app.py

import os
os.system('git clone https://github.com/facebookresearch/detectron2.git')
os.system('pip install -e detectron2')
os.system("git clone https://github.com/microsoft/unilm.git")
os.system("sed -i 's/from collections import Iterable/from collections.abc import Iterable/' unilm/dit/object_detection/ditod/table_evaluation/data_structure.py")
os.system("curl -LJ -o publaynet_dit-b_cascade.pth 'https://layoutlm.blob.core.windows.net/dit/dit-fts/publaynet_dit-b_cascade.pth?sv=2022-11-02&ss=b&srt=o&sp=r&se=2033-06-08T16:48:15Z&st=2023-06-08T08:48:15Z&spr=https&sig=a9VXrihTzbWyVfaIDlIT1Z0FoR1073VB0RLQUMuudD4%3D'")

import sys
sys.path.append("unilm")
sys.path.append("detectron2")

import cv2
import filetype
from PIL import Image
import numpy as np
from io import BytesIO
from pdf2image import convert_from_bytes, convert_from_path

import re
import requests
from urllib.parse import urlparse, parse_qs

from unilm.dit.object_detection.ditod import add_vit_config

import torch

from detectron2.config import CfgNode as CN
from detectron2.config import get_cfg
from detectron2.utils.visualizer import ColorMode, Visualizer
from detectron2.data import MetadataCatalog
from detectron2.engine import DefaultPredictor

from huggingface_hub import hf_hub_download

import gradio as gr


# Step 1: instantiate config
cfg = get_cfg()
add_vit_config(cfg)
#cfg.merge_from_file("cascade_dit_base.yml")
cfg.merge_from_file("unilm/dit/object_detection/publaynet_configs/cascade/cascade_dit_base.yaml")

# Step 2: add model weights URL to config
filepath = hf_hub_download(repo_id="Sebas6k/DiT_weights", filename="publaynet_dit-b_cascade.pth", repo_type="model")
cfg.MODEL.WEIGHTS = filepath

# Step 3: set device
cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Step 4: define model
predictor = DefaultPredictor(cfg)


def analyze_image(img):
    md = MetadataCatalog.get(cfg.DATASETS.TEST[0])
    if cfg.DATASETS.TEST[0]=='icdar2019_test':
        md.set(thing_classes=["table"])
    else:
        md.set(thing_classes=["text","title","list","table","figure"]) ## these are categories from PubLayNet (PubMed PDF/XML data): https://ieeexplore.ieee.org/document/8977963

    outputs = predictor(img)
    instances = outputs["instances"]

    # Ensure we're operating on CPU for numpy compatibility
    instances = instances.to("cpu")

    # Filter out figures based on class labels
    high_confidence = []
    medium_confidence = []
    low_confidence = []
    for i in range(len(instances)):
        if md.thing_classes[instances.pred_classes[i]] == "figure":
            box = instances.pred_boxes.tensor[i].numpy().astype(int)
            cropped_img = img[box[1]:box[3], box[0]:box[2]]
            confidence_score = instances.scores[i].numpy() * 100  # convert to percentage
            confidence_text = f"Score: {confidence_score:.2f}%"

            # Overlay confidence score on the image
            # Enhanced label visualization with orange color
            font_scale = 0.9
            font_thickness = 2
            text_color = (255, 255, 255)  # white background
            background_color = (255, 165, 0)  # RGB for orange

            (text_width, text_height), _ = cv2.getTextSize(confidence_text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, font_thickness)
            padding = 12
            text_offset_x = padding - 3
            text_offset_y = cropped_img.shape[0] - padding + 2
            box_coords = ((text_offset_x, text_offset_y + padding // 2), (text_offset_x + text_width + padding, text_offset_y - text_height - padding // 2))
            cv2.rectangle(cropped_img, box_coords[0], box_coords[1], background_color, cv2.FILLED)
            cv2.putText(cropped_img, confidence_text, (text_offset_x, text_offset_y), cv2.FONT_HERSHEY_SIMPLEX, font_scale, text_color, font_thickness)

            # Categorize images based on confidence levels
            if confidence_score > 85:
                high_confidence.append(cropped_img)
            elif confidence_score > 50:
                medium_confidence.append(cropped_img)
            else:
                low_confidence.append(cropped_img)

    v = Visualizer(img[:, :, ::-1], md, scale=1.0, instance_mode=ColorMode.SEGMENTATION)
    result_image = v.draw_instance_predictions(instances).get_image()[:, :, ::-1]

    return result_image, high_confidence, medium_confidence, low_confidence
#    output = predictor(img)["instances"]
#    v = Visualizer(img[:, :, ::-1],
#                    md,
#                    scale=1.0,
#                    instance_mode=ColorMode.SEGMENTATION)
#    result = v.draw_instance_predictions(output.to("cpu"))
#    result_image = result.get_image()[:, :, ::-1]
#
##    figs = [img[box[1]:box[3], box[0]:box[2]] for box, cls in zip(output.pred_boxes, output.pred_classes) if md.thing_classes[cls] == "figure"]
#
#    return result_image, figs

def handle_input(input_data):
  images = []

