Find_Hyperlinking_text.py

import fitz  # PyMuPDF
from io import BytesIO
import re
import requests
import pandas as pd
from collections import Counter
import fitz  # PyMuPDF
import re
import urllib.parse
import pandas as pd
import tempfile
from fpdf import FPDF


baselink='https://marthee-nbslink.hf.space/view-pdf?'
class PDF(FPDF):
    def header(self):
        self.set_font("Arial", "B", 12)
        self.cell(0, 10, "NBS Document Links", ln=True, align="C")
        self.ln(5)  # Space after header

def save_df_to_pdf(df):
    pdf = PDF()
    pdf.set_auto_page_break(auto=True, margin=15)

    # Set equal margins
    margin = 15
    pdf.set_left_margin(margin)
    pdf.set_right_margin(margin)

    pdf.add_page()
    pdf.set_font("Arial", size=10)

    # Set column widths and calculate total table width
    col_width = 50
    num_cols = 4
    table_width = col_width * num_cols

    # Get page width and calculate left alignment
    page_width = pdf.w
    start_x = (page_width - table_width) / 2  # Centering the table

    pdf.set_x(start_x)  # Move to calculated start position

    # Table headers
    pdf.set_fill_color(200, 200, 200)  # Light gray background
    pdf.set_font("Arial", "B", 10)
    headers = ["NBS Link", "NBS", "Head Above 1", "Head Above 2"]

    # Draw table headers
    for header in headers:
        pdf.cell(col_width, 8, header, border=1, fill=True, align="C")
    pdf.ln()

    pdf.set_font("Arial", size=9)

    # Add rows
    for _, row in df.iterrows():
        x_start = start_x  # Ensure every row starts at the same position
        y_start = pdf.get_y()

        # Calculate max height needed for this row
        text_lines = {col: pdf.multi_cell(col_width, 5, row[col], border=0, align="L", split_only=True) for col in ["NBS", "head above 1", "head above 2"]}
        max_lines = max(len(lines) for lines in text_lines.values())
        max_height = max_lines * 5

        pdf.set_x(x_start)  # Ensure correct alignment for each row

        # Clickable link cell (keeps same height as others)
        pdf.cell(col_width, max_height, "Click Here", border=1, link=row["NBSLink"], align="C")

        # Move to next column
        pdf.set_xy(x_start + col_width, y_start)

        # Draw each cell manually, ensuring equal height
        for i, col_name in enumerate(["NBS", "head above 1", "head above 2"]):
            x_col = x_start + col_width * (i + 1)
            y_col = y_start
            pdf.multi_cell(col_width, 5, row[col_name], border=0, align="L")  # Draw text
            pdf.rect(x_col, y_col, col_width, max_height)  # Draw border
            pdf.set_xy(x_col + col_width, y_start)  # Move to next column

        # Move to the next row
        pdf.ln(max_height)
        # Save PDF to memory instead of a file
    # pdf_output = BytesIO()
    # pdf_output = 'output.pdf'
    pdf_output = pdf.output(dest="S").encode("latin1")  # Returns the PDF as a byte string

    return pdf_output


    
def normalize_text(text):
    """Lowercase, remove extra spaces, and strip special characters."""
    text = text.lower().strip()
    text = re.sub(r'\s+', ' ', text)  # Normalize multiple spaces
    return re.sub(r'[^\w\s]', '', text)  # Remove punctuation
def get_repeated_texts(pdf_document, threshold=0.9):
    """
    Identify text that appears on most pages.
    :param pdf_document: The opened PDF document.
    :param threshold: The percentage of pages a text must appear on to be considered "repeated".
    """
    text_counts = Counter()
    total_pages = pdf_document.page_count

    for page_num in range(total_pages):
        page = pdf_document.load_page(page_num)
        page_text = page.get_text("text")
        normalized_lines = {normalize_text(line) for line in page_text.splitlines() if line.strip()}

        text_counts.update(normalized_lines)

    # Find texts that appear in at least `threshold * total_pages` pages
    min_occurrence = max(1, int(threshold * total_pages))
    repeated_texts = {text for text, count in text_counts.items() if count >= min_occurrence}
    return repeated_texts


def split_links(links_string):
    """Split a comma-separated string of links into an array of trimmed links."""
    return [link.strip() for link in links_string.split(',')]
def annotate_text_from_pdf(pdfshareablelinks, LISTheading_to_search):
    """
    Annotates text under a specific heading in a PDF, highlights it, 
    and constructs zoom coordinates for the first occurrence of the heading.
    Args:
        pdfshareablelinks (list): List of shareable links to PDFs.
        heading_to_search (str): The heading to search for in the PDF.
    Returns:
        Tuple: Annotated PDF bytes, count of heading occurrences, and zoom string.
    """
    print("Input links:", pdfshareablelinks)
    print(LISTheading_to_search)
    
    link = pdfshareablelinks[0]
    pdf_content = None
    headings_TOC = []
    # Modify Dropbox shareable link for direct download
    if link and ('http' in link or 'dropbox' in link):
        if 'dl=0' in link:
            link = link.replace('dl=0', 'dl=1')

    # Download the PDF content from the shareable link
    response = requests.get(link)
    pdf_content = BytesIO(response.content)  # Store the content in memory
    if pdf_content is None:
        raise ValueError("No valid PDF content found.")

