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src/florence/model.py

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+# Importing necessary libraries
+import sys
+import subprocess
+from typing import Optional
+from PIL import Image
+import gradio as gr
+import spaces
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+# Local imports
+from src.logger import logging
+from src.exception import CustomExceptionHandling
+
+
+# Install the required dependencies
+subprocess.run(
+    "pip install flash-attn --no-build-isolation",
+    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
+    shell=True,
+)
+
+# Load model and processor from Hugging Face
+model_id = "microsoft/Florence-2-large-ft"
+try:
+    model = (
+        AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+        .to("cuda")
+        .eval()
+    )
+    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+    logging.info("Model and processor loaded successfully.")
+
+# Handle exceptions that may occur during the process
+except Exception as e:
+    # Custom exception handling
+    raise CustomExceptionHandling(e, sys) from e
+
+
+@spaces.GPU
+def run_example(
+    task_prompt: str, image: Image.Image, text_input: Optional[str] = None
+) -> str:
+    """
+    Runs an example using the given task prompt and image.
+
+    Args:
+        - task_prompt (str): The task prompt for the example.
+        - image (PIL.Image.Image): The image to be processed.
+        - text_input (str, optional): Additional text input to be appended to the task prompt. Defaults to None.
+
+    Returns:
+        str: The parsed answer generated by the model.
+    """
+    try:
+        # Check if image is None
+        if image is None:
+            gr.Warning("Please provide an image.")
+
+        # If there is no text input, use the task prompt as the prompt
+        prompt = task_prompt if text_input is None else task_prompt + text_input
+
+        # Process the image and text input
+        inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
+
+        # Generate the answer using the model
+        generated_ids = model.generate(
+            input_ids=inputs["input_ids"],
+            pixel_values=inputs["pixel_values"],
+            max_new_tokens=1024,
+            early_stopping=False,
+            do_sample=False,
+            num_beams=3,
+        )
+        generated_text = processor.batch_decode(
+            generated_ids, skip_special_tokens=False
+        )[0]
+        parsed_answer = processor.post_process_generation(
+            generated_text, task=task_prompt, image_size=(image.width, image.height)
+        )
+
+        # Return the parsed answer
+        return parsed_answer
+
+    # Handle exceptions that may occur during the process
+    except Exception as e:
+        # Custom exception handling
+        raise CustomExceptionHandling(e, sys) from e

src/florence/task.py

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+# Import necessary libraries
+import sys
+import copy
+from typing import Tuple
+from PIL import Image
+import supervision as sv
+
+# Local imports
+from src.utils.processing import clean_text, draw_ocr_bboxes
+from src.florence.model import run_example
+from src.logger import logging
+from src.exception import CustomExceptionHandling
+
+
+def ocr_task(image: Image.Image) -> Tuple[Image.Image, str]:
+    """
+    Perform OCR (Optical Character Recognition) on the given image.
+
+    Args:
+        image (PIL.Image.Image): The input image to perform OCR on.
+
+    Returns:
+        tuple: A tuple containing the output image with OCR bounding boxes drawn and the cleaned OCR text.
+    """
+    try:
+        # Task prompts
+        ocr_prompt = "<OCR>"
+        ocr_with_region_prompt = "<OCR_WITH_REGION>"
+
+        # Get OCR text
+        ocr_results = run_example(ocr_prompt, image)
+        cleaned_text = clean_text(ocr_results["<OCR>"])
+
+        # Log the successful extraction and cleaning of OCR text
+        logging.info("OCR text extracted and cleaned successfully.")
+
+        # Get OCR with region
+        ocr_with_region_results = run_example(ocr_with_region_prompt, image)
+        output_image = copy.deepcopy(image)
+        detections = sv.Detections.from_lmm(
+            lmm=sv.LMM.FLORENCE_2,
+            result=ocr_with_region_results,
+            resolution_wh=image.size,
+        )
+        output_image = draw_ocr_bboxes(image, detections)
+
+        # Log the successful drawing of OCR bounding boxes
+        logging.info("OCR bounding boxes drawn successfully.")
+
+        # Return the output image and cleaned OCR text
+        return output_image, cleaned_text
+
+    # Handle exceptions that may occur during the process
+    except Exception as e:
+        # Custom exception handling
+        raise CustomExceptionHandling(e, sys) from e