  #input_data is a dict with keys 'text' and 'files'
  if 'text' in input_data and input_data['text']:
    input_text = input_data['text'].strip()

    # this is either a URL or a PDF ID
    if input_text.startswith('http://') or input_text.startswith('https://'):
      # Extract the ID from the URL
      url_parts = urlparse(input_text)
      query_params = parse_qs(url_parts.fragment)  # Assumes ID is a fragment parameter
      pdf_id = query_params.get('id', [None])[0]
      if not pdf_id:
         raise ValueError("PDF ID not found in URL")
    else:
      # Assume input is a direct PDF ID
      pdf_id = input_text

    if not re.match(r'^[a-zA-Z]{4}\d{4}$', pdf_id):
      raise ValueError("Invalid PDF ID format. Expected four letters followed by four numbers.")

    # Assume input is a PDF ID, convert to URL
    # Now construct the download URL
    pdf_url = construct_download_url(pdf_id)

    #https://download.industrydocuments.ucsf.edu/k/t/k/l/ktkl0236/ktkl0236.pdf
    # Assume input is a PDF URL
    pdf_data = download_pdf(pdf_url)
    images = pdf_to_images(pdf_data)

  if 'files' in input_data and input_data['files']:
    for file_path in input_data['files']:
      print("Type of file as uploaded:", type(file_path))
      print(f"  File: {file_path}")

      # Check if the input is a file and determine its type
      kind = filetype.guess(file_path)
      if kind.mime.startswith('image'):
        # Process a single image
        images.append(load_image(file_path))  # Process image directly
      elif kind.mime == 'application/pdf':
        # Convert PDF pages to images
        images.extend(pdf_to_images(file_path))
      else:
        raise ValueError("Unsupported file type.")
  if not images:
     raise ValueError("No valid input provided. Please upload a file or enter a PDF ID.")

  # Assuming processing images returns galleries of images by confidence
  return process_images(images)

def load_image(img_path):
  print(f"Loading image: {img_path}")
  # Load an image from a file path
  image = Image.open(img_path)
  if isinstance(image, Image.Image):
    image = np.array(image)  # Convert PIL Image to numpy array
  # Ensure the image is in the correct format
  if image.ndim == 2:  # Image is grayscale
    image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
  elif image.ndim == 3 and image.shape[2] == 3:
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
#     image = image[:, :, ::-1]  # Convert RGB to BGR if necessary

  return image

def construct_download_url(pdf_id):
  # Construct the download URL from the PDF ID
  # https://download.examples.edu/k/t/k/l/ktkl0236/ktkl0236.pdf
  path_parts = '/'.join(pdf_id[i] for i in range(4))  # 'k/t/k/l'
  download_url = f"https://download.industrydocuments.ucsf.edu/{path_parts}/{pdf_id}/{pdf_id}.pdf"
  return download_url


def download_pdf(pdf_url):
    # Download the PDF file from the given URL
    response = requests.get(pdf_url)
    response.raise_for_status()  # Ensure we notice bad responses
    return BytesIO(response.content)


def pdf_to_images(data_or_path):
  # Create a temporary directory to store the page images
  temp_dir = "temp_images"
  os.makedirs(temp_dir, exist_ok=True)


  try:
    # Convert PDF to a list of PIL images
    # Handle both BytesIO and file path input for PDF conversion
    if isinstance(data_or_path, BytesIO):
      # Convert directly from bytes
      pages = convert_from_bytes(data_or_path.read())
    elif isinstance(data_or_path, str):
      # Convert from a file path
      pages = convert_from_path(data_or_path)

