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Update Find_Hyperlinking_text.py
Browse files- Find_Hyperlinking_text.py +226 -189
Find_Hyperlinking_text.py
CHANGED
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@@ -1,3 +1,4 @@
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import fitz # PyMuPDF
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from io import BytesIO
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import re
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@@ -10,7 +11,8 @@ import urllib.parse
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import pandas as pd
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import tempfile
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from fpdf import FPDF
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baselink='https://marthee-nbslink.hf.space/view-pdf?'
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class PDF(FPDF):
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pdf.set_right_margin(margin)
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pdf.add_page()
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pdf.set_font("Arial", size=
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#
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table_width = col_width * num_cols
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# Get page width and calculate left alignment
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# Table headers
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pdf.set_fill_color(200, 200, 200) # Light gray background
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pdf.set_font("Arial", "B",
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# Draw table headers
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for header in headers:
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pdf.cell(col_width, 8, header, border=1, fill=True, align="C")
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pdf.ln()
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pdf.set_font("Arial", size=
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# Add rows
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for _, row in df.iterrows():
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x_start = start_x # Ensure every row starts at the same position
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y_start = pdf.get_y()
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# Calculate max height needed for this row
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text_lines = {col: pdf.multi_cell(col_width, 5, row[col], border=0, align="L", split_only=True) for col in ["
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max_lines = max(len(lines) for lines in text_lines.values())
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max_height = max_lines * 5
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pdf.set_xy(x_start + col_width, y_start)
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# Draw each cell manually, ensuring equal height
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for i, col_name in enumerate(["
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x_col = x_start + col_width * (i + 1)
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y_col = y_start
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pdf.multi_cell(col_width, 5, row[col_name], border=0, align="L") # Draw text
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# Move to the next row
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pdf.ln(max_height)
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# pdf_output = 'output.pdf'
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pdf_output = pdf.output(dest="S").encode("latin1") # Returns the PDF as a byte string
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return pdf_output
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text = text.lower().strip()
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text = re.sub(r'\s+', ' ', text) # Normalize multiple spaces
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return re.sub(r'[^\w\s]', '', text) # Remove punctuation
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def get_repeated_texts(pdf_document, threshold=0.
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"""
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Identify text that appears on most pages.
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:param pdf_document: The opened PDF document.
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"""
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Annotates text under a specific heading in a PDF, highlights it,
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and constructs zoom coordinates for the first occurrence of the heading.
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Args:
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pdfshareablelinks (list): List of shareable links to PDFs.
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heading_to_search (str): The heading to search for in the PDF.
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Returns:
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Tuple: Annotated PDF bytes, count of heading occurrences, and zoom string.
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"""
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# Open the PDF using PyMuPDF
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pdf_document = fitz.open(stream=pdf_content, filetype="pdf")
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repeated_texts = get_repeated_texts(pdf_document)
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df = pd.DataFrame(columns=["NBSLink","
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dictionaryNBS={}
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for NBSindex, heading_to_search in enumerate(LISTheading_to_search):
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if NBSindex == len(LISTheading_to_search) - 1:
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flagAllNBSvisited = True
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f10_count = 0
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current_y = None
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highlight_rect = None
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zoom_str = None
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toc_flag = False
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span_font_goal = None
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groupheadings = []
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merged_groupheadings = []
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collectheader2 = False
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header2 = ''
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header2_first_span_size = 0
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previous_header = ''
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span_y = span['bbox'][1]
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span_font = span['font']
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span_size = span['size']
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# Check if it's not the last span in the current line
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print(i + 1, len(spans))
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if i + 1 < len(spans):
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next_span_text = (spans[i + 1]['text'].strip())
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# Check if the next span's text is in the heading list
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if next_span_text.replace(" ", "") in heading_to_search.replace(" ", ""):
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text = (span_text + ' ' + next_span_text)
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# After processing the current line, check if there's a next line
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if first_word == span_text:
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if line_index + 1 < len(block.get('lines', [])):
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next_line = block['lines'][line_index + 1]
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# You can process the spans of the next line here
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for next_span in next_line.get('spans', []):
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next_span_text = next_span['text'].strip()
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text = span_text + ' ' + next_span_text
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if len(merged_groupheadings) > 0:
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if re.match(r"[A-Za-z]\d{2}", span_text) and span_size > 10:
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previous_header = span_text # Store last detected header
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print('previous_header', span_text)
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groupmainheadingFromArray = [item for item in merged_groupheadings if previous_header in item]
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if previous_header:
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if not collectheader2:
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if header2_first_span_size == 0:
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spanSizeHeader = 10
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else:
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if current_line.strip():
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all_text += current_line.strip() + '\n' # Append the current line
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print(df)
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print(dictionaryNBS)
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xx=save_df_to_pdf(df)
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outputpdfFitz =fitz.open('pdf',xx)
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pdf_bytes = BytesIO()
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pdf_document.save(pdf_bytes)
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+
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import fitz # PyMuPDF
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from io import BytesIO
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import re
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import pandas as pd
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import tempfile
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from fpdf import FPDF
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import json
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from datetime import datetime
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baselink='https://marthee-nbslink.hf.space/view-pdf?'
