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 collections import namedtuple
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)

# Set up internal data structure
# Define a namedtuple for holding extracted image data
ExtractedImage = namedtuple("ExtractedImage", ["image", "annotated_page", "original_page", "confidence_score", "top_left", "bottom_right", "num_pixels", "is_color"])


def analyze_image(img):
  images = extract_images(img)

  # Filter out figures based on class labels
  high_confidence = []
  medium_confidence = []
  low_confidence = []
  result_image = img

  for extracted_image_object in images:
    cropped_img = extracted_image_object.image
    confidence_score = extracted_image_object.confidence_score
    confidence_text = f"Score: {confidence_score:.2f}%"

    if cropped_img is not None:
      # 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 = (0, 165, 255)  # BGR for orange
      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)
      # end adding score annotation

    #result_image.append(extracted_image_object.annotated_page)
    if extracted_image_object.annotated_page is not None:
      result_image = extracted_image_object.annotated_page
    # Categorize images based on confidence levels
    if confidence_score > 85:
      high_confidence.append(cropped_img)
    elif confidence_score > 50:
      medium_confidence.append(cropped_img)
    elif cropped_img is not None:
      low_confidence.append(cropped_img)

  return result_image, high_confidence, medium_confidence, low_confidence


def extract_images(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
    
  is_color = None
  print(f"###################### Is effectively grayscale? {is_effectively_grayscale_np(img)} #######################")
  print(f"############################### ndim {img.ndim} -- shape[2] {img.shape[2]} #######################")
  # Ensure the image is in the correct format
  if img.ndim == 2:  # Image is grayscale, needs converting
    img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
  elif img.ndim == 3 and img.shape[2] == 3:
    if not is_effectively_grayscale_np(img):  # Image is RGB mode, but still only using grayscale colors
      img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
      is_color = True
  outputs = predictor(img)
  instances = outputs["instances"]

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

  extracted_images = []

  v = Visualizer(img[:, :, ::-1], md, scale=1.0, instance_mode=ColorMode.SEGMENTATION)
  result_image = v.draw_instance_predictions(instances).get_image()[:, :, ::-1]
  result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)

  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]]
      cropped_img = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2RGB)
      confidence_score = instances.scores[i].numpy() * 100  # convert to percentage
      num_pixels = cropped_img.shape[0] * cropped_img.shape[1]
      is_color = len(cropped_img.shape) == 3 and cropped_img.shape[2] == 3 and not is_effectively_grayscale_np(img)

      extracted_images.append(ExtractedImage(
            image=cropped_img,
            annotated_page=result_image,
            original_page=img,
            confidence_score=confidence_score,
            top_left=f"{box[0]}-{box[1]}",
            bottom_right=f"{box[2]}-{box[3]}",
            num_pixels=num_pixels,
            is_color=is_color
      ))

  if not extracted_images: # there were none to process, still need to return basic image
      extracted_images.append(ExtractedImage(
          image=None,  # or an appropriate default value
          annotated_page=result_image,
          original_page=img,  # The original input image
          confidence_score=-1,  # Indicates no confidence
          top_left=None,
          bottom_right=None,  # No bounding box coordinates
          num_pixels=0,  # No pixels counted
          is_color=False  # Default to grayscale or False
      ))

  return extracted_images


def is_effectively_grayscale_np(array):
  if array.ndim != 3 or array.shape[2] != 3:
    raise ValueError("Input must be an RGB image")
  # Check if all color channels are equal across the image
  r, g, b = array[:,:,0], array[:,:,1], array[:,:,2]
  return np.array_equal(r, g) and np.array_equal(g, b)


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 process_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)
  print(f"  Image mode: {image.mode}")  # Add this debug line
  if image.mode != 'RGB':
    print(f"  Converting from {image.mode} to RGB")
    image = image.convert('RGB')
  if isinstance(image, Image.Image):
    print("  Converting to numpy")
    image = np.array(image)  # Convert PIL Image to numpy array
    print(f"  Array shape: {image.shape}")  # Add this debug line
  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 = []

  idx = 0
  for img in images:
    idx += 1
    #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)
    if processed_images is None:
      print(f"  ******* processed_images is None on page: {idx}")
    else:
      all_processed_images.append(processed_images)
      print(f"  ******* type of processed_images: {type(processed_images)}")

    if not high_confidence:
      print(f"  ******* high_confidence is empty on page: {idx}")
    all_high_confidence.extend(high_confidence)

    if not medium_confidence:
      print(f"  ******* medium_confidence is empty on page: {idx}")
    all_medium_confidence.extend(medium_confidence)

    if not low_confidence:
      print(f"  ******* low_confidence is empty on page: {idx}")
    all_low_confidence.extend(low_confidence)

  print(f"  ******* Size of all_process_images: {len(all_processed_images)}")
  for item in all_processed_images: print(f"Type Check all_processed: {type(item)}")
  print(f"  ******* Size of all_high_conf: {len(all_high_confidence)}")
  for item in all_high_confidence: print(f"Type Check high_conf: {type(item)}")
  print(f"  ******* Size of all_med: {len(all_medium_confidence)}")
  for item in all_medium_confidence: print(f"Type Check med_conf: {type(item)}")
  print(f"  ******* Size of all_low: {len(all_low_confidence)}")
  for item in all_low_confidence: print(f"Type Check low_conf: {type(item)}")
  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'},{'text':'gpdk0256'}]

css = ".output-image, .input-image, .image-preview {height: 600px !important} td.textbox {display:none;} #component-5 .submit-button {display:none;}"

def setup_gradio_interface():
  #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)

  return iface

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

if __name__ == "__main__":
  main()