server.py

#!/usr/bin/env python3
"""
Image Tagging Server using ONNX and FastAPI.

This script sets up a web server that provides endpoints for tagging images
using a pre-trained ONNX model. It supports single image processing, batch
processing, and can download model artifacts from a Hugging Face repository.
"""

import argparse
import logging
import math
import os
import pathlib
import time
import types
import typing
from contextlib import asynccontextmanager
from io import BytesIO
from pathlib import Path
from typing import Any, Dict, List

import huggingface_hub
import numpy as np
import pandas as pd
import timm
import torch
import uvicorn
from fastapi import FastAPI, File, HTTPException, UploadFile
from fastapi.responses import RedirectResponse
from PIL import Image
from pydantic import BaseModel, Field
from pydantic_settings import BaseSettings
from timm.data import create_transform, resolve_data_config
from torch import nn
from torch.nn import functional as F


# --- Configuration Management ---
class Settings(BaseSettings):
    """Manages application configuration using Pydantic."""

    host: str = Field(default="0.0.0.0", description="Server host.")
    port: int = Field(default=8080, description="Server port.")
    instances: int = Field(default=1, description="Number of uvicorn workers.")
    triton: int = Field(default=0, description="Enable triton / torch.compile()")
    log_level: str = Field(default="INFO", description="Logging level.")

    model_repo: str = Field(
        default=None, description="HuggingFace repository for model files."
    )
    model_file: str = Field(
        default="model.safetensors", description="ONNX model filename."
    )
    tags_file: str = Field(
        default="selected_tags.csv", description="CSV file with tag names."
    )
    thresholds_file: str = Field(
        default="thresholds.csv", description="CSV file with category thresholds."
    )
    backend: str = Field(
        default="cpu",
        description="Inference backend ('cpu', 'cuda', 'tensorrt').",
        pattern="^(cpu|cuda|tensorrt)$",
    )
    token: str | None = Field(default=None, description="HuggingFace Token.")

    class Config:
        env_prefix = "TAGGER_"


# --- Logging Setup ---
class CustomFormatter(logging.Formatter):
    """A custom log formatter with colors for different log levels."""

    LEVEL_COLORS = {
        logging.DEBUG: "\x1b[38;20m",  # Grey
        logging.INFO: "\x1b[32m",  # Green
        logging.WARNING: "\x1b[33;20m",  # Yellow
        logging.ERROR: "\x1b[31;20m",  # Red
        logging.CRITICAL: "\x1b[31;1m",  # Bold Red
    }
    RESET = "\x1b[0m"

    def format(self, record: logging.LogRecord) -> str:
        color = self.LEVEL_COLORS.get(record.levelno, "")
        record.levelprefix = f"{color}{record.levelname:<8}{self.RESET}"
        return super().format(record)


def setup_logging(log_level: str):
    """Configures the root logger."""
    logger = logging.getLogger()
    logger.setLevel(log_level)
    handler = logging.StreamHandler()
    handler.setFormatter(CustomFormatter("%(levelprefix)s | %(message)s"))
    logger.handlers = [handler]
    # Suppress verbose logs from other libraries
    logging.getLogger("uvicorn").handlers = []
    logging.getLogger("uvicorn.access").handlers = []
    return logger


def pil_ensure_rgb(image: Image.Image) -> Image.Image:
    if image.mode not in ["RGB", "RGBA"]:
        image = (
            image.convert("RGBA")
            if "transparency" in image.info
            else image.convert("RGB")
        )
    if image.mode == "RGBA":
        canvas = Image.new("RGBA", image.size, (255, 255, 255))
        canvas.alpha_composite(image)
        image = canvas.convert("RGB")
    return image


def pil_pad_square(image: Image.Image) -> Image.Image:
    w, h = image.size
    px = max(w, h)
    canvas = Image.new("RGB", (px, px), (255, 255, 255))
    canvas.paste(image, ((px - w) // 2, (px - h) // 2))
    return canvas


logger = setup_logging("DEBUG")


# --- API Models (Pydantic) ---
class Timing(BaseModel):
    total_seconds: float
    processing_seconds: float


TAG_RESPONSE = dict[str, list[dict[str, Any]]]


class TaggingResponse(BaseModel):
    tags: TAG_RESPONSE
    timing: Timing


class BatchTaggingResponse(BaseModel):
    batch_size: int
    results: list[TAG_RESPONSE]
    timing: Timing


class StatusResponse(BaseModel):
    status: str
    model_name: str | None


class TaggerArgs(BaseModel):
    tags_threshold: bool = False


# --- Core Logic: Tags & Tagger Classes ---
class Tags:
    """Handles loading and processing of tag data and thresholds."""