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class PDF(FPDF):
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pdf.set_right_margin(margin)
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pdf.add_page()
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pdf.set_font("Arial", size=8) # Reduce font size to fit more text
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# Table headers
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headers = ["NBSLink", "Subject","Page","Author","Creation Date","Layer",'Code', 'head above 1', "head above 2"]
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num_cols = len(headers)
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# Calculate column width dynamically
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max_table_width = pdf.w - 2 * margin # Total available width
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col_width = max_table_width / num_cols # Distribute evenly
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table_width = col_width * num_cols
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# Get page width and calculate left alignment
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# Table headers
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pdf.set_fill_color(200, 200, 200) # Light gray background
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pdf.set_font("Arial", "B", 8)
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for header in headers:
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pdf.cell(col_width, 8, header, border=1, fill=True, align="C")
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pdf.ln()
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pdf.set_font("Arial", size=7) # Reduce font size for data rows
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for _, row in df.iterrows():
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x_start = start_x # Ensure every row starts at the same position
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y_start = pdf.get_y()
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# Calculate max height needed for this row
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text_lines = {col: pdf.multi_cell(col_width, 5, row[col], border=0, align="L", split_only=True) for col in ["Subject","Page","Author","Creation Date","Layer",'Code', 'head above 1', "head above 2"]}
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max_lines = max(len(lines) for lines in text_lines.values())
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max_height = max_lines * 5
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pdf.set_xy(x_start + col_width, y_start)
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# Draw each cell manually, ensuring equal height
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for i, col_name in enumerate(["Subject","Page","Author","Creation Date","Layer",'Code', 'head above 1', "head above 2"]):
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x_col = x_start + col_width * (i + 1)
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y_col = y_start
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pdf.multi_cell(col_width, 5, row[col_name], border=0, align="L") # Draw text
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# Move to the next row
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pdf.ln(max_height)
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# Save PDF to memory
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pdf_output = pdf.output(dest="S").encode("latin1")
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return pdf_output
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text = text.lower().strip()
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text = re.sub(r'\s+', ' ', text) # Normalize multiple spaces
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return re.sub(r'[^\w\s]', '', text) # Remove punctuation
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def get_repeated_texts(pdf_document, threshold=0.85):
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"""
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Identify text that appears on most pages.
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:param pdf_document: The opened PDF document.
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"""
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Annotates text under a specific heading in a PDF, highlights it,
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and constructs zoom coordinates for the first occurrence of the heading.
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Args:
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pdfshareablelinks (list): List of shareable links to PDFs.
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heading_to_search (str): The heading to search for in the PDF.
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Returns:
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Tuple: Annotated PDF bytes, count of heading occurrences, and zoom string.
