directory if needed
            user_models_dir = get_generated_dir("models", username)
            if os.path.exists(user_models_dir):
                files = sorted(os.listdir(user_models_dir), reverse=True)
                for f in files:
                    if f.endswith('.glb'):
                        if not any(x.get("id") == f or f in str(x.get("glbUrl")) for x in models_list):
                            fbx_f = f.replace('.glb', '.fbx')
                            has_fbx = os.path.exists(os.path.join(user_models_dir, fbx_f))
                            models_list.append({
                                "id": f,
                                "name": f,
                                "prompt": "",
                                "glbUrl": f"/generated_models/{username}/{f}",
                                "gltfUrl": f"/generated_models/{username}/{f}",
                                "fbxUrl": f"/generated_models/{username}/{fbx_f}" if has_fbx else None,
                                "previewUrl": f"/generated_models/{username}/{f}",
                                "createdAt": time.time()
                            })

            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps({"success": True, "models": models_list}).encode('utf-8'))


        except Exception as e:
            print(f"[Backend Error in handle_get_gallery] {e}")
            self.send_error_response(500, str(e))

    def do_GET(self):
        if self.path == '/api/gallery':
            self.handle_get_gallery()
        elif self.path.startswith('/api/space-status'):
            self.handle_space_status()
        elif self.path.startswith('/api/job-status'):
            self.handle_job_status()
        elif self.path == '/api/user-active-job':
            self.handle_user_active_job()
        elif self.path == '/api/me':
            username = self.get_logged_in_user()
            credits = 0
            nick = None
            full_name = None
            avatar = None
            email = None
            if username:
                try:
                    credits = get_user_credits(username)
                    db_path = get_db_path()
                    conn = sqlite3.connect(db_path)
                    cursor = conn.cursor()
                    cursor.execute("SELECT nick, full_name, avatar, email FROM users WHERE username = ?", (username,))
                    row = cursor.fetchone()
                    conn.close()
                    if row:
                        nick = row[0]
                        full_name = row[1]
                        avatar = row[2]
                        email = row[3]
                except Exception as e:
                    print(f"[Backend] Error checking user profile: {e}")




                except Exception as e:
                    print(f"[Backend] Error checking user profile: {e}")
            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps({
                "username": username,
                "credits": credits,
                "nick": nick if nick is not None else "",
                "full_name": full_name or "",
                "avatar": avatar or "",
                "email": email or ""
            }).encode('utf-8'))
        else:
            super().do_GET()

    def do_POST(self):
        if self.path == '/api/register':
            self.handle_register()
        elif self.path == '/api/login':
            self.handle_login()
        elif self.path == '/api/logout':
            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.send_header('Set-Cookie', 'session_user=; Path=/; Expires=Thu, 01 Jan 1970 00:00:00 GMT; Max-Age=0; SameSite=Lax')
            self.send_header('Set-Cookie', 'session_user=; Path=/; Expires=Thu, 01 Jan 1970 00:00:00 GMT; Max-Age=0; SameSite=None; Secure')
            self.end_headers()
            self.wfile.write(json.dumps({"success": True}).encode('utf-8'))
        elif self.path == '/api/update-profile':
            self.handle_update_profile()
        elif self.path == '/api/update-privacy':
            self.handle_update_privacy()
        elif self.path == '/api/generate-3d':
            self.handle_generate_3d()
        elif self.path == '/api/optimize-3d':
            self.handle_optimize_3d()
        elif self.path == '/api/rig-3d':
            self.handle_rig_3d()
        elif self.path == '/api/generate-2d':
            self.handle_generate_2d()
        elif self.path == '/api/delete-gallery':
            self.handle_delete_gallery()
        elif self.path == '/api/save-weights':
            self.handle_save_weights()
        elif self.path == '/api/topup':
            self.handle_topup()
        elif self.path == '/api/create-checkout-session':
            self.handle_create_checkout_session()
        elif self.path == '/api/lemonsqueezy-webhook':
            self.handle_lemonsqueezy_webhook()
        else:
            self.send_error(404, "Endpoint not found")

    def handle_generate_2d(self):
        try:
            username = self.get_logged_in_user() or "guest"
            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))
            prompt = params.get('prompt', '')

            if not prompt:
                self.send_error_response(400, "El prompt es requerido.")
                return

            # Check Together AI key or Pollinations fallback
            together_key = os.environ.get('TOGETHER_API_KEY', '')
            hf_token = os.environ.get('HF_TOKEN', '')
            data_url = None
            model_used = "FLUX.1 Schnell"

            if together_key:
                try:
                    headers = {
                        'Authorization': f'Bearer {together_key}',
                        'Content-Type': 'application/json'
                    }
                    payload = {
                        'model': 'black-forest-labs/FLUX.1-schnell-Free',
                        'prompt': prompt,
                        'width': 1024,
                        'height': 1024,
                        'steps': 4,
                        'n': 1,
                        'response_format': 'b64_json'
                    }
                    res = requests.post('https://api.together.xyz/v1/images/generations', json=payload, headers=headers, timeout=40)
                    if res.status_code == 200:
                        j = res.json()
                        b64 = j.get('data', [{}])[0].get('b64_json')
                        if b64:
                            data_url = f"data:image/jpeg;base64,{b64}"
                            model_used = "FLUX.1 Schnell (Together AI)"
                except Exception as te:
                    print(f"[Backend 2D Together Notice] {te}")

            if not data_url:
                try:
                    import urllib.parse
                    encoded_p = urllib.parse.quote(prompt)
                    poll_url = f"https://image.pollinations.ai/prompt/{encoded_p}?width=512&height=512&model=flux&nologo=true&seed={random.randint(1,99999)}"
                    res = requests.get(poll_url, timeout=35)
                    if res.status_code == 200:
                        b64 = base64.b64encode(res.content).decode('utf-8')
                        data_url = f"data:image/png;base64,{b64}"
                        model_used = "FLUX (Pollinations AI)"
                except Exception as pe:
                    print(f"[Backend 2D Pollinations Notice] {pe}")

            if not data_url:
                self.send_error_response(500, "No se pudo generar la imagen 2D.")
                return

            # Save 2D image file locally
            img_dir = get_generated_dir("images", username)
            os.makedirs(img_dir, exist_ok=True)
            timestamp = int(time.time())
            img_filename = f"image_{timestamp}.png"
            img_path = os.path.join(img_dir, img_filename)

            if ',' in data_url:
                raw_b64 = data_url.split(',')[1]
                with open(img_path, 'wb') as img_f:
                    img_f.write(base64.b64decode(raw_b64))

            image_public_url = f"/generated_images/{username}/{img_filename}"