    DEFAULT_CATEGORIES = {
        0: {"name": "general", "threshold": 0.35},
        4: {"name": "character", "threshold": 0.85},
        9: {"name": "rating", "threshold": 0.0},
    }

    def __init__(self, labels_path: Path, threshold_path: Path | None = None):
        logger.info(f"Loading labels from '{labels_path}'...")
        start_time = time.time()

        tags_df = pd.read_csv(labels_path)
        self.tag_names = tags_df["name"].tolist()
        self.tag_names_ndarray = np.array(self.tag_names)
        self.categories: Dict[int, Dict[str, Any]] = {}

        if "best_threshold" in tags_df:
            self.tag_thresholds = np.array(tags_df["best_threshold"].tolist())
        else:
            self.tag_thresholds = None

        if (
            threshold_path
            and threshold_path.is_file()
            and threshold_path.stat().st_size > 0
        ):
            logger.info(f"Loading thresholds from '{threshold_path}'.")
            for item in pd.read_csv(threshold_path).to_dict("records"):
                if item["category"] not in self.categories:
                    self.categories[item["category"]] = {
                        "name": item["name"],
                        "threshold": item["threshold"],
                    }
        else:
            logger.info("No valid threshold file found. Using default categories.")
            self.categories = self.DEFAULT_CATEGORIES

        for cat_id, cat_info in self.categories.items():
            cat_info["indices"] = list(np.where(tags_df["category"] == cat_id)[0])

        logger.info(
            f"Loaded {len(self.tag_names)} tags and {len(self.categories)} categories in {time.time() - start_time:.2f}s."
        )

    def process_predictions(
        self,
        preds: np.ndarray,
        tag_indices: List[int],
        threshold: float,
        tags_threshold: bool = False,
    ) -> List[List[dict[str, Any]]]:
        """Filters and sorts predictions based on a threshold."""

        tag_names = self.tag_names_ndarray
        # preds = np.asarray(preds)
        tag_scores = preds[:, tag_indices]
        tag_names_sel = tag_names[tag_indices]

        if tags_threshold and self.tag_thresholds is not None:
            mask = tag_scores > self.tag_thresholds[tag_indices]
            tag_scores = np.where(mask, tag_scores, -np.inf)
        else:
            if threshold is not None:
                mask = tag_scores > threshold
                tag_scores = np.where(mask, tag_scores, -np.inf)

        sorted_idx = np.argsort(-tag_scores, axis=1)
        sorted_names = tag_names_sel[sorted_idx]
        sorted_scores = np.take_along_axis(tag_scores, sorted_idx, axis=1)

        return [
            [
                {"name": name, "confidence": float(score)}
                for name, score in zip(names, scores)
                if not math.isinf(float(score))
            ]
            for names, scores in zip(sorted_names, sorted_scores)
        ]

    def resolve_batch_probs(
        self, probs: np.ndarray, tags_threshold: bool = False
    ) -> list[dict[str, list[dict[str, Any]]]]:
        """Resolves raw probabilities into categorized tag predictions."""
        logger.info(f"Shapery: {probs.shape[0]}")
        results_batched: dict[str, Any] = {
            cat_info["name"]: [] for cat_info in self.categories.values()
        }
        for cat_info in self.categories.values():
            for _, result in enumerate(
                self.process_predictions(
                    probs,
                    cat_info["indices"],
                    cat_info["threshold"],
                    tags_threshold=tags_threshold,
                )
            ):
                # {k: [dic[k] for dic in LD] for k in LD[0]}
                results_batched[cat_info["name"]].append(result)
        results_list = [
            dict(zip(results_batched, t)) for t in zip(*results_batched.values())
        ]
        return results_list


class Tagger:
    """Manages the ONNX model, image preprocessing, and inference."""

    def __init__(
        self,
        model_repo: str,
        tags: Tags,
        backend: str = "cpu",
        instances: int = 1,
        triton: bool = False,
    ):
        self.tags_data = tags
        self.model_repo = model_repo
        self.device = torch.device(
            "cuda" if backend == "cuda" and torch.cuda.is_available() else "cpu"
        )

        logger.info(f"Loading model from HuggingFace repo: {model_repo}...")
        self.model: nn.Module = timm.create_model(
            "hf-hub:" + model_repo, pretrained=False
        )
        self.swap_colorspace = False
        if model_repo.startswith("animetimm/"):
            logger.warning("Detected animetimm model. Enabling color swap.")
            self.swap_colorspace = True

        state_dict = timm.models.load_state_dict_from_hf(model_repo)
        self.model.load_state_dict(state_dict)
        self.model = self.model.eval().to(self.device)
        if triton:
            self.model.compile(
                fullgraph=True,
            )
        self.transform = create_transform(
            **resolve_data_config(self.model.pretrained_cfg, model=self.model)
        )
        self.model = nn.DataParallel(self.model, device_ids=list(range(instances)))

        logger.info("Model loaded and ready.")