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"""
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# Open the PDF using PyMuPDF
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pdf_document = fitz.open(stream=pdf_content, filetype="pdf")
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repeated_texts = get_repeated_texts(pdf_document)
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df = pd.DataFrame(columns=["NBSLink","Subject","Page","Author","Creation Date","Layer",'Code', 'head above 1', "head above 2"])
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dictionaryNBS={}
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data_list_JSON = []
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for NBSindex, heading_to_search in enumerate(LISTheading_to_search):
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if NBSindex == len(LISTheading_to_search) - 1:
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flagAllNBSvisited = True
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f10_count = 0
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current_y = None
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highlight_rect = None
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highlight_rectEnding=None
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highlight_rectBegin=None
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zoom_str = None
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toc_flag = False
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span_font_goal = None
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groupheadings = []
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merged_groupheadings = []
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collectheader2 = False
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endingcontentFlag=True
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header2 = ''
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header2_first_span_size = 0
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previous_header = ''
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span_y = span['bbox'][1]
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span_font = span['font']
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span_size = span['size']
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if normalize_text(span_text) not in repeated_texts and not (span_text.startswith('Page')):
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if previous_y is None:
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previous_y = span_y # Initialize on first span
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# If same Y coordinate as previous, append to the current line
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if abs(span_y - previous_y) < 5: # Allow a small margin for OCR variations
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current_line_text += " " + span_text
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current_line_text = normalize_text(current_line_text)
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current_line_span_size = span_size
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else:
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# Store the complete line and reset for the new line
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if current_line_text.strip():
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all_text.append(current_line_text.strip())
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current_line_text = span_text # Start a new line
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previous_y = span_y # Update the reference Y
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text = span_text
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if collecting_text and span_font == span_font_goal and span_size == span_size_goal and span_text[0].isdigit():
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print(f"Ending collection at heading: {span_text}")
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highlight_rectEnding=highlight_rect
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print("merged_groupheadings:", merged_groupheadings)
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+
print('groupheadingss',groupheadings)
|
| 240 |
+
collecting_text = False
|