            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps({
                "success": True,
                "image": data_url,
                "imageUrl": image_public_url,
                "model_used": model_used
            }).encode('utf-8'))

        except Exception as e:
            print(f"[Backend Error in handle_generate_2d] {e}")
            self.send_error_response(500, str(e))
        else:
            self.send_error(404, "Endpoint not found")

    def handle_register(self):
        try:
            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))
            
            username = params.get('username', '').strip().lower()
            password = params.get('password', '')
            
            if not username or not password:
                self.send_error_response(400, "Nombre de usuario y contraseña son obligatorios.")
                return
                
            if not username.isalnum() or len(username) < 3:
                self.send_error_response(400, "El nombre de usuario debe ser alfanumérico y de al menos 3 caracteres.")
                return

            if len(password) < 4:
                self.send_error_response(400, "La contraseña debe tener al menos 4 caracteres.")
                return

            db_path = get_db_path()
            conn = sqlite3.connect(db_path)
            cursor = conn.cursor()
            
            cursor.execute("SELECT id FROM users WHERE username = ?", (username,))
            if cursor.fetchone():
                conn.close()
                self.send_error_response(400, "El nombre de usuario ya está registrado.")
                return
                
            salt = base64.b64encode(os.urandom(16)).decode('utf-8')
            hasher = hashlib.sha256()
            hasher.update((password + salt).encode('utf-8'))
            password_hash = hasher.hexdigest()
            
            cursor.execute(
                "INSERT INTO users (username, password_hash, salt, created_at, credits) VALUES (?, ?, ?, ?, 300)",
                (username, password_hash, salt, time.time())
            )
            conn.commit()
            conn.close()
            
            os.makedirs(get_generated_dir("images", username), exist_ok=True)
            os.makedirs(get_generated_dir("models", username), exist_ok=True)
            
            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.send_header('Set-Cookie', f'session_user={username}; Path=/; Max-Age=2592000; SameSite=Lax')
            self.end_headers()
            self.wfile.write(json.dumps({"success": True, "username": username, "credits": 300}).encode('utf-8'))

            
        except Exception as e:
            self.send_error_response(500, str(e))

    def handle_update_profile(self):
        try:
            username = self.get_logged_in_user()
            if not username:
                self.send_error_response(401, "No has iniciado sesión.")
                return

            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))

            nick = params.get('nick', '').strip()
            full_name = params.get('full_name', '').strip()
            avatar = params.get('avatar', '').strip()

            if not nick:
                self.send_error_response(400, "El apodo / nick no puede estar vacío.")
                return

            db_path = get_db_path()
            conn = sqlite3.connect(db_path)
            cursor = conn.cursor()
            cursor.execute("INSERT OR IGNORE INTO users (username, created_at, credits) VALUES (?, ?, 300)", (username, time.time()))
            cursor.execute(
                "UPDATE users SET nick = ?, full_name = ?, avatar = ? WHERE username = ?",
                (nick, full_name, avatar, username)
            )
            conn.commit()
            conn.close()

            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps({"success": True, "nick": nick, "full_name": full_name, "avatar": avatar}).encode('utf-8'))

        except Exception as e:
            print(f"[Backend] Error updating profile: {e}")
            self.send_error_response(500, str(e))

    def handle_update_privacy(self):
        try:
            username = self.get_logged_in_user()
            if not username:
                self.send_error_response(401, "No has iniciado sesión.")
                return

            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))

            email = params.get('email', '').strip()
            current_password = params.get('current_password', '')
            new_password = params.get('new_password', '')

            db_path = get_db_path()
            conn = sqlite3.connect(db_path)
            cursor = conn.cursor()

            if new_password:
                cursor.execute("SELECT password_hash, salt FROM users WHERE username = ?", (username,))
                user_row = cursor.fetchone()
                if not user_row:
                    conn.close()
                    self.send_error_response(404, "Usuario no encontrado.")
                    return

                stored_hash, salt = user_row[0], user_row[1]
                hasher = hashlib.sha256()
                hasher.update((current_password + salt).encode('utf-8'))
                if hasher.hexdigest() != stored_hash:
                    conn.close()
                    self.send_error_response(400, "La contraseña actual es incorrecta.")
                    return

                new_salt = base64.b64encode(os.urandom(16)).decode('utf-8')
                new_hasher = hashlib.sha256()
                new_hasher.update((new_password + new_salt).encode('utf-8'))
                new_hash = new_hasher.hexdigest()

                cursor.execute(
                    "UPDATE users SET email = ?, password_hash = ?, salt = ? WHERE username = ?",
                    (email, new_hash, new_salt, username)
                )
            else:
                cursor.execute(
                    "UPDATE users SET email = ? WHERE username = ?",
                    (email, username)
                )

            conn.commit()
            conn.close()

            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps({"success": True}).encode('utf-8'))

        except Exception as e:
            print(f"[Backend] Error updating privacy: {e}")
            self.send_error_response(500, str(e))

    def handle_login(self):
        try:
            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))
            
            username = params.get('username', '').strip().lower()
            password = params.get('password', '')
            
            if not username or not password:
                self.send_error_response(400, "Nombre de usuario y contraseña son obligatorios.")
                return

            db_path = get_db_path()
            conn = sqlite3.connect(db_path)
            cursor = conn.cursor()
            
            cursor.execute("SELECT password_hash, salt FROM users WHERE username = ?", (username,))
            row = cursor.fetchone()
            conn.close()
            
            if not row:
                self.send_error_response(400, "Usuario o contraseña incorrectos.")
                return
                
            db_hash, salt = row
            hasher = hashlib.sha256()
            hasher.update((password + salt).encode('utf-8'))
            login_hash = hasher.hexdigest()
            
            if login_hash != db_hash:
                self.send_error_response(400, "Usuario o contraseña incorrectos.")
                return
                
            os.makedirs(get_generated_dir("images", username), exist_ok=True)
            os.makedirs(get_generated_dir("models", username), exist_ok=True)
            
            # Query credits
            credits = 0
            try:
                db_path = get_db_path()
                conn = sqlite3.connect(db_path)
                cursor = conn.cursor()
                cursor.execute("SELECT credits FROM users WHERE username = ?", (username,))
                row = cursor.fetchone()
                conn.close()
                if row:
                    credits = row[0]
            except Exception as e:
                print(f"[Backend] Error checking login credits: {e}")

            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.send_header('Set-Cookie', f'session_user={username}; Path=/; Max-Age=2592000; SameSite=Lax')
            self.end_headers()
            self.wfile.write(json.dumps({"success": True, "username": username, "credits": credits}).encode('utf-8'))
            
        except Exception as e:
            self.send_error_response(500, str(e))

    def handle_topup(self):
        try:
            username = self.get_logged_in_user()
            if not username:
                self.send_error_response(401, "No has iniciado sesión.")
                return

            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))
            amount = int(params.get('amount', 50))

            db_path = get_db_path()
            conn = sqlite3.connect(db_path)
            cursor = conn.cursor()
            cursor.execute("UPDATE users SET credits = credits + ? WHERE username = ?", (amount, username))
            conn.commit()
            cursor.execute("SELECT credits FROM users WHERE username = ?", (username,))
            credits_row = cursor.fetchone()
            conn.close()

            new_credits = credits_row[0] if credits_row else 0
            
            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps({"success": True, "credits": new_credits}).encode('utf-8'))

        except Exception as e:
            self.send_error_response(500, str(e))

    def handle_create_checkout_session(self):
        try:
            username = self.get_logged_in_user()
            if not username:
                self.send_error_response(401, "No has iniciado sesión.")
                return

