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Update main.py
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main.py
CHANGED
@@ -4,6 +4,7 @@ import base64
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import io
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import os
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import logging
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from PIL import Image, UnidentifiedImageError
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import torch
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import asyncio
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@@ -35,7 +36,7 @@ try:
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"weights/icon_caption_florence",
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torch_dtype=torch.float16,
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trust_remote_code=True,
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).to(
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except Exception as e:
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logger.warning(f"Failed to load caption model on GPU: {e}. Falling back to CPU.")
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model = AutoModelForCausalLM.from_pretrained(
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@@ -50,7 +51,7 @@ logger.info("Finished loading models!")
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# Initialize FastAPI app
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app = FastAPI()
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MAX_QUEUE_SIZE =
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request_queue = asyncio.Queue(maxsize=MAX_QUEUE_SIZE)
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# Define response model
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@@ -79,7 +80,7 @@ async def startup_event():
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asyncio.create_task(worker())
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# Image processing function
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async def process(image_input: Image.Image, box_threshold: float, iou_threshold: float) -> ProcessResponse:
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try:
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# Define save path
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@@ -132,11 +133,18 @@ async def process(image_input: Image.Image, box_threshold: float, iou_threshold:
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# Join parsed content list
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parsed_content_list_str = "\n".join([str(item) for item in parsed_content_list])
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image=img_str,
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parsed_content_list=parsed_content_list_str,
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label_coordinates=str(label_coordinates),
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)
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except Exception as e:
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logger.error(f"Error in process function: {e}")
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raise HTTPException(status_code=500, detail=f"Failed to process the image: {e}")
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import io
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import os
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import logging
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import gc # Import garbage collector
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from PIL import Image, UnidentifiedImageError
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import torch
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import asyncio
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"weights/icon_caption_florence",
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torch_dtype=torch.float16,
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trust_remote_code=True,
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).to(device)
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except Exception as e:
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logger.warning(f"Failed to load caption model on GPU: {e}. Falling back to CPU.")
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model = AutoModelForCausalLM.from_pretrained(
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# Initialize FastAPI app
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app = FastAPI()
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MAX_QUEUE_SIZE = 10 # Set a reasonable limit based on your system capacity
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request_queue = asyncio.Queue(maxsize=MAX_QUEUE_SIZE)
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# Define response model
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asyncio.create_task(worker())
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# Image processing function with memory cleanup
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async def process(image_input: Image.Image, box_threshold: float, iou_threshold: float) -> ProcessResponse:
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try:
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# Define save path
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# Join parsed content list
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parsed_content_list_str = "\n".join([str(item) for item in parsed_content_list])
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response = ProcessResponse(
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image=img_str,
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parsed_content_list=parsed_content_list_str,
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label_coordinates=str(label_coordinates),
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)
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# **Memory Cleanup**
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del image_input, text, ocr_bbox, dino_labled_img, label_coordinates, parsed_content_list
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torch.cuda.empty_cache() # Free GPU memory
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gc.collect() # Free CPU memory
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return response
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except Exception as e:
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logger.error(f"Error in process function: {e}")
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raise HTTPException(status_code=500, detail=f"Failed to process the image: {e}")
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