| 241 |
+
continue
|
| 242 |
+
if collecting_text:
|
| 243 |
+
annot = page.add_rect_annot(highlight_rect) # Create a rectangle annotation
|
| 244 |
+
annot.set_colors(stroke=(1, 0, 0)) # Set border color (Red)
|
| 245 |
+
annot.update() # Apply changes
|
| 246 |
+
|
| 247 |
+
if 'Content' in span_text:
|
| 248 |
+
toc_flag = True
|
| 249 |
+
TOC_start = span_text
|
| 250 |
+
print('content', TOC_start, span_size)
|
| 251 |
+
|
| 252 |
+
if toc_flag or endingcontentFlag:
|
| 253 |
+
if 'Content' not in span_text:
|
| 254 |
+
if current_y is None:
|
| 255 |
+
current_y = span_y
|
| 256 |
+
current_size = span_size # Initialize the reference span size
|
| 257 |
+
# Check if the current span size deviates significantly
|
| 258 |
+
if abs(span_size - current_size) > 1: # Threshold for size difference
|
| 259 |
+
toc_flag = False
|
| 260 |
+
|
| 261 |
+
if abs(current_y - span_y) < 5: # Allowing more flexibility for multi-line headings
|
| 262 |
+
current_line += " " + span_text # Keep accumulating text
|
| 263 |
+
else:
|
| 264 |
+
if current_line.strip(): # Only process non-empty lines
|
| 265 |
+
clean_text = re.sub(r'\.{5,}\d*$', '', current_line, flags=re.MULTILINE) # Remove dots and trailing numbers
|
| 266 |
+
|
| 267 |
+
print(clean_text.strip())
|
| 268 |
+
if clean_text:
|
| 269 |
+
groupheadings.append(clean_text)
|
| 270 |
+
# else:
|
| 271 |
+
# toc_flag = False
|
| 272 |
+
|
| 273 |
+
current_line = span_text
|
| 274 |
+
current_y = span_y
|
| 275 |
+
current_size = span_size # Update reference span size
|
| 276 |
+
# print('outofcurrent')
|
| 277 |
+
if len(groupheadings) > 0:
|
| 278 |
+
pattern = re.compile(r"^[A-Za-z]\d{2} ") # Match headings starting with letter + 2 digits
|
| 279 |
+
merged_groupheadings = []
|
| 280 |
+
current_item = None # Start as None to avoid an initial blank entry
|
| 281 |
+
|
| 282 |
+
for item in groupheadings:
|
| 283 |
+
if pattern.match(item): # If item starts with correct pattern, it's a new heading
|
| 284 |
+
if current_item: # Append only if current_item is not empty
|
| 285 |
+
if current_item not in merged_groupheadings:
|
| 286 |
+
extracted_text = re.split(r"\.{3,}", current_item)[0].strip()
|
| 287 |
+
merged_groupheadings.append(extracted_text.strip())
|
| 288 |
+
current_item = item # Start new heading
|
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|
| 289 |
else:
|
| 290 |
+
if current_item:
|
| 291 |
+
if item not in current_item:
|
| 292 |
+
current_item += " " + item # Merge with previous heading
|
| 293 |
+
|
| 294 |
+
# Append last merged item after loop
|
| 295 |
+
if current_item:
|
| 296 |
+
if current_item not in merged_groupheadings:
|
| 297 |
+
extracted_text = re.split(r"\.{3,}", current_item)[0].strip()
|
| 298 |
+
merged_groupheadings.append(extracted_text.strip())
|
| 299 |
+
if span_text == first_word:
|
| 300 |
+
print('First word found:', span_text)
|
| 301 |
+
# Check if it's not the last span in the current line
|
| 302 |
+
print(i + 1, len(spans))
|
| 303 |
+
if i + 1 < len(spans):
|
| 304 |
+
next_span_text = (spans[i + 1]['text'].strip())
|
| 305 |
+
# Check if the next span's text is in the heading list
|
| 306 |
+
if next_span_text.replace(" ", "") in heading_to_search.replace(" ", ""):
|
| 307 |
+
text = (span_text + ' ' + next_span_text)
|
| 308 |
+
# After processing the current line, check if there's a next line
|
| 309 |
+
if first_word == span_text:
|
| 310 |
+
if line_index + 1 < len(block.get('lines', [])):
|
| 311 |
+
next_line = block['lines'][line_index + 1]
|
| 312 |
+
# You can process the spans of the next line here
|
| 313 |
+
for next_span in next_line.get('spans', []):
|
| 314 |
+
next_span_text = next_span['text'].strip()
|
| 315 |
+
text = span_text + ' ' + next_span_text
|
| 316 |
+
if len(merged_groupheadings) > 0:
|
| 317 |
+
if re.match(r"[A-Za-z]\d{2}", span_text) and span_size > 10:
|
| 318 |
+
toc_flag = False
|
| 319 |
+
endingcontentFlag=False
|
| 320 |
+
previous_header = span_text # Store last detected header
|
| 321 |
+
print('previous_header', span_text)
|
| 322 |
+
|
| 323 |
+
groupmainheadingFromArray = [item for item in merged_groupheadings if previous_header in item]
|
| 324 |
+
|
| 325 |
+
if previous_header:
|
| 326 |
+
if not collectheader2:
|
| 327 |
+
if header2_first_span_size == 0:
|
| 328 |
+
spanSizeHeader = 10
|
| 329 |
+
else:
|
| 330 |
+
spanSizeHeader = header2_first_span_size
