# trellis_fastAPI_integration.py
# Version: 1.0.0

# a.1 Imports and Initial Setup
import os
import shutil
import threading
import uvicorn
import logging
import numpy as np
import torch

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from easydict import EasyDict as edict # Assuming EasyDict might be needed if state used

# Assuming these are available or installed correctly in the environment
from trellis.utils import postprocessing_utils
# We get the pipeline object passed in, so no direct import needed here

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# FastAPI app
api_app = FastAPI()

# --- Temporary Directory --- (Consistent with appTrellis.py)
TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
os.makedirs(TMP_DIR, exist_ok=True)

# b.1 Request/Response Models
class GenerateRequest(BaseModel):
    prompt: str
    seed: int = 0 # Default seed
    mesh_simplify: float = 0.95 # Default simplify factor
    texture_size: int = 1024 # Default texture size
    # Add other generation parameters if needed (e.g., guidance, steps)

# c.1 API Endpoint for Synchronous Generation
@api_app.post("/generate-sync")
async def generate_sync_api(request_data: GenerateRequest):
    """API endpoint to synchronously generate a model and return the GLB path."""
    # Access the pipeline object stored in app state
    pipeline = api_app.state.pipeline
    if pipeline is None:
        logger.error("API Error: Pipeline not initialized or passed correctly")
        raise HTTPException(status_code=503, detail="Pipeline not ready")

    prompt = request_data.prompt
    seed = request_data.seed
    mesh_simplify = request_data.mesh_simplify
    texture_size = request_data.texture_size
    # Extract other params if added to GenerateRequest
    ss_sampling_steps = 25 # Example default
    ss_guidance_strength = 7.5 # Example default
    slat_sampling_steps = 25 # Example default
    slat_guidance_strength = 7.5 # Example default

    logger.info(f"API /generate-sync received prompt: {prompt}")
    user_dir = None # Define user_dir outside try for cleanup

    try:
        # --- Determine a unique temporary directory for this API call ---
        # Using a simpler random hash name for the API call directory
        api_call_hash = f"api_sync_{np.random.randint(100000)}"
        user_dir = os.path.join(TMP_DIR, api_call_hash)
        os.makedirs(user_dir, exist_ok=True)
        logger.info(f"API using temp dir: {user_dir}")

        # --- Stage 1: Run the text-to-3D pipeline ---
        logger.info("API running pipeline...")
        # Ensure pipeline is run with appropriate parameters
        outputs = pipeline.run(
            prompt,
            seed=seed,
            formats=["gaussian", "mesh"],
            sparse_structure_sampler_params={
                "steps": ss_sampling_steps,
                "cfg_strength": ss_guidance_strength,
            },
            slat_sampler_params={
                "steps": slat_sampling_steps,
                "cfg_strength": slat_guidance_strength,
            },
        )
        gs = outputs['gaussian'][0] # Get the Gaussian representation
        mesh = outputs['mesh'][0] # Get the Mesh representation
        logger.info("API pipeline finished.")
        torch.cuda.empty_cache()

        # --- Stage 2: Extract GLB ---
        logger.info("API extracting GLB...")
        # Use the postprocessing utility
        glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
        glb_path = os.path.join(user_dir, 'generated_sync.glb')
        glb.export(glb_path)
        logger.info(f"API GLB exported to: {glb_path}")
        torch.cuda.empty_cache()

        # Return the absolute path within the container
        # This path needs to be accessible via the /file= route from outside
        absolute_glb_path = os.path.abspath(glb_path)
        logger.info(f"API returning absolute path: {absolute_glb_path}")
        return {"status": "success", "glb_path": absolute_glb_path}

    except Exception as e:
        logger.error(f"API /generate-sync error: {str(e)}", exc_info=True)
        # Clean up temp dir on error if it exists and was created
        if user_dir and os.path.exists(user_dir):
            try:
                shutil.rmtree(user_dir)
                logger.info(f"API cleaned up failed directory: {user_dir}")
            except Exception as cleanup_e:
                logger.error(f"API Error cleaning up dir {user_dir}: {cleanup_e}")
        raise HTTPException(status_code=500, detail=f"Generation failed: {str(e)}")
    # Note: We don't automatically clean up the user_dir on success,
    # as the file needs to be accessible for download by the calling server.
    # A separate cleanup mechanism might be needed eventually.

# d.1 API Server Setup Functions
def run_api():
    """Run the FastAPI server."""
    # Ensure pipeline is available in app state before starting
    if not hasattr(api_app.state, 'pipeline') or api_app.state.pipeline is None:
         logger.error("Cannot start API server: Pipeline object not found in app state.")
         return
    # Run on port 8000 - ensure this doesn't conflict if Gradio also tries this port
    uvicorn.run(api_app, host="0.0.0.0", port=8000)

def start_api_thread(pipeline_object):
    """Start the API server in a background thread

    Args:
        pipeline_object: The initialized TrellisTextTo3DPipeline object
    """
    # Store the passed pipeline object in the app's state
    api_app.state.pipeline = pipeline_object

    api_thread = threading.Thread(target=run_api, daemon=True)
    api_thread.start()
    logger.info("Started Trellis FastAPI integration server thread on port 8000")
    return api_thread