    def _create_model(
        self, model_repo: str, backend: str, index: int
    ) -> torch.nn.Module:
        """Creates and validates the ONNX Runtime inference session."""
        model: torch.nn.Module = timm.create_model(
            "hf-hub:" + model_repo, pretrained=False
        )
        state_dict = timm.models.load_state_dict_from_hf(model_repo)
        model.load_state_dict(state_dict)
        model = model.eval()
        if backend == "cuda":
            model = model.to(torch.device(backend, index), dtype=torch.float32)
        # model.compile(
        #     fullgraph=True,
        # )
        return model

    def preprocess_batch(self, image_batch: np.ndarray) -> torch.Tensor:
        """Converts NHWC float32 [0-1] NumPy images to a PyTorch tensor in NCHW RGB format."""
        pil_images = [
            Image.fromarray((img * 255).astype(np.uint8)) for img in image_batch
        ]
        images = [pil_pad_square(pil_ensure_rgb(im)) for im in pil_images]
        tensors = [self.transform(im) for im in images]
        batch = torch.stack(tensors, dim=0)

        if self.swap_colorspace:
            print(batch.shape)
            batch = batch[:, [2, 1, 0], :, :]
        return batch.to(self.device)

    def predict_batch(
        self, image_batch: np.ndarray, tags_threshold=False
    ) -> List[dict[str, list[dict[str, Any]]]]:
        batch_tensor = self.preprocess_batch(image_batch)

        with (
            torch.inference_mode(),
            torch.autocast(device_type="cuda", dtype=torch.bfloat16),
        ):
            logits = self.model(batch_tensor)
            probs = F.sigmoid(logits).cpu().to(torch.float32).numpy()

        resolved = self.tags_data.resolve_batch_probs(
            probs, tags_threshold=tags_threshold
        )
        return resolved


# --- FastAPI Application Setup ---
class AppState:
    """Container for application state, like the tagger instance."""

    def __init__(self, settings: Settings):
        self.settings = settings
        self.tagger: Tagger | None = None


def download_file(repo: str, filename: str, output_path: Path):
    """Downloads a file from Hugging Face Hub if it doesn't exist."""
    if not output_path.exists():
        logger.info(f"Downloading '{filename}' from repo '{repo}'...")
        try:
            path = huggingface_hub.hf_hub_download(
                repo,
                filename,
                local_dir=output_path.parent,
                local_dir_use_symlinks=False,
            )
            # Ensure the downloaded file is at the expected path
            if Path(path) != output_path:
                os.rename(path, output_path)
        except Exception as e:
            raise FileNotFoundError(
                f"Failed to download '{filename}' from '{repo}': {e}"
            ) from e


@asynccontextmanager
async def lifespan(app: FastAPI):
    """Initializes the Tagger on startup and handles cleanup."""
    settings: Settings = app.state.settings

    model_dir = Path("models")
    model_dir.mkdir(exist_ok=True)

    if settings.model_repo and pathlib.Path(settings.model_repo).is_dir():
        model_dir = pathlib.Path(settings.model_repo)
    elif settings.model_repo:
        model_dir = model_dir / pathlib.Path(settings.model_repo)
    logger.info(f"Using directory: {model_dir} for storage...")
    tags_path = model_dir / settings.tags_file
    thresholds_path = model_dir / settings.thresholds_file

    if settings.model_repo and not pathlib.Path(settings.model_repo).is_dir():
        try:
            download_file(settings.model_repo, settings.tags_file, tags_path)
            # Thresholds file is optional, so don't fail if it's not there
            try:
                download_file(
                    settings.model_repo, settings.thresholds_file, thresholds_path
                )
            except FileNotFoundError:
                logger.warning(
                    f"Optional thresholds file '{settings.thresholds_file}' not found in repo."
                )
        except FileNotFoundError as e:
            logger.critical(f"Could not start server: {e}")
            # Exit if critical files are missing
            return

    if not tags_path.is_file():
        logger.critical(
            "Model or tags file not found, and no model repository was specified. Exiting."
        )
        return