|
| 331 |
+
|
| 332 |
+
for item in groupmainheadingFromArray:
|
| 333 |
+
if not any(normalize_text(current_line_text) in normalize_text(item) for item in groupmainheadingFromArray):
|
| 334 |
+
if not current_line_text[0].isdigit() :
|
| 335 |
+
if span_size >= spanSizeHeader:
|
| 336 |
+
if not re.match(r"^\d{2}", current_line_text) and current_line_text not in repeated_texts and "Bold" in span["font"] :
|
| 337 |
+
if len(header2) > 0 :
|
| 338 |
+
header2_first_span_size = span_size
|
| 339 |
+
header2 = current_line_text
|
| 340 |
+
print('header2', header2, span_size, spanSizeHeader)
|
| 341 |
+
|
| 342 |
+
trimmed_text = text.replace(" ", "")
|
| 343 |
+
if len(text) > 0:
|
| 344 |
+
if text.split()[0] in heading_words:
|
| 345 |
+
if len(trimmed_text) > 0 and (heading_to_search.replace(" ", "") in trimmed_text):
|
| 346 |
+
print(trimmed_text, heading_to_search)
|
| 347 |
+
f10_count += 1
|
| 348 |
+
# Start collecting text under the second occurrence of the heading
|
| 349 |
+
if f10_count == 1:
|
| 350 |
+
collecting_text = True
|
| 351 |
+
print(f"Starting collection under heading: {text}, {span_font}, {span_size}")
|
| 352 |
+
collectheader2 = True
|
| 353 |
+
NBS_heading = heading_to_searchNBS
|
| 354 |
+
highlight_rectBegin=highlight_rect
|
| 355 |
+
x0, y0, x1, y1 = highlight_rectBegin
|
| 356 |
+
|
| 357 |
+
span_font_goal = span_font # Capture the font at the first heading match
|
| 358 |
+
span_size_goal = span_size # Capture the size at the first heading match
|
| 359 |
+
zoom = 200
|
| 360 |
+
left = int(x0)
|
| 361 |
+
top = int(y0)
|
| 362 |
+
zoom_str = f"{zoom},{left},{top}"
|
| 363 |
+
pageNumberFound = page_num + 1
|
| 364 |
+
dictionaryNBS[heading_to_searchNBS] = [pageNumberFound, zoom_str]
|
| 365 |
+
|
| 366 |
+
annot = page.add_rect_annot(highlight_rect) # Create a rectangle annotation
|
| 367 |
+
annot.set_colors(stroke=(1, 0, 0)) # Set border color (Red)
|
| 368 |
+
annot.update() # Apply changes
|
| 369 |
+
groupmainheadingFromArray = [item for item in merged_groupheadings if previous_header in item]
|
| 370 |
+
|
| 371 |
+
# Build the query parameters
|
| 372 |
+
params = {
|
| 373 |
+
'pdfLink': link, # Your PDF link
|
| 374 |
+
'keyword': NBS_heading, # Your keyword (could be a string or list)
|
| 375 |
+
}
|
| 376 |
+
|
| 377 |
+
# URL encode each parameter
|
| 378 |
+
encoded_params = {key: urllib.parse.quote(value, safe='') for key, value in params.items()}
|
| 379 |
+
|
| 380 |
+
# Construct the final encoded link
|
| 381 |
+
encoded_link = '&'.join([f"{key}={value}" for key, value in encoded_params.items()])
|
| 382 |
+
|
| 383 |
+
# Correctly construct the final URL with page and zoom
|
| 384 |
+
final_url = f"{baselink}{encoded_link}#page={str(pageNumberFound)}&zoom={zoom_str}"
|
| 385 |
+
|
| 386 |
+
# Get current date and time
|
| 387 |
+
now = datetime.now()
|
| 388 |
+
|
| 389 |
+
# Format the output
|
| 390 |
+
formatted_time = now.strftime("%d/%m/%Y %I:%M:%S %p")
|
| 391 |
+
# Optionally, add the URL to a DataFrame
|
| 392 |
+
|
| 393 |
+
|
| 394 |
+
if len(groupmainheadingFromArray) > 0:
|
| 395 |
+
data_entry = {
|
| 396 |
+
"NBSLink": final_url,
|
| 397 |
+
"Subject": NBS_heading,
|
| 398 |
+
"Page": str(pageNumberFound),
|
| 399 |
+
"Author": "ADR",
|
| 400 |
+
"Creation Date": formatted_time,
|
| 401 |
+
"Layer": "Initial",
|
| 402 |
+
"Code": "to be added",
|
| 403 |
+
"head above 1": header2,
|
| 404 |
+
"head above 2": groupmainheadingFromArray[0]
|
| 405 |
+
}
|
| 406 |
+
data_list_JSON.append(data_entry)
|
| 407 |
+
|
| 408 |
+
# Convert list to JSON
|
| 409 |
+
json_output = json.dumps(data_list_JSON, indent=4)
|
| 410 |
+
|
| 411 |
+
print("Final URL:", final_url)
|
| 412 |
+
|
| 413 |
+
if collecting_text:
|
| 414 |
+
annot = page.add_rect_annot(highlight_rect) # Create a rectangle annotation
|
| 415 |
+
annot.set_colors(stroke=(1, 0, 0)) # Set border color (Red)
|
| 416 |
+
annot.update() # Apply changes
|
| 417 |
if current_line.strip():
|
| 418 |
all_text += current_line.strip() + '\n' # Append the current line
|
| 419 |
print(df)
|
| 420 |
print(dictionaryNBS)
|
| 421 |
+
# xx=save_df_to_pdf(df)
|
| 422 |
+
# outputpdfFitz =fitz.open('pdf',xx)
|
| 423 |
pdf_bytes = BytesIO()
|
| 424 |
pdf_document.save(pdf_bytes)
|
| 425 |
+
print('JSONN',json_output)
|
| 426 |
+
return pdf_bytes.getvalue(), pdf_document , df, json_output
|