            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))
            pack_type = str(params.get('pack_type', '25'))

            api_key = os.environ.get('LEMON_SQUEEZY_API_KEY', '').strip()
            store_id = os.environ.get('LEMON_SQUEEZY_STORE_ID', '').strip()
            variant_25 = os.environ.get('LEMON_SQUEEZY_VARIANT_25', '').strip()
            variant_100 = os.environ.get('LEMON_SQUEEZY_VARIANT_100', '').strip()

            host = self.headers.get('Host', 'localhost:8000')
            protocol = 'https' if 'hf.space' in host or 'huggingface.co' in host else 'http'
            base_url = f"{protocol}://{host}"

            amount_credits = 100 if pack_type == '100' else 25
            variant_id = variant_100 if pack_type == '100' else variant_25

            if not api_key or not store_id or not variant_id:
                print("[⚠️ Lemon Squeezy] API keys/Variant IDs missing. Simulating checkout url.")
                mock_url = f"{base_url}/?payment=success"
                
                db_path = get_db_path()
                conn = sqlite3.connect(db_path)
                cursor = conn.cursor()
                cursor.execute("UPDATE users SET credits = credits + ? WHERE username = ?", (amount_credits, username))
                conn.commit()
                conn.close()
                
                self.send_response(200)
                self.send_header('Content-Type', 'application/json')
                self.end_headers()
                self.wfile.write(json.dumps({"url": mock_url}).encode('utf-8'))
                return

            import urllib.request
            import urllib.error

            url = "https://api.lemonsqueezy.com/v1/checkouts"
            req_data = {
                "data": {
                    "type": "checkouts",
                    "attributes": {
                        "product_options": {
                            "redirect_url": f"{base_url}/?payment=success"
                        },
                        "checkout_data": {
                            "custom": {
                                "username": username,
                                "amount": str(amount_credits)
                            }
                        }
                    },
                    "relationships": {
                        "store": {
                            "data": {
                                "type": "stores",
                                "id": str(store_id)
                            }
                        },
                        "variant": {
                            "data": {
                                "type": "variants",
                                "id": str(variant_id)
                            }
                        }
                    }
                }
            }

            req = urllib.request.Request(
                url,
                data=json.dumps(req_data).encode('utf-8'),
                headers={
                    "Authorization": f"Bearer {api_key}",
                    "Content-Type": "application/vnd.api+json",
                    "Accept": "application/vnd.api+json"
                },
                method="POST"
            )

            try:
                with urllib.request.urlopen(req) as response:
                    res_body = response.read().decode('utf-8')
                    res_json = json.loads(res_body)
                    checkout_url = res_json["data"]["attributes"]["url"]
                    
                    self.send_response(200)
                    self.send_header('Content-Type', 'application/json')
                    self.end_headers()
                    self.wfile.write(json.dumps({"url": checkout_url}).encode('utf-8'))
            except urllib.error.HTTPError as http_err:
                err_content = http_err.read().decode('utf-8')
                print(f"[Lemon Squeezy API Error] {http_err.code}: {err_content}")
                self.send_error_response(http_err.code, f"Error de Lemon Squeezy: {err_content}")

        except Exception as e:
            print(f"[Lemon Squeezy Checkout Error] {e}")
            self.send_error_response(500, str(e))

    def handle_lemonsqueezy_webhook(self):
        try:
            content_length = int(self.headers.get('Content-Length', 0))
            payload = self.rfile.read(content_length)
            
            sig_header = self.headers.get('X-Signature', '')
            webhook_secret = os.environ.get('LEMON_SQUEEZY_WEBHOOK_SECRET', '').strip()

            if webhook_secret and webhook_secret != 'PON_TU_WEBHOOK_SECRET_AQUI':
                digest = hmac.new(
                    webhook_secret.encode('utf-8'),
                    payload,
                    hashlib.sha256
                ).hexdigest()
                
                if not hmac.compare_digest(digest, sig_header):
                    print("[⚠️ Lemon Squeezy Webhook] Invalid signature verification.")
                    self.send_response(400)
                    self.end_headers()
                    return
            else:
                print("[⚠️ Lemon Squeezy Webhook] Webhook secret not configured. Bypassing signature check (Developer Mode).")

            event = json.loads(payload.decode('utf-8'))
            event_name = event.get('meta', {}).get('event_name')

            if event_name == 'order_created':
                custom_data = event.get('meta', {}).get('custom_data', {})
                username = custom_data.get('username')
                amount = custom_data.get('amount')

                if username and amount:
                    try:
                        amount = int(amount)
                        db_path = get_db_path()
                        conn = sqlite3.connect(db_path)
                        cursor = conn.cursor()
                        cursor.execute("UPDATE users SET credits = credits + ? WHERE username = ?", (amount, username))
                        conn.commit()
                        conn.close()
                        print(f"[Lemon Squeezy Webhook] Successfully credited {amount} credits to user: {username}")
                    except Exception as db_err:
                        print(f"[Lemon Squeezy Webhook Database Error] {db_err}")
                        self.send_response(500)
                        self.end_headers()
                        return
                else:
                    print(f"[Lemon Squeezy Webhook Warning] Webhook custom_data missing username/amount: {custom_data}")

            self.send_response(200)
            self.end_headers()

        except Exception as e:
            print(f"[Lemon Squeezy Webhook Exception] {e}")
            self.send_response(500)
            self.end_headers()

    def handle_generate_3d(self):
        try:
            username = self.get_logged_in_user()
            if not username:
                self.send_error_response(401, "No has iniciado sesión.")
                return

            load_dotenv() # Reload env dynamically
            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))

            resolution = params.get('resolution', '1024')
            texture_size = int(params.get('texture_size', 2048))
            required_credits = calculate_3d_cost(resolution, texture_size)
            params['cost'] = required_credits # store cost in params for potential refund

            # Check credits dynamically from Supabase
            user_credits = get_user_credits(username)
            if user_credits < required_credits:
                self.send_response(402)
                self.send_header('Content-Type', 'application/json')
                self.end_headers()
                self.wfile.write(json.dumps({"error": f"Créditos insuficientes. Esta generación 3D cuesta {required_credits} créditos (Saldo actual: {user_credits})."}).encode('utf-8'))
                return
            
            # Deduct credits dynamically from Supabase
            new_credits = deduct_user_credits(username, required_credits)


            
            # Create asynchronous job
            job_id = str(uuid.uuid4())
            now = time.time()
            db_path = get_db_path()
            conn = sqlite3.connect(db_path)
            cursor = conn.cursor()
            cursor.execute(
                "INSERT INTO jobs (id, username, type, status, progress, message, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
                (job_id, username, '3d', 'pending', 0, 'En cola de espera...', now, now)
            )
            
            conn.commit()
            conn.close()