    # Open the PDF using PyMuPDF
    pdf_document = fitz.open(stream=pdf_content, filetype="pdf")
    repeated_texts = get_repeated_texts(pdf_document)
    df = pd.DataFrame(columns=["NBSLink","NBS", 'head above 1', "head above 2"])
    dictionaryNBS={}
    for NBSindex, heading_to_search in enumerate(LISTheading_to_search):
        if NBSindex == len(LISTheading_to_search) - 1:
            flagAllNBSvisited = True
        all_text = []
        current_line = ""
        collecting_text = False
        f10_count = 0
        current_y = None
        highlight_rect = None
        zoom_str = None
        toc_flag = False
        span_font_goal = None
        span_size_goal = None
        pageNumberFound = None
        groupheadings = []
        merged_groupheadings = []
        collectheader2 = False
        header2 = ''
        header2_first_span_size = 0
        previous_header = ''
        next_span_text = ''
        current_line_span_size = 0
        flagAllNBSvisited = False
        
        text = ''
        heading_to_searchNBS = heading_to_search
        heading_words = heading_to_search.split()  # Split heading into words
        first_word = heading_words[0]  # First word to search for
        remaining_words = heading_words[1:]  # Remaining words to verify
        print(heading_words)
        heading_to_search = heading_to_search.replace(" ", "")

        # Process each page in the PDF
        for page_num in range(pdf_document.page_count):
            page = pdf_document.load_page(page_num)
            # Get page dimensions
            page_height = page.rect.height
            header_threshold = page_height * 0.1  # Top 10% of the page height
            footer_threshold = page_height * 0.9  # Bottom 10% of the page height

            # Extract text in dictionary format
            text_dict = page.get_text("dict")

            # Collect header y-coordinates to detect header area
            header_threshold = 0  # Header area: top 10% of the page height
            current_line_text = ""
            previous_y = None
            # Process text blocks
            for block in text_dict['blocks']:
                for line_index, line in enumerate(block.get('lines', [])):
                    spans = line.get('spans', [])
                    if spans and any(span['text'].strip() for span in spans):
                        for i, span in enumerate(spans):
                            span_text = span['text'].strip()
                            highlight_rect = span['bbox']
                            span_y = span['bbox'][1]
                            span_font = span['font']
                            span_size = span['size']

                            if previous_y is None:
                                previous_y = span_y  # Initialize on first span

                            # If same Y coordinate as previous, append to the current line
                            if abs(span_y - previous_y) < 5:  # Allow a small margin for OCR variations
                                current_line_text += " " + span_text
                                current_line_text = normalize_text(current_line_text)
                                current_line_span_size = span_size
                            else:
                                # Store the complete line and reset for the new line
                                if current_line_text.strip():
                                    all_text.append(current_line_text.strip())

                                current_line_text = span_text  # Start a new line
                                previous_y = span_y  # Update the reference Y
                            text = span_text
                            if collecting_text and span_font == span_font_goal and span_size == span_size_goal and span_text[0].isdigit():
                                print(f"Ending collection at heading: {span_text}")
                                print("merged_groupheadings:", merged_groupheadings)
                                print('groupheadingss',groupheadings)
                                collecting_text = False
                                continue
                            if collecting_text:
                                annot = page.add_highlight_annot(highlight_rect)
                                annot.update()

                            if 'Content' in span_text:
                                toc_flag = True
                                TOC_start = span_text
                                print('content', TOC_start, span_size)

                            if toc_flag:
                                if 'Content' not in span_text:
                                    if current_y is None:
                                        current_y = span_y
                                        current_size = span_size  # Initialize the reference span size
                                    # Check if the current span size deviates significantly
                                    if abs(span_size - current_size) > 1:  # Threshold for size difference
                                        toc_flag = False
                                 
                                    if abs(current_y - span_y) < 5:  # Allowing more flexibility for multi-line headings
                                        current_line += " " + span_text  # Keep accumulating text
                                    else:
                                        if current_line.strip():  # Only process non-empty lines
                                            print('current_line',current_line)
                                            pattern = r"^([A-Za-z0-9\s\/\-,]+)(?=\.+)"
                                            match = re.match(pattern, current_line.strip())
                                            
                                            if match:
                                                groupheadings.append(match.group(1).strip())
                                        # else:
                                        #     toc_flag = False

                                        current_line = span_text
                                        current_y = span_y
                                        current_size = span_size  # Update reference span size
                            # print('outofcurrent')
                            if len(groupheadings) > 0:
                                pattern = re.compile(r"^[A-Za-z]\d{2} ")  # Match headings starting with letter + 2 digits
                                merged_groupheadings = []
                                current_item = None  # Start as None to avoid an initial blank entry