    # Save each page as an image file
    page_images = []
    for i, page in enumerate(pages):
      image_path = os.path.join(temp_dir, f"page_{i+1}.jpg")
      page.save(image_path, "JPEG")
      page_images.append(load_image(image_path))

    return page_images

  except Exception as e:
    print(f"Error converting PDF to images: {str(e)}")
    return []
  finally:
    # Clean up the temporary directory (optional)
    # os.rmdir(temp_dir)
    pass

def process_images(images):
  all_processed_images = []
  all_high_confidence = []
  all_medium_confidence = []
  all_low_confidence = []

  for img in images:
    #print("Type of img before processing:", type(img))
    #print(f"   img before processing: {img}")
    processed_images, high_confidence, medium_confidence, low_confidence = analyze_image(img)
    all_processed_images.append(processed_images)
    all_high_confidence.extend(high_confidence)
    all_medium_confidence.extend(medium_confidence)
    all_low_confidence.extend(low_confidence)

  return all_processed_images, all_high_confidence, all_medium_confidence, all_low_confidence

title = "OIDA Image Collection Interactive demo: Document Layout Analysis with DiT and PubLayNet"
description = "<h3>OIDA Demo -- adapted liberally from <a href='https://huggingface.co/spaces/nielsr/dit-document-layout-analysis'>https://huggingface.co/spaces/nielsr/dit-document-layout-analysis</a></h3>Demo for Microsoft's DiT, the Document Image Transformer for state-of-the-art document understanding tasks. This particular model is fine-tuned on PubLayNet, a large dataset for document layout analysis (read more at the links below). To use it, simply upload an image or use the example image below and click 'Submit'. Results will show up in a few seconds. If you want to make the output bigger, right-click on it and select 'Open image in new tab'."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2203.02378' target='_blank'>Paper</a> | <a href='https://github.com/microsoft/unilm/tree/master/dit' target='_blank'>Github Repo</a> | <a href='https://huggingface.co/docs/transformers/master/en/model_doc/dit' target='_blank'>HuggingFace doc</a> | <a href='https://ieeexplore.ieee.org/document/8977963' target='_blank'>PubLayNet paper</a></p>"
#examples =[['fpmj0236_Page_012.png'],['fnmf0234_Page_2.png'],['publaynet_example.jpeg'],['fpmj0236_Page_018.png'],['lrpw0232_Page_14.png'],['kllx0250'],['https://www.industrydocuments.ucsf.edu/opioids/docs/#id=yqgg0230']]
examples =[{'files': ['fnmf0234_Page_2.png']},{'files': ['fpmj0236_Page_012.png']},{'files': ['lrpw0232.pdf']},{'text': 'https://www.industrydocuments.ucsf.edu/opioids/docs/#id=yqgg0230'},{'files':['fpmj0236_Page_018.png']},{'files':['lrpw0232_Page_14.png']},{'files':['publaynet_example.jpeg']},{'text':'kllx0250'},{'text':'txhk0255'}]
#txhk0255
css = ".output-image, .input-image, .image-preview {height: 600px !important} td.textbox {display:none;} #component-5 .submit-button {display:none;}"

#iface = gr.Interface(fn=handle_input,
#                     inputs=gr.MultimodalTextbox(interactive=True,
#                                                 label="Upload image/PDF file OR enter OIDA ID or URL",
#                                                 file_types=["image",".pdf"],
#                                                 placeholder="Upload image/PDF file OR enter OIDA ID or URL"),
#                     outputs=[gr.Gallery(label="annotated documents"),
#                              gr.Gallery(label="Figures with High (>85%) Confidence Scores"),
#                              gr.Gallery(label="Figures with Moderate (50-85%) Confidence Scores"),
#                              gr.Gallery(label="Figures with Lower Confidence (under 50%) Scores")],
#                     title=title,
#                     description=description,
#                     examples=examples,
#                     article=article,
#                     css=css)
##                     enable_queue=True)
with gr.Blocks(css=css) as iface:
    gr.Markdown(f"# {title}")
    gr.HTML(description)

    with gr.Row():
        with gr.Column():
            input = gr.MultimodalTextbox(interactive=True,
                                         label="Upload image/PDF file OR enter OIDA ID or URL",
                                         file_types=["image",".pdf"],
                                         placeholder="Upload image/PDF file OR enter OIDA ID or URL",
                                         submit_btn=None)
            submit_btn = gr.Button("Submit")
            gr.HTML('<br /><br /><hr />')
            gr.Examples(examples, [input])

        with gr.Column():
            outputs = [gr.Gallery(label="annotated documents"),
                       gr.Gallery(label="Figures with High (>85%) Confidence Scores"),
                       gr.Gallery(label="Figures with Moderate (50-85%) Confidence Scores"),
                       gr.Gallery(label="Figures with Lower Confidence (under 50%) Scores")]

    with gr.Row():
      gr.HTML(article)
    submit_btn.click(handle_input, [input], outputs)

iface.launch(debug=True, auth=[("oida", "OIDA3.1"), ("Brian", "Hi")]) #, cache_examples=True)