            # Push to background worker queue
            job_queue.put({
                "id": job_id,
                "username": username,
                "type": "3d",
                "params": params
            })

            response_data = {
                "success": True,
                "job_id": job_id,
                "status": "pending",
                "credits": new_credits
            }
            
            self.send_response(202) # 202 Accepted
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps(response_data).encode('utf-8'))
            
        except Exception as e:
            print(f"[Backend] Error initiating 3D generation job: {e}")
            self.send_error_response(500, str(e))

    def handle_optimize_3d(self):
        try:
            username = self.get_logged_in_user() or "guest"


            load_dotenv()
            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))

            model_url = params.get('modelUrl', '') # e.g. "/generated_models/model_1782268665.glb"
            quad_target_faces = int(params.get('quad_target_faces', 60000))
            remesh_method = params.get('remeshMethod', 'cleanup')
            if not model_url:
                self.send_error_response(400, "No modelUrl provided")
                return

            if '?' in model_url:
                model_url = model_url.split('?')[0]

            filename = os.path.basename(model_url)

            output_dir = get_generated_dir("models", username)
            dest_path = os.path.join(output_dir, filename)

            fbx_filename = filename.replace(".glb", ".fbx")
            clean_filename = filename if "_quad" in filename else filename.replace(".glb", "_quad.glb")

            # Remote Blender mesh optimization via Hugging Face Space
            target_space = os.environ.get('HF_SPACE', 'LogicalTrue/TRELLIS.2')
            token_to_use = os.environ.get('HF_TOKEN', '')
            connect_options = {"token": token_to_use} if token_to_use else {}


            print(f"[Backend Optimization] Request: user={username}, modelUrl={model_url}, faces={quad_target_faces}, method={remesh_method}")

            if not os.path.exists(dest_path) and model_url:
                try:
                    download_target_url = model_url if model_url.startswith("http") else f"https://hskkswijqervbpibwvfh.supabase.co/storage/v1/object/public/creations/{username}/{filename}"
                    print(f"[Backend Optimization] Downloading GLB from remote: {download_target_url} -> {dest_path}")
                    dl_res = requests.get(download_target_url, timeout=30)
                    if dl_res.status_code == 200 and len(dl_res.content) > 1000:
                        os.makedirs(os.path.dirname(dest_path), exist_ok=True)
                        with open(dest_path, "wb") as f:
                            f.write(dl_res.content)
                        print(f"[Backend Optimization] ✓ Downloaded target GLB model ({len(dl_res.content)} bytes)")
                    else:
                        # Try models-3d bucket fallback
                        fallback_url = f"https://hskkswijqervbpibwvfh.supabase.co/storage/v1/object/public/models-3d/{username}/{filename}"
                        dl_res2 = requests.get(fallback_url, timeout=30)
                        if dl_res2.status_code == 200 and len(dl_res2.content) > 1000:
                            os.makedirs(os.path.dirname(dest_path), exist_ok=True)
                            with open(dest_path, "wb") as f:
                                f.write(dl_res2.content)
                            print(f"[Backend Optimization] ✓ Downloaded target GLB from models-3d bucket ({len(dl_res2.content)} bytes)")
                except Exception as dle:
                    print(f"[Backend Optimization Download Exception] {dle}")

            if os.path.exists(dest_path):
                # 1. Try local Blender if installed AND NOT ON RENDER (to prevent 512MB RAM OOM crash)
                is_render = 'RENDER' in os.environ or 'RENDER_SERVICE_ID' in os.environ
                try:
                    local_blender_script = os.path.join(os.path.dirname(__file__), "scripts", "blender", "clean_mesh_blender.py")
                    if not is_render and os.path.exists(local_blender_script):
                        sh_cmd = ["blender", "-t", "2", "--background", "--python", local_blender_script, "--", dest_path, dest_path, str(quad_target_faces), remesh_method]
                        import subprocess
                        print(f"[Backend Optimization] Attempting local Blender execution (personal PC mode)...")

                        sub_res = subprocess.run(sh_cmd, capture_output=True, text=True, timeout=90)
                        if sub_res.returncode == 0 and os.path.exists(dest_path) and os.path.getsize(dest_path) > 1000:
                            print(f"[Backend Optimization] ✓ Local Blender optimization succeeded!")
                            try:
                                upload_to_supabase_storage(dest_path, f"{username}/{filename}", "creations")
                                fbx_dest = os.path.join(output_dir, fbx_filename)
                                if os.path.exists(fbx_dest):
                                    upload_to_supabase_storage(fbx_dest, f"{username}/{fbx_filename}", "creations")
                            except Exception:
                                pass

                            glb_url = f"/generated_models/{username}/{filename}"
                            fbx_url = f"/generated_models/{username}/{fbx_filename}"
                            self.send_response(200)
                            self.send_header('Content-Type', 'application/json')
                            self.end_headers()
                            self.wfile.write(json.dumps({
                                "success": True,
                                "glbUrl": glb_url,
                                "gltfUrl": glb_url,
                                "fbxUrl": fbx_url if os.path.exists(os.path.join(output_dir, fbx_filename)) else glb_url
                            }).encode('utf-8'))
                            return
                except Exception as lbe:
                    print(f"[Backend Optimization] Local Blender notice (continuing to HF Space): {lbe}")

                # 2. Remote Hugging Face Space optimization
                try:
                    print(f"[Backend Optimization] Connecting to Space '{target_space}' with model at {dest_path} ({os.path.getsize(dest_path)} bytes)...")
                    client = Client(target_space, **connect_options)
                    
                    # Initialize Gradio session if needed
                    try:
                        client.predict(api_name="/start_session")
                    except Exception:
                        pass

                    try:

                        api_info = client.view_api(return_format='dict')
                        named_endpoints = list(api_info.get('named_endpoints', {}).keys())
                        unnamed_endpoints = list(api_info.get('unnamed_endpoints', {}).keys())
                        print(f"[Backend Optimization] Available Space Endpoints: named={named_endpoints}, unnamed={unnamed_endpoints}")
                    except Exception as ve:
                        print(f"[Backend Optimization view_api notice]: {ve}")

                    opt_res = None

                    for api_candidate in ["/optimize_mesh_api", "/optimize_mesh", "/optimize"]:
                        try:
                            print(f"[Backend Optimization] Trying Gradio API name '{api_candidate}'...")
                            opt_res = client.predict(
                                handle_file(dest_path),
                                quad_target_faces,
                                remesh_method,
                                api_name=api_candidate
                            )
                            if opt_res:
                                print(f"[Backend Optimization] ✓ Successfully called API '{api_candidate}'!")
                                break
                        except Exception as api_err:
                            if "Cannot find a function" in str(api_err):
                                continue
                            raise api_err

                    if not opt_res:
                        raise Exception("No se encontró el endpoint /optimize_mesh_api en la API de Gradio de tu Space. Registra el evento en app.py con api_name='optimize_mesh_api'.")

                    print(f"[Backend Optimization] Received HF response: {opt_res}")

                    if isinstance(opt_res, (list, tuple)) and len(opt_res) >= 1:
                        clean_glb = opt_res[0].get('path') if isinstance(opt_res[0], dict) else opt_res[0]
                        clean_fbx = opt_res[1].get('path') if len(opt_res) > 1 and isinstance(opt_res[1], dict) else (opt_res[1] if len(opt_res) > 1 else None)
                        