                                for item in groupheadings:
                                    if pattern.match(item):  # If item starts with correct pattern, it's a new heading
                                        if current_item:  # Append only if current_item is not empty
                                            merged_groupheadings.append(current_item.strip())
                                        current_item = item  # Start new heading
                                    else:
                                        if current_item:  
                                            current_item += " " + item  # Merge with previous heading

                                # Append last merged item after loop
                                if current_item:
                                    merged_groupheadings.append(current_item.strip())
                            if span_text == first_word:
                                print('First word found:', span_text)
                                # Check if it's not the last span in the current line
                                print(i + 1, len(spans))
                                if i + 1 < len(spans):
                                    next_span_text = (spans[i + 1]['text'].strip())
                                # Check if the next span's text is in the heading list
                                if next_span_text.replace(" ", "") in heading_to_search.replace(" ", ""):
                                    text = (span_text + ' ' + next_span_text)
                                            # After processing the current line, check if there's a next line
                            if first_word == span_text:
                                if line_index + 1 < len(block.get('lines', [])):
                                    next_line = block['lines'][line_index + 1]
                                    # You can process the spans of the next line here
                                    for next_span in next_line.get('spans', []):
                                        next_span_text = next_span['text'].strip()
                                        text = span_text + ' ' + next_span_text
                            if len(merged_groupheadings) > 0:
                                if re.match(r"[A-Za-z]\d{2}", span_text) and span_size > 10:
                                    previous_header = span_text  # Store last detected header
                                    print('previous_header', span_text)
                                groupmainheadingFromArray = [item for item in merged_groupheadings if previous_header in item]

                                if previous_header:
                                    if not collectheader2:
                                        if header2_first_span_size == 0:
                                            spanSizeHeader = 10
                                        else:
                                            spanSizeHeader = header2_first_span_size

                                        for item in groupmainheadingFromArray:
                                            if not any(normalize_text(current_line_text) in normalize_text(item) for item in groupmainheadingFromArray):
                                                if span_size >= spanSizeHeader:
                                                    if not re.match(r"^\d{2}", current_line_text) and current_line_text not in repeated_texts and "Bold" in span["font"] :
                                                        if len(header2) > 0:
                                                            header2_first_span_size = span_size
                                                        header2 = current_line_text
                                                        print('header2', header2, span_size, spanSizeHeader)

                            trimmed_text = text.replace(" ", "")
                            if len(text) > 0:
                                if text.split()[0] in heading_words:
                                    if len(trimmed_text) > 0 and (heading_to_search.replace(" ", "") in trimmed_text):
                                        print(trimmed_text, heading_to_search)
                                        f10_count += 1
                                        # Start collecting text under the second occurrence of the heading
                                        if f10_count == 1:
                                            collecting_text = True
                                            print(f"Starting collection under heading: {text}, {span_font}, {span_size}")
                                            collectheader2 = True
                                            NBS_heading = heading_to_searchNBS
                                            x0, y0, x1, y1 = highlight_rect
                                            
                                            span_font_goal = span_font  # Capture the font at the first heading match
                                            span_size_goal = span_size  # Capture the size at the first heading match
                                            zoom = 200
                                            left = int(x0)
                                            top = int(y0)
                                            zoom_str = f"{zoom},{left},{top}"
                                            pageNumberFound = page_num + 1
                                            dictionaryNBS[heading_to_searchNBS] = [pageNumberFound, zoom_str]

                                            annot = page.add_highlight_annot(highlight_rect)
                                            annot.update()
                                            groupmainheadingFromArray = [item for item in merged_groupheadings if previous_header in item]
                                            
                                                        # Build the query parameters
                                            params = {
                                                'pdfLink': link,  # Your PDF link
                                                'keyword': NBS_heading,  # Your keyword (could be a string or list)                                              
                                            }
                                            
                                            # URL encode each parameter
                                            encoded_params = {key: urllib.parse.quote(value, safe='') for key, value in params.items()}
                                            
                                            # Construct the final encoded link
                                            encoded_link = '&'.join([f"{key}={value}" for key, value in encoded_params.items()])
                                            
                                            # Correctly construct the final URL with page and zoom
                                            final_url = f"{baselink}{encoded_link}#page={str(pageNumberFound)}&zoom={zoom_str}"
                                            
                                            # Optionally, add the URL to a DataFrame
                                            if len(groupmainheadingFromArray) > 0:
                                                df = pd.concat([df, pd.DataFrame([{
                                                    "NBSLink": final_url,
                                                    "NBS": NBS_heading,
                                                    'head above 1': header2,
                                                    "head above 2": groupmainheadingFromArray[0]
                                                }])], ignore_index=True)
                                            
                                            print("Final URL:", final_url)

                            if collecting_text:
                                annot = page.add_highlight_annot(highlight_rect)
                                annot.update()
                if current_line.strip():
                    all_text += current_line.strip() + '\n'  # Append the current line
    print(df)
    print(dictionaryNBS)
    xx=save_df_to_pdf(df)
    outputpdfFitz =fitz.open('pdf',xx)
    pdf_bytes = BytesIO()
    pdf_document.save(pdf_bytes)
    return pdf_bytes.getvalue(), pdf_document , df,outputpdfFitz