Create main.py

main.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+from typing import Optional
+import base64
+import io
+from PIL import Image
+import torch
+import numpy as np
+import os
+
+# Existing imports
+import numpy as np
+import torch
+from PIL import Image
+import io
+
+from utils import (
+    check_ocr_box,
+    get_yolo_model,
+    get_caption_model_processor,
+    get_som_labeled_img,
+)
+import torch
+
+# yolo_model = get_yolo_model(model_path='/data/icon_detect/best.pt')
+# caption_model_processor = get_caption_model_processor(model_name="florence2", model_name_or_path="/data/icon_caption_florence")
+
+from ultralytics import YOLO
+
+# if not os.path.exists("/data/icon_detect"):
+#     os.makedirs("/data/icon_detect")
+
+try:
+    yolo_model = YOLO("weights/icon_detect/best.pt").to("cuda")
+except:
+    yolo_model = YOLO("weights/icon_detect/best.pt")
+
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+processor = AutoProcessor.from_pretrained(
+    "microsoft/Florence-2-base", trust_remote_code=True
+)
+
+try:
+    model = AutoModelForCausalLM.from_pretrained(
+        "weights/icon_caption_florence",
+        torch_dtype=torch.float16,
+        trust_remote_code=True,
+    ).to("cuda")
+except:
+    model = AutoModelForCausalLM.from_pretrained(
+        "weights/icon_caption_florence",
+        torch_dtype=torch.float16,
+        trust_remote_code=True,
+    )
+caption_model_processor = {"processor": processor, "model": model}
+print("finish loading model!!!")
+
+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"
+    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,
+    )
+    image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
+    print("finish processing")
+    parsed_content_list_str = "\n".join(parsed_content_list)
+
+    # Encode 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=str(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")
+    except Exception as e:
+        raise HTTPException(status_code=400, detail="Invalid image file")
+
+    response = process(image_input, box_threshold, iou_threshold)
+    return response