                        print(f"[Backend Optimization] Clean GLB path: {clean_glb}, Clean FBX path: {clean_fbx}")
                        
                        clean_name = filename.replace(".glb", "_clean.glb") if not "_clean" in filename else filename
                        clean_fbx_name = filename.replace(".glb", "_clean.fbx") if not "_clean" in filename else fbx_filename
                        clean_dest_path = os.path.join(output_dir, clean_name)
                        clean_fbx_dest_path = os.path.join(output_dir, clean_fbx_name)

                        sb_clean_glb_url = None
                        sb_clean_fbx_url = None

                        if clean_glb and os.path.exists(str(clean_glb)) and os.path.getsize(str(clean_glb)) > 1000:
                            shutil.copy(str(clean_glb), clean_dest_path)
                            # Also overwrite original for quick fallback
                            shutil.copy(str(clean_glb), dest_path)
                            print(f"[Backend Optimization] ✓ Saved clean GLB: {clean_dest_path}")
                            try:
                                sb_clean_glb_url = upload_to_supabase_storage(clean_dest_path, f"{username}/{clean_name}", "creations")
                                upload_to_supabase_storage(dest_path, f"{username}/{filename}", "creations")
                            except Exception as upe:
                                print(f"[Backend Optimization Upload Warning] {upe}")
                                
                        if clean_fbx and os.path.exists(str(clean_fbx)) and os.path.getsize(str(clean_fbx)) > 1000:
                            shutil.copy(str(clean_fbx), clean_fbx_dest_path)
                            print(f"[Backend Optimization] ✓ Saved clean FBX: {clean_fbx_dest_path}")
                            try:
                                sb_clean_fbx_url = upload_to_supabase_storage(clean_fbx_dest_path, f"{username}/{clean_fbx_name}", "creations")
                            except Exception as upe:
                                print(f"[Backend Optimization Upload Warning] {upe}")

                        # Save as NEW separate item in Supabase Database!
                        final_glb_url = sb_clean_glb_url or f"/generated_models/{username}/{clean_name}"
                        final_fbx_url = sb_clean_fbx_url or f"/generated_models/{username}/{clean_fbx_name}"
                        try:
                            save_model_to_supabase(username, f"Modelo Optimizado {clean_name}", final_glb_url, fbx_url=final_fbx_url, prompt=f"Optimizado ({remesh_method})")
                        except Exception as sbe:
                            print(f"[Backend Supabase DB Save Notice] {sbe}")

                except Exception as oe:
                    import traceback
                    err_msg = str(oe)
                    print(f"[Backend Remote Optimization Error] {err_msg}\n{traceback.format_exc()}")
                    self.send_error_response(500, f"Error al procesar optimización en Hugging Face: {err_msg}")
                    return
            else:
                self.send_error_response(404, f"No se encontró el archivo del modelo 3D para optimizar: {filename}")
                return

            response_data = {
                "success": True,
                "glbUrl": final_glb_url,
                "gltfUrl": final_glb_url,
                "fbxUrl": final_fbx_url
            }

            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps(response_data).encode('utf-8'))




        except Exception as e:
            print(f"[Backend] Error during 3D optimization: {e}")
            self.send_error_response(500, str(e))

    def handle_rig_3d(self):
        try:
            username = self.get_logged_in_user()
            if not username:
                self.send_error_response(401, "No has iniciado sesión.")
                return

            load_dotenv()
            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))

            model_url = params.get('modelUrl', '') # e.g. "/generated_models/model_1782268665.glb"
            rig_method = params.get('rigMethod', 'ai')
            hf_token = params.get('token', '')

            if not model_url:
                self.send_error_response(400, "No modelUrl provided")
                return

            filename = os.path.basename(model_url)
            output_dir = get_generated_dir("models", username)
            dest_path = os.path.join(output_dir, filename)

            if not os.path.exists(dest_path):
                self.send_error_response(404, f"Model file {filename} not found")
                return

            # Rigging target paths
            base_name, _ = os.path.splitext(filename)
            rigged_fbx_filename = f"{base_name}_rigged.fbx"
            rigged_fbx_path = os.path.join(output_dir, rigged_fbx_filename)

            if rig_method == 'ai':
                rigged_glb_filename = f"{base_name}_rigged.glb"
                rigged_glb_path = os.path.join(output_dir, rigged_glb_filename)
                
                print(f"[Backend] AI Rigging requested via Hugging Face...")
                # Get the correct token
                current_token = os.environ.get('HF_TOKEN', '')
                # Clean token from spaces/quotes
                hf_token_clean = str(hf_token).strip() if hf_token else ''
                if hf_token_clean in ('null', 'undefined'):
                    hf_token_clean = ''
                
                # If running on HF Spaces, prioritize token sent by the client. If running locally, only use the .env token.
                is_hf_space = 'SPACE_ID' in os.environ
                if is_hf_space:
                    token_to_use = hf_token_clean if hf_token_clean else current_token
                else:
                    token_to_use = current_token
                if token_to_use == 'PON_TU_TOKEN_AQUI':
                    token_to_use = ''
                token_to_use = token_to_use.strip()
                
                print(f"[Backend] Client token length: {len(hf_token_clean)}, Env token length: {len(current_token)}, Token to use length: {len(token_to_use)}")
                
                connect_options = {}
                if token_to_use:
                    connect_options['token'] = token_to_use
                
                # Check Space status before calling Gradio
                unirig_space = "LogicalTrue/Unirig"
                print(f"[Backend] Checking status of Hugging Face Space: '{unirig_space}'...")
                stage = get_space_status(unirig_space, token_to_use)
                print(f"[Backend] Checked Space stage: '{stage}'")
                
                if stage == "PAUSED":
                    self.send_error_response(503, f"El Space de Rigging '{unirig_space}' está PAUSADO. Por favor, reanúdalo en la consola de Hugging Face.")
                    return
                elif stage in ("STOPPED", "ERROR"):
                    self.send_error_response(503, f"El Space de Rigging '{unirig_space}' está APAGADO o tiene un ERROR (Estado actual: {stage}).")
                    return
                elif stage == "SLEEPING":
                    print(f"[Backend] ¡Atención! El Space de Rigging '{unirig_space}' está DORMIDO (SLEEPING). Gradio intentará despertarlo (esto puede demorar de 2 a 3 minutos)...")

