main.py

from fastapi import FastAPI, File, UploadFile, HTTPException
from pydantic import BaseModel
import base64
import io
import os
import logging
import gc  # Import garbage collector##
from PIL import Image, UnidentifiedImageError
import torch
import asyncio
from utils import (
    check_ocr_box,
    get_yolo_model,
    get_caption_model_processor,
    get_som_labeled_img,
)
from transformers import AutoProcessor, AutoModelForCausalLM

# Configure logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

# Load YOLO model
yolo_model = get_yolo_model(model_path="weights/best.pt")

# Handle device placement
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
yolo_model = yolo_model.to(device)

# Load caption model and processor
try:
    processor = AutoProcessor.from_pretrained(
        "microsoft/Florence-2-base", trust_remote_code=True
    )
    model = AutoModelForCausalLM.from_pretrained(
        "weights/icon_caption_florence",
        torch_dtype=torch.float16,
        trust_remote_code=True,
    ).to(device)
except Exception as e:
    logger.warning(f"Failed to load caption model on GPU: {e}. Falling back to CPU.")
    model = AutoModelForCausalLM.from_pretrained(
        "weights/icon_caption_florence",
        torch_dtype=torch.float16,
        trust_remote_code=True,
    )

caption_model_processor = {"processor": processor, "model": model}
logger.info("Finished loading models!")

# Initialize FastAPI app
app = FastAPI()

MAX_QUEUE_SIZE = 10  # Set a reasonable limit based on your system capacity
request_queue = asyncio.Queue(maxsize=MAX_QUEUE_SIZE)

# Define response model
class ProcessResponse(BaseModel):
    image: str  # Base64 encoded image
    parsed_content_list: str
    label_coordinates: str


# Background worker to process queue tasks
async def worker():
    while True:
        task = await request_queue.get()
        try:
            await task
        except Exception as e:
            logger.error(f"Error while processing task: {e}")
        finally:
            request_queue.task_done()


# Start worker on startup
@app.on_event("startup")
async def startup_event():
    logger.info("Starting background worker...")
    asyncio.create_task(worker())


# Image processing function with memory cleanup
async def process(image_input: Image.Image, box_threshold: float, iou_threshold: float) -> ProcessResponse:
    try:
        # Define save path
        image_save_path = "imgs/saved_image_demo.png"
        os.makedirs(os.path.dirname(image_save_path), exist_ok=True)

        # Save image
        image_input.save(image_save_path)
        logger.debug(f"Image saved to: {image_save_path}")

        # YOLO and caption model inference
        box_overlay_ratio = image_input.size[0] / 3200
        draw_bbox_config = {
            "text_scale": 0.8 * box_overlay_ratio,
            "text_thickness": max(int(2 * box_overlay_ratio), 1),
            "text_padding": max(int(3 * box_overlay_ratio), 1),
            "thickness": max(int(3 * box_overlay_ratio), 1),
        }

        ocr_bbox_rslt, is_goal_filtered = await asyncio.to_thread(
            check_ocr_box,
            image_save_path,
            display_img=False,
            output_bb_format="xyxy",
            goal_filtering=None,
            easyocr_args={"paragraph": False, "text_threshold": 0.9},
            use_paddleocr=True,
        )
        text, ocr_bbox = ocr_bbox_rslt

        dino_labled_img, label_coordinates, parsed_content_list = await asyncio.to_thread(
            get_som_labeled_img,
            image_save_path,
            yolo_model,
            BOX_TRESHOLD=box_threshold,
            output_coord_in_ratio=True,
            ocr_bbox=ocr_bbox,
            draw_bbox_config=draw_bbox_config,
            caption_model_processor=caption_model_processor,
            ocr_text=text,
            iou_threshold=iou_threshold,
        )

        # Convert labeled image to base64
        image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
        buffered = io.BytesIO()
        image.save(buffered, format="PNG")
        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")

        # Join parsed content list
        parsed_content_list_str = "\n".join([str(item) for item in parsed_content_list])

        response = ProcessResponse(
            image=img_str,
            parsed_content_list=parsed_content_list_str,
            label_coordinates=str(label_coordinates),
        )

        # **Memory Cleanup**
        del image_input, text, ocr_bbox, dino_labled_img, label_coordinates, parsed_content_list
        torch.cuda.empty_cache()  # Free GPU memory
        gc.collect()  # Free CPU memory

        return response
    except Exception as e:
        logger.error(f"Error in process function: {e}")
        raise HTTPException(status_code=500, detail=f"Failed to process the image: {e}")


# API endpoint for processing images
@app.post("/process_image", response_model=ProcessResponse)
async def process_image(
    image_file: UploadFile = File(...),
    box_threshold: float = 0.05,
    iou_threshold: float = 0.1,
):
    try:
        # Read image file
        contents = await image_file.read()
        try:
            image_input = Image.open(io.BytesIO(contents)).convert("RGB")
        except UnidentifiedImageError:
            logger.error("Unsupported image format.")
            raise HTTPException(status_code=400, detail="Unsupported image format.")

        # Create processing task
        task = asyncio.create_task(process(image_input, box_threshold, iou_threshold))

        # Add task to queue
        await request_queue.put(task)
        logger.info(f"Task added to queue. Current queue size: {request_queue.qsize()}")

        # Wait for processing to complete
        response = await task

        return response
    except HTTPException as he:
        raise he
    except Exception as e:
        logger.error(f"Error processing image: {e}")
        raise HTTPException(status_code=500, detail=f"Internal server error: {e}")#