import base64
import re
from tempfile import TemporaryDirectory
from math import atan, cos, sin
from typing import Dict, Optional, Tuple
from xml.etree import ElementTree as ET
from xml.etree.ElementTree import Element
import numpy as np
import PyPDF2
from PyPDF2 import PdfFileMerger
from doctr.io import DocumentFile
from doctr.models import ocr_predictor
from PIL import Image
from reportlab.lib.colors import black
from reportlab.lib.units import inch
from reportlab.lib.utils import ImageReader
from reportlab.pdfgen.canvas import Canvas
class HocrParser():
def __init__(self):
self.box_pattern = re.compile(r'bbox((\s+\d+){4})')
self.baseline_pattern = re.compile(r'baseline((\s+[\d\.\-]+){2})')
def _element_coordinates(self, element: Element) -> Dict:
"""
Returns a tuple containing the coordinates of the bounding box around
an element
"""
out = out = {'x1': 0, 'y1': 0, 'x2': 0, 'y2': 0}
if 'title' in element.attrib:
matches = self.box_pattern.search(element.attrib['title'])
if matches:
coords = matches.group(1).split()
out = {'x1': int(coords[0]), 'y1': int(
coords[1]), 'x2': int(coords[2]), 'y2': int(coords[3])}
return out
def _get_baseline(self, element: Element) -> Tuple[float, float]:
"""
Returns a tuple containing the baseline slope and intercept.
"""
if 'title' in element.attrib:
matches = self.baseline_pattern.search(
element.attrib['title']).group(1).split()
if matches:
return float(matches[0]), float(matches[1])
return (0.0, 0.0)
def _pt_from_pixel(self, pxl: Dict, dpi: int) -> Dict:
"""
Returns the quantity in PDF units (pt) given quantity in pixels
"""
pt = [(c / dpi * inch) for c in pxl.values()]
return {'x1': pt[0], 'y1': pt[1], 'x2': pt[2], 'y2': pt[3]}
def _get_element_text(self, element: Element) -> str:
"""
Return the textual content of the element and its children
"""
text = ''
if element.text is not None:
text += element.text
for child in element:
text += self._get_element_text(child)
if element.tail is not None:
text += element.tail
return text
def export_pdfa(self,
out_filename: str,
hocr: ET.ElementTree,
image: Optional[np.ndarray] = None,
fontname: str = "Times-Roman",
fontsize: int = 12,
invisible_text: bool = True,
add_spaces: bool = True,
dpi: int = 300):
"""
Generates a PDF/A document from a hOCR document.
"""
width, height = None, None
# Get the image dimensions
for div in hocr.findall(".//div[@class='ocr_page']"):
coords = self._element_coordinates(div)
pt_coords = self._pt_from_pixel(coords, dpi)
width, height = pt_coords['x2'] - \
pt_coords['x1'], pt_coords['y2'] - pt_coords['y1']
# after catch break loop
break
if width is None or height is None:
raise ValueError("Could not determine page size")
pdf = Canvas(out_filename, pagesize=(width, height), pageCompression=1)
span_elements = [element for element in hocr.iterfind(".//span")]
for line in span_elements:
if 'class' in line.attrib and line.attrib['class'] == 'ocr_line' and line is not None:
# get information from xml
pxl_line_coords = self._element_coordinates(line)
line_box = self._pt_from_pixel(pxl_line_coords, dpi)
# compute baseline
slope, pxl_intercept = self._get_baseline(line)
if abs(slope) < 0.005:
slope = 0.0
angle = atan(slope)
cos_a, sin_a = cos(angle), sin(angle)
intercept = pxl_intercept / dpi * inch
baseline_y2 = height - (line_box['y2'] + intercept)
# configure options
text = pdf.beginText()
text.setFont(fontname, fontsize)
pdf.setFillColor(black)
if invisible_text:
text.setTextRenderMode(3) # invisible text
# transform overlayed text
text.setTextTransform(
cos_a, -sin_a, sin_a, cos_a, line_box['x1'], baseline_y2)
elements = line.findall(".//span[@class='ocrx_word']")
for elem in elements:
elemtxt = self._get_element_text(elem).strip()
# replace unsupported characters
elemtxt = elemtxt.translate(str.maketrans(
{'ff': 'ff', 'ffi': 'ffi', 'ffl': 'ffl', 'fi': 'fi', 'fl': 'fl'}))
if not elemtxt:
continue
# compute string width
pxl_coords = self._element_coordinates(elem)
box = self._pt_from_pixel(pxl_coords, dpi)
if add_spaces:
elemtxt += ' '
box_width = box['x2'] + pdf.stringWidth(elemtxt, fontname, fontsize) - box['x1']
else:
box_width = box['x2'] - box['x1']
font_width = pdf.stringWidth(elemtxt, fontname, fontsize)
# Adjust relative position of cursor
cursor = text.getStartOfLine()
dx = box['x1'] - cursor[0]
dy = baseline_y2 - cursor[1]
text.moveCursor(dx, dy)
# suppress text if it is 0 units wide
if font_width > 0:
text.setHorizScale(100 * box_width / font_width)
text.textOut(elemtxt)
pdf.drawText(text)
# overlay image if provided
if image is not None:
pdf.drawImage(ImageReader(Image.fromarray(image)),
0, 0, width=width, height=height)
pdf.save()
from langchain_huggingface import HuggingFaceEmbeddings
from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM
from langchain_community.vectorstores import Chroma
from langchain.schema import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
import torch
embedding_model_name = 'l3cube-pune/punjabi-sentence-similarity-sbert'
model_kwargs = {'device':'cpu',"trust_remote_code": True}
embeddings = HuggingFaceEmbeddings(
model_name=embedding_model_name,
model_kwargs=model_kwargs
)
vectorstore = None
def read_file(data: str) -> Document:
f = open(data,'r')
content = f.read()
f.close()
doc = Document(page_content=content, metadata={"name": data.split('/')[-1]})
return doc
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=400)
def add_doc(data,vectorstore):
doc = read_file(data)
splits = text_splitter.split_documents([doc])
vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
retriever = vectorstore.as_retriever(search_kwargs={'k':1})
return retriever, vectorstore
def delete_doc(delete_name,vectorstore):
delete_doc_ids = []
for idx,name in enumerate(vectorstore.get()['metadatas']):
if name['name'] == delete_name:
delete_doc_ids.append(vectorstore.get()['ids'][idx])
for id in delete_doc_ids:
vectorstore.delete(ids = id)
# vectorstore.persist()
retriever = vectorstore.as_retriever(search_kwargs={'k':1})
return retriever, vectorstore
def delete_all_doc(vectorstore):
delete_doc_ids = vectorstore.get()['ids']
for id in delete_doc_ids:
vectorstore.delete(ids = id)
# vectorstore.persist()
retriever = vectorstore.as_retriever(search_kwargs={'k':1})
return retriever, vectorstore