                # UniRig API call
                from gradio_client import Client, handle_file
                
                print(f"[Backend] Connecting to '{unirig_space}'...")
                client = Client(unirig_space, **connect_options)
                print(f"[Backend] Submitting {filename} to UniRig...")
                res_path = client.predict(
                    handle_file(dest_path),  # archivo_3d
                    12345,                  # seed
                    api_name="/rig_mesh"
                )
                
                if res_path and os.path.exists(res_path):
                    shutil.copy(res_path, rigged_glb_path)
                    print(f"[Backend] ✓ AI Rigging completed successfully. Saved to: {rigged_glb_path}")
                    response_data = {
                        "success": True,
                        "riggedFbxUrl": f"/generated_models/{username}/{rigged_glb_filename}"
                    }
                else:
                    raise Exception("AI Rigging failed: could not retrieve the generated rigged GLB model from Hugging Face Space.")
            else:
                # Local procedural rigging using Blender
                blender_path = os.environ.get('BLENDER_PATH', '')
                if not blender_path or not os.path.exists(blender_path):
                    blender_path = shutil.which("blender") or ""
                if blender_path and os.path.exists(blender_path):
                    script_name = "rig_quadruped_blender.py" if rig_method == "local_quadruped" else "rig_mesh_blender.py"
                    print(f"[Backend] Local procedural rigging ({rig_method}) requested. Running Blender with {script_name}...")
                    import subprocess
                    script_path = os.path.join(os.path.dirname(__file__), "scripts", "blender", script_name)
                    cmd = [blender_path, "--background", "--python", script_path, "--", dest_path, rigged_fbx_path]
                    print(f"[Backend] Executing: {' '.join(cmd)}")
                    result = subprocess.run(cmd, capture_output=True, text=True)
                    
                    print(f"[Backend] Blender Output:\n{result.stdout}")
                    if result.stderr:
                        print(f"[Backend] Blender Errors:\n{result.stderr}")

                    rigged_glb_filename = f"{base_name}_rigged.glb"
                    rigged_glb_path = os.path.join(output_dir, rigged_glb_filename)
                    if result.returncode == 0 and os.path.exists(rigged_fbx_path):
                        has_glb = os.path.exists(rigged_glb_path)
                        response_data = {
                            "success": True,
                            "riggedFbxUrl": f"/generated_models/{username}/{rigged_glb_filename}" if has_glb else f"/generated_models/{username}/{rigged_fbx_filename}"
                        }
                    else:
                        raise Exception(f"Blender rigging failed with exit status {result.returncode}")
                else:
                    raise Exception("BLENDER_PATH is not configured or executable not found locally.")

            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps(response_data).encode('utf-8'))

        except Exception as e:
            print(f"[Backend] Error during rigging: {e}")
            self.send_error_response(500, str(e))

    def handle_generate_2d(self):
        try:
            username = self.get_logged_in_user()
            if not username:
                self.send_error_response(401, "No has iniciado sesión.")
                return

            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))

            prompt = params.get('prompt', '')
            params['cost'] = 1 # 2D image cost is 1 credit

            if not prompt:
                self.send_error_response(400, "No prompt provided")
                return

            # Check credits dynamically from Supabase (2D costs 1 credit)
            required_credits = 1
            user_credits = get_user_credits(username)
            if user_credits < required_credits:
                self.send_response(402)
                self.send_header('Content-Type', 'application/json')
                self.end_headers()
                self.wfile.write(json.dumps({"error": f"Créditos insuficientes. Generar una imagen 2D cuesta {required_credits} crédito (Saldo actual: {user_credits})."}).encode('utf-8'))
                return

            new_credits = deduct_user_credits(username, required_credits)



            # Create async job
            job_id = str(uuid.uuid4())
            now = time.time()
            db_path = get_db_path()
            conn = sqlite3.connect(db_path)
            cursor = conn.cursor()
            cursor.execute(
                "INSERT INTO jobs (id, username, type, status, progress, message, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
                (job_id, username, '2d', 'pending', 0, 'En cola de espera...', now, now)
            )
            conn.commit()
            conn.close()


            # Push to background worker queue
            job_queue.put({
                "id": job_id,
                "username": username,
                "type": "2d",
                "params": params
            })

            response_data = {
                "success": True,
                "job_id": job_id,
                "status": "pending",
                "credits": new_credits
            }

            self.send_response(202) # 202 Accepted
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps(response_data).encode('utf-8'))

        except Exception as e:
            print(f"[Backend] Error during 2D generation initialization: {e}")
            self.send_error_response(500, str(e))

    def handle_space_status(self):
        try:
            from urllib.parse import urlparse, parse_qs
            parsed_path = urlparse(self.path)
            query_params = parse_qs(parsed_path.query)
            space_type = query_params.get('type', ['3d'])[0]
            token = query_params.get('token', [''])[0]
            
            load_dotenv()
            current_token = os.environ.get('HF_TOKEN', '').strip()
            hf_token_clean = token.strip() if token else ''
            if hf_token_clean in ('null', 'undefined'):
                hf_token_clean = ''
                
            # If running on HF Spaces, prioritize token sent by the client. If running locally, only use the .env token.
            is_hf_space = 'SPACE_ID' in os.environ
            if is_hf_space:
                token_to_use = hf_token_clean if hf_token_clean else current_token
            else:
                token_to_use = current_token
            if token_to_use == 'PON_TU_TOKEN_AQUI':
                token_to_use = ''
            token_to_use = token_to_use.strip()
            
            if space_type == 'rig':
                target_space = "LogicalTrue/Unirig"
            else:
                target_space = os.environ.get('HF_SPACE', 'LogicalTrue/TRELLIS.2')
                
            stage = get_space_status(target_space, token_to_use)
            
            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps({"stage": stage, "space": target_space}).encode('utf-8'))
        except Exception as e:
            self.send_error_response(500, str(e))

    def handle_job_status(self):
        try:
            from urllib.parse import urlparse, parse_qs
            parsed_path = urlparse(self.path)
            query_params = parse_qs(parsed_path.query)
            
            job_id_list = query_params.get('job_id')
            if not job_id_list:
                self.send_error_response(400, "Missing job_id parameter")
                return
                
            job_id = job_id_list[0]
            
            db_path = get_db_path()
            conn = sqlite3.connect(db_path)
            cursor = conn.cursor()
            cursor.execute("SELECT id, username, type, status, progress, message, result FROM jobs WHERE id = ?", (job_id,))
            row = cursor.fetchone()
            conn.close()
            
            if not row:
                self.send_error_response(404, f"Job {job_id} not found")
                return
                
            job_data = {
                "job_id": row[0],
                "username": row[1],
                "type": row[2],
                "status": row[3],
                "progress": row[4],
                "message": row[5],
                "result": json.loads(row[6]) if row[6] and (row[6].startswith('{') or row[6].startswith('[')) else row[6]
            }
            
            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps(job_data).encode('utf-8'))
            
        except Exception as e:
            print(f"[Backend Error in handle_job_status] {e}")
            self.send_error_response(500, str(e))

    def handle_user_active_job(self):
        try:
            username = self.get_logged_in_user()
            if not username:
                self.send_error_response(401, "No has iniciado sesión.")
                return

            conn = get_db_connection()
            cursor = conn.cursor()
            cursor.execute(
                "SELECT id, username, type, status, progress, message, result FROM jobs WHERE username = ? AND status IN ('pending', 'processing') ORDER BY created_at DESC LIMIT 1",
                (username,)
            )
            row = cursor.fetchone()
            conn.close()

            if not row:
                self.send_response(200)
                self.send_header('Content-Type', 'application/json')
                self.end_headers()
                self.wfile.write(json.dumps({"has_active": False}).encode('utf-8'))
                return

            job_data = {
                "has_active": True,
                "job_id": row[0],
                "username": row[1],
                "type": row[2],
                "status": row[3],
                "progress": row[4],
                "message": row[5],
                "result": json.loads(row[6]) if row[6] and (row[6].startswith('{') or row[6].startswith('[')) else row[6]
            }