    try:
        logger.info("Initializing tagger...")
        tags = Tags(labels_path=tags_path, threshold_path=thresholds_path)
        app.state.tagger = Tagger(
            settings.model_repo,
            tags,
            settings.backend,
            instances=settings.instances,
            triton=True if settings.triton else False,
        )
        logger.info("Tagger initialized successfully. Server is ready.")
    except (ValueError, RuntimeError) as e:
        logger.critical(f"Failed to initialize tagger: {e}")
        return

    yield

    # --- Cleanup ---
    app.state.tagger = None
    logger.info("Server shutting down.")


def create_app(settings: Settings) -> FastAPI:
    """Creates and configures the FastAPI application instance."""
    app = FastAPI(
        title="Image Tagger API",
        description="An API for tagging images using an ONNX model.",
        version="1.0.1",  # Incremented version
        lifespan=lifespan,
         docs_url="/docs",
        
    )
    app.state = AppState(settings)
    return app


# --- Dependency for Endpoints ---
def get_tagger(app: FastAPI) -> Tagger:
    """A dependency that provides the initialized tagger instance."""
    if not app.state.tagger:
        raise HTTPException(
            status_code=503,
            detail="Tagger is not initialized. The server may be starting up or has encountered an error.",
        )
    return app.state.tagger


# --- API Endpoints ---
def add_endpoints(app: FastAPI):
    tagger_dependency = lambda: get_tagger(app)

    # Root welcome/docs page
    @app.get("/", include_in_schema=False)
    async def root():
        if app.docs_url:
            return RedirectResponse(url=app.docs_url)
        elif app.redoc_url:
            return RedirectResponse(url=app.redoc_url)
        return HTMLResponse(
            content="<h1>Welcome to the Tagger API</h1><p>Use /batch to tag images.</p>",
            status_code=200
        )

    # Tagging endpoint at /batch
    @app.post("/batch", response_model=BatchTaggingResponse, summary="Tag a batch of images")
    async def tag_batch(
        tags_threshold: TaggerArgs = TaggerArgs(),
        file: UploadFile = File(
            ..., description="A .npz file containing a batch of images in NHWC format."
        ),
    ):
        if not file.filename or not file.filename.endswith(".npz"):
            raise HTTPException(
                status_code=400,
                detail="Only .npz files are supported for batch processing.",
            )

        start_time = time.time()
        tagger = tagger_dependency()

        logger.info(f"Processing batch file: {file.filename}")
        contents = await file.read()
        with np.load(BytesIO(contents)) as npz:
            batch = npz[npz.files[0]]

        logger.info(f"Loaded batch of shape: {batch.shape}")
        process_start = time.time()
        try:
            results = tagger.predict_batch(batch, tags_threshold=tags_threshold)
        except ValueError as e:
            raise HTTPException(status_code=400, detail=str(e))
        processing_time = time.time() - process_start
        logger.info(f"Processed batch in {processing_time:.2f}s")

        return BatchTaggingResponse(
            batch_size=len(results),
            results=results,
            timing=Timing(
                total_seconds=time.time() - start_time,
                processing_seconds=processing_time,
            ),
        )

    # Status endpoint
    @app.get("/status", response_model=StatusResponse, summary="Get server status")
    async def status():
        tagger = tagger_dependency()
        return StatusResponse(
            status="ok",
            model_name=tagger.model_repo,
        )


def determine_type(field_type: type):
    if type(field_type) is types.UnionType:
        return typing.get_args(field_type)[0]
    return field_type


# --- Main Execution ---
def main():
    """Parses arguments, sets up the app, and runs the server."""
    parser = argparse.ArgumentParser(description="Image Tagging Server")
    # Add arguments that correspond to the Settings fields
    for field_name, field in Settings.model_fields.items():
        parser.add_argument(
            f"--{field_name.replace('_', '-')}",
            type=determine_type(field.annotation),  # Basic type handling for argparse
            default=field.default,
            help=field.description,
        )
    args = parser.parse_args()

    # Create settings from a combination of args, env vars, and defaults
    settings = Settings(**vars(args))

    global logger
    logger = setup_logging(settings.log_level.upper())

    if settings.token:
        import os

        logger.info("Using custom token...")
        os.environ["HF_TOKEN"] = settings.token

    app = create_app(settings)
    add_endpoints(app)

    uvicorn.run(
        app,
        host=settings.host,
        port=settings.port,
        log_config=None,  # Use our custom logger
    )


if __name__ == "__main__":
    main()