main.py

from fastapi import FastAPI, File, UploadFile, HTTPException
from pydantic import BaseModel
import base64
import io
import os
import logging
from PIL import Image
import torch

# Existing imports
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)  # Changed to DEBUG for more verbosity
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")
if str(device) == "cuda":
    yolo_model = yolo_model.cuda()
else:
    yolo_model = yolo_model.cpu()

# 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("cuda")
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!!!")

app = FastAPI()

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

def process(image_input: Image.Image, box_threshold: float, iou_threshold: float) -> ProcessResponse:
    image_save_path = "imgs/saved_image_demo.png"
    os.makedirs(os.path.dirname(image_save_path), exist_ok=True)
    image_input.save(image_save_path)
    
    image = Image.open(image_save_path)
    box_overlay_ratio = image.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 = 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 = 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,
    )

    # Log parsed_content_list to inspect its structure before joining
    logger.info(f"Parsed content list before join: {parsed_content_list}")
    
    # Ensure parsed_content_list is a list of strings, not dictionaries
    parsed_content_list_str = "\n".join([str(item) for item in parsed_content_list])

    image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
    print("Finish processing")

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

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


@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:
        contents = await image_file.read()
        image_input = Image.open(io.BytesIO(contents)).convert("RGB")
        
        logger.info(f"Processing image: {image_file.filename}")
        logger.info(f"Image size: {image_input.size}")
        
        # Debugging the input image
        if not image_input:
            raise ValueError("Image input is empty or invalid.")
        
        response = process(image_input, box_threshold, iou_threshold)
        
        # Ensure the response contains an image
        if not response.image:
            raise ValueError("Empty image in response")
        
        logger.info("Processing complete, returning response.")
        return response
        
    except Exception as e:
        logger.error(f"Error processing image: {e}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=str(e))