            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps(job_data).encode('utf-8'))
        except Exception as e:
            self.send_error_response(500, str(e))

    def handle_get_gallery(self):
        try:
            username = self.get_logged_in_user() or "guest"
            items = []

            # 1. Query Supabase models table
            try:
                supabase_url, headers = get_supabase_headers()
                sb_res = requests.get(f"{supabase_url}/rest/v1/models?select=*&order=created_at.desc&limit=50", headers=headers, timeout=5)
                if sb_res.status_code == 200:
                    sb_models = sb_res.json()
                    for m in sb_models:
                        glb = m.get("output_glb_url") or m.get("glb_url") or ""
                        fbx = m.get("output_fbx_url") or m.get("fbx_url") or ""
                        img = m.get("input_image_url") or m.get("preview_url") or glb
                        m_name = m.get("name") or m.get("title") or "Creación"
                        m_type = "image" if (m_name.endswith(('.png', '.jpg', '.jpeg', '.webp')) or (img and not glb)) else "model"

                        if glb or fbx or img:
                            items.append({
                                "id": m.get("id") or m_name,
                                "name": m_name,
                                "type": m_type,
                                "prompt": m.get("prompt", ""),
                                "url": glb or img,
                                "glbUrl": glb,
                                "gltfUrl": glb,
                                "fbxUrl": fbx if fbx else None,
                                "previewUrl": img or glb,
                                "createdAt": m.get("created_at") or time.time(),
                                "mtime": m.get("created_at") or time.time()
                            })
            except Exception as sb_err:
                print(f"[Backend Gallery Supabase Notice] {sb_err}")

            # 2. Query Supabase Storage buckets directly (creations & models-3d)
            try:
                supabase_url, headers = get_supabase_headers()
                for bucket in ("creations", "models-3d"):
                    st_res = requests.post(f"{supabase_url}/storage/v1/object/list/{bucket}", json={"prefix": f"{username}/"}, headers=headers, timeout=5)
                    if st_res.status_code == 200:
                        objects = st_res.json()
                        for obj in objects:
                            obj_name = obj.get("name")
                            if obj_name and obj_name.endswith(('.glb', '.png', '.jpg', '.jpeg', '.fbx', '.webp')):
                                pub_url = f"{supabase_url}/storage/v1/object/public/{bucket}/{username}/{obj_name}"
                                m_type = "image" if obj_name.endswith(('.png', '.jpg', '.jpeg', '.webp')) else "model"
                                if not any(x.get("name") == obj_name or x.get("id") == obj_name for x in items):
                                    items.append({
                                        "id": obj_name,
                                        "name": obj_name,
                                        "type": m_type,
                                        "prompt": "",
                                        "url": pub_url,
                                        "glbUrl": pub_url,
                                        "gltfUrl": pub_url,
                                        "fbxUrl": pub_url if obj_name.endswith('.fbx') else None,
                                        "previewUrl": pub_url,
                                        "createdAt": time.time(),
                                        "mtime": time.time()
                                    })
            except Exception as st_err:
                print(f"[Backend Gallery Storage Notice] {st_err}")

            # 3. Fallback scan local generated_models & generated_images directories
            images_dir = get_generated_dir("images", username)
            models_dir = get_generated_dir("models", username)
            os.makedirs(images_dir, exist_ok=True)
            os.makedirs(models_dir, exist_ok=True)

            if os.path.exists(images_dir):
                for f in os.listdir(images_dir):
                    if f.endswith(('.png', '.jpg', '.jpeg', '.webp')):
                        if not any(x.get("name") == f for x in items):
                            path = os.path.join(images_dir, f)
                            items.append({
                                "id": f,
                                "name": f,
                                "type": "image",
                                "url": f"/generated_images/{username}/{f}",
                                "previewUrl": f"/generated_images/{username}/{f}",
                                "mtime": os.path.getmtime(path)
                            })

            if os.path.exists(models_dir):
                for f in os.listdir(models_dir):
                    if f.endswith('.glb') and not f.endswith('_dirty.glb'):
                        if not any(x.get("name") == f or f in str(x.get("glbUrl")) for x in items):
                            path = os.path.join(models_dir, f)
                            fbx_filename = f.replace('.glb', '.fbx')
                            has_fbx = os.path.exists(os.path.join(models_dir, fbx_filename))
                            items.append({
                                "id": f,
                                "name": f,
                                "type": "model",
                                "url": f"/generated_models/{username}/{f}",
                                "glbUrl": f"/generated_models/{username}/{f}",
                                "gltfUrl": f"/generated_models/{username}/{f}",
                                "fbxUrl": f"/generated_models/{username}/{fbx_filename}" if has_fbx else None,
                                "previewUrl": f"/generated_models/{username}/{f}",
                                "mtime": os.path.getmtime(path)
                            })

            items.sort(key=lambda x: x.get("mtime", 0), reverse=True)

            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps({
                "success": True,
                "models": items,
                "items": items
            }).encode('utf-8'))

        except Exception as e:
            print(f"[Backend] Error getting gallery: {e}")
            self.send_error_response(500, str(e))

    def handle_delete_gallery(self):
        try:
            username = self.get_logged_in_user()
            if not username:
                self.send_error_response(401, "No has iniciado sesión.")
                return

            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))
            
            filename = params.get('name', '')
            item_type = params.get('type', '')
            
            if not filename or not item_type:
                self.send_error_response(400, "Missing name or type")
                return
                
            if item_type == "image":
                target_dir = get_generated_dir("images", username)
            elif item_type == "model":
                target_dir = get_generated_dir("models", username)
            else:
                self.send_error_response(400, "Invalid type")
                return
                
            username = self.get_logged_in_user() or "guest"
            
            # Security check: avoid directory traversal
            clean_name = os.path.basename(filename)
            file_path = os.path.join(target_dir, clean_name)
            
            # 1. Delete from local disk if exists
            if os.path.exists(file_path):
                try:
                    os.remove(file_path)
                    print(f"[Backend Delete] Deleted local file: {file_path}")
                except Exception as file_err:
                    print(f"[Backend Delete Warning] Could not remove local file: {file_err}")
                
                # Cleanup associated extensions
                if item_type == "model" and clean_name.endswith(".glb"):
                    prefix = clean_name.replace(".glb", "")
                    for ext in [".obj", ".mtl", ".fbx", "_dirty.glb", "_clean.glb", "_clean.fbx", "_texture.png", "_rigged.fbx", "_rigged.glb", ".json"]:
                        assoc_file = os.path.join(target_dir, prefix + ext)
                        if os.path.exists(assoc_file):
                            try:
                                os.remove(assoc_file)
                            except Exception:
                                pass

            # 2. Delete from Supabase Storage buckets using standard prefixes API
            try:
                supabase_url, headers = get_supabase_headers()
                del_headers = dict(headers)
                del_headers["Content-Type"] = "application/json"

                base_stem = clean_name.split('.')[0]
                prefixes_list = [
                    f"{username}/{clean_name}",
                    f"{username}/{clean_name.replace('.glb', '.fbx')}",
                    f"{username}/{clean_name.replace('.glb', '_clean.glb')}",
                    f"{username}/{clean_name.replace('.glb', '_clean.fbx')}",
                    clean_name,
                    f"{base_stem}.glb",
                    f"{base_stem}.fbx"
                ]

                for bucket in ["creations", "models-3d", "images-2d"]:
                    st_del_url = f"{supabase_url}/storage/v1/object/{bucket}"
                    res_st = requests.delete(st_del_url, json={"prefixes": prefixes_list}, headers=del_headers, timeout=5)
                    print(f"[Backend Delete Storage] Bucket '{bucket}' deletion status: {res_st.status_code}")
            except Exception as st_err:
                print(f"[Backend Delete Storage Notice] {st_err}")

            # 3. Delete from Supabase Database `models` table
            try:
                supabase_url, headers = get_supabase_headers()
                base_stem = clean_name.split('.')[0]
                
                # Delete by name match
                requests.delete(f"{supabase_url}/rest/v1/models?name=eq.{clean_name}", headers=headers, timeout=5)
                requests.delete(f"{supabase_url}/rest/v1/models?name=ilike.*{base_stem}*", headers=headers, timeout=5)
                requests.delete(f"{supabase_url}/rest/v1/models?glb_url=like.*{base_stem}*", headers=headers, timeout=5)
                requests.delete(f"{supabase_url}/rest/v1/models?fbx_url=like.*{base_stem}*", headers=headers, timeout=5)
                print(f"[Backend Delete DB] Executed Supabase DB models deletion for {base_stem}")
            except Exception as db_err:
                print(f"[Backend Delete DB Notice] {db_err}")


            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps({"success": True}).encode('utf-8'))


        except Exception as e:
            print(f"[Backend] Error deleting gallery item: {e}")
            self.send_error_response(500, str(e))

    def handle_save_weights(self):
        try:
            username = self.get_logged_in_user()
            if not username:
                self.send_error_response(401, "No has iniciado sesión.")
                return

            content_length = int(self.headers['Content-Length'])
            post_data = self.rfile.read(content_length)
            params = json.loads(post_data.decode('utf-8'))

            model_url = params.get('modelUrl', '')
            glb_base64 = params.get('glbBase64', '')

            if not model_url or not glb_base64:
                self.send_error_response(400, "Missing modelUrl or glbBase64 data")
                return

            filename = os.path.basename(model_url)
            output_dir = get_generated_dir("models", username)
            dest_path = os.path.join(output_dir, filename)

            if not os.path.exists(dest_path):
                self.send_error_response(404, f"Model file {filename} not found")
                return

            # Extract base64 binary
            if ',' in glb_base64:
                glb_base64 = glb_base64.split(',')[1]
            glb_bytes = base64.b64decode(glb_base64)

            # Write updated GLB
            with open(dest_path, "wb") as f:
                f.write(glb_bytes)
            print(f"[Backend] Saved updated GLB weights for: {dest_path}")

            # Check if there is an associated FBX (regenerate it)
            fbx_filename = filename.replace(".glb", ".fbx")
            fbx_dest_path = os.path.join(output_dir, fbx_filename)

            blender_path = os.environ.get('BLENDER_PATH', '')
            if not blender_path or not os.path.exists(blender_path):
                blender_path = shutil.which("blender") or ""
            if blender_path and os.path.exists(blender_path):
                print(f"[Backend] Regenerating FBX from updated GLB weights...")
                import subprocess
                script_path = os.path.join(os.path.dirname(__file__), "scripts", "blender", "glb_to_fbx_weights.py")
                
                with open(script_path, "w", encoding="utf-8") as f_script:
                    f_script.write('''import bpy
import sys
import json

def strip_gltf_extensions(glb_path):
    try:
        with open(glb_path, "rb") as f:
            data = f.read()
        if len(data) < 20 or data[:4] != b'glTF':
            return
        json_len = int.from_bytes(data[12:16], byteorder='little')
        json_bytes = data[20:20+json_len]
        gltf_json = json.loads(json_bytes.decode('utf-8', errors='ignore'))
        
        modified = False
        for key in ['extensionsRequired', 'extensionsUsed']:
            if key in gltf_json and 'EXT_texture_webp' in gltf_json[key]:
                gltf_json[key].remove('EXT_texture_webp')
                modified = True
                
        if modified:
            new_bytes = json.dumps(gltf_json).encode('utf-8')
            if len(new_bytes) <= len(json_bytes):
                new_bytes = new_bytes.ljust(len(json_bytes), b' ')
                new_data = data[:20] + new_bytes + data[20+len(json_bytes):]
                with open(glb_path, "wb") as f:
                    f.write(new_data)
                print(f"[Blender] Stripped EXT_texture_webp extension requirement from GLB.")
    except Exception as e:
        print(f"[Blender] Extension strip note: {e}")

args = sys.argv[sys.argv.index("--") + 1:]
glb_in = args[0]
fbx_out = args[1]

strip_gltf_extensions(glb_in)
bpy.ops.wm.read_factory_settings(use_empty=True)
print(f"Importing GLB: {glb_in}")
bpy.ops.import_scene.gltf(filepath=glb_in)

print(f"Exporting FBX: {fbx_out}")
bpy.ops.export_scene.fbx(
    filepath=fbx_out,
    use_selection=False,
    object_types={'ARMATURE', 'MESH'},
    use_mesh_modifiers=True,
    add_leaf_bones=False,
    bake_anim=False
)
print("FBX conversion completed successfully.")
''')

                cmd = [blender_path, "--background", "--python", script_path, "--", dest_path, fbx_dest_path]
                print(f"[Backend] Executing: {' '.join(cmd)}")
                result = subprocess.run(cmd, capture_output=True, text=True)
                print(f"[Backend] Blender Output:\n{result.stdout}")
                if result.stderr:
                    print(f"[Backend] Blender Errors:\n{result.stderr}")
                
                try:
                    os.remove(script_path)
                except:
                    pass

            self.send_response(200)
            self.send_header('Content-Type', 'application/json')
            self.end_headers()
            self.wfile.write(json.dumps({"success": True, "fbxUrl": f"/generated_models/{username}/{fbx_filename}" if os.path.exists(fbx_dest_path) else None}).encode('utf-8'))

        except Exception as e:
            print(f"[Backend] Error saving weights: {e}")
            self.send_error_response(500, str(e))

    def send_error_response(self, code, message):
        self.send_response(code)
        self.send_header('Content-Type', 'application/json')
        self.end_headers()
        self.wfile.write(json.dumps({"error": message}).encode('utf-8'))

def run_server():
    init_db()
    server_address = ('', PORT)
    httpd = ThreadingHTTPServer(server_address, F23DHTTPRequestHandler)
    print(f"[Backend] 23DFactory server running at http://localhost:{PORT}")
    try:
        httpd.serve_forever()
    except KeyboardInterrupt:
        print("\n[Backend] Server shutting down.")
        httpd.server_close()

if __name__ == '__main__':
    run_server()