Update InitialMarkups.py

InitialMarkups.py

@@ -1468,7 +1468,7 @@ def extract_section_under_header_tobebilledOnly(pdf_path):
 
     pdf_bytes = BytesIO()
     docHighlights.save(pdf_bytes)
-    return pdf_bytes.getvalue(), docHighlights , json_output
+    return pdf_bytes.getvalue(), docHighlights , json_output , Alltexttobebilled
 
 
 
@@ -1852,389 +1852,3 @@ def extract_section_under_headerRawan(pdf_path,headingjson,pagenum=0,incominghea
 
 
 
-
-########################################################################################################################################################
-########################################################################################################################################################
-
-def extract_section_under_headerRawan(pdf_path,headingjson,pagenum=0,incomingheader=0):
-    top_margin = 70
-    bottom_margin = 50
-    # Optimized URL handling
-    if pdf_path and ('http' in pdf_path or 'dropbox' in pdf_path):
-        pdf_path = pdf_path.replace('dl=0', 'dl=1')
-
-    # Cache frequently used values
-    response = requests.get(pdf_path)
-    pdf_content = BytesIO(response.content)
-    if not pdf_content:
-        raise ValueError("No valid PDF content found.")
-
-    doc = fitz.open(stream=pdf_content, filetype="pdf")
-    docHighlights = fitz.open(stream=pdf_content, filetype="pdf")
-    most_common_font_size, most_common_color, most_common_font = get_regular_font_size_and_color(doc)
-
-    # Precompute regex patterns
-    dot_pattern = re.compile(r'\.{3,}')
-    url_pattern = re.compile(r'https?://\S+|www\.\S+')
-
-    def get_toc_page_numbers(doc, max_pages_to_check=15):
-        toc_pages = []
-        for page_num in range(min(len(doc), max_pages_to_check)):
-            page = doc.load_page(page_num)
-            blocks = page.get_text("dict")["blocks"]
-
-            dot_line_count = 0
-            for block in blocks:
-                for line in block.get("lines", []):
-                    line_text = get_spaced_text_from_spans(line["spans"]).strip()
-                    if dot_pattern.search(line_text):
-                        dot_line_count += 1
-
-            if dot_line_count >= 3:
-                toc_pages.append(page_num)
-
-        return list(range(0, toc_pages[-1] +1)) if toc_pages else toc_pages
-
-    toc_pages = get_toc_page_numbers(doc)
-
-    headers, top_3_font_sizes, smallest_font_size, headersSpans = extract_headers(
-        doc, toc_pages, most_common_font_size, most_common_color, most_common_font, top_margin, bottom_margin
-    )
-    
-    listofheadingsfromrawan=[]
-    if type(headingjson) == str:
-        listofheadingsfromrawan.append(headingjson)
-        headingjson=[headingjson]
-    else:
-        for item in headingjson:
-            listofheadingsfromrawan.append(normalize_text(item['Subject']))
-    # print('hereeeeeeeeeeeeeee0',listofheadingsfromrawan)
-    # Precompute all children headers once
-    allchildrenheaders = listofheadingsfromrawan
-    # print('hereeeeeeeeeeeeeee00',allchildrenheaders)
-    allchildrenheaders_set = set(allchildrenheaders)  # For faster lookups
-    
-    df = pd.DataFrame(columns=["NBSLink","Subject","Page","Author","Creation Date","Layer",'Code', 'head above 1', "head above 2"])
-    data_list_JSON = []
-
-    if len(top_3_font_sizes)==3:
-        mainHeaderFontSize, subHeaderFontSize, subsubheaderFontSize = top_3_font_sizes
-    elif len(top_3_font_sizes)==2:
-        mainHeaderFontSize= top_3_font_sizes[0]
-        subHeaderFontSize= top_3_font_sizes[1]
-        subsubheaderFontSize= top_3_font_sizes[1]
-
-    # print("📌 Has TOC:", bool(toc_pages), " | Pages to skip:", toc_pages)
-
-    # Preload all pages to avoid repeated loading
-    # pages = [doc.load_page(page_num) for page_num in range(len(doc)) if page_num not in toc_pages]
-    newjsonList=[]
-    for heading_to_searchDict in headingjson:
-        if type(heading_to_searchDict) == str:
-            heading_to_search = heading_to_searchDict
-            heading_to_searchPageNum = pagenum
-        else:
-            heading_to_search = heading_to_searchDict['Subject']
-            heading_to_searchPageNum = int(heading_to_searchDict['Page'])-1
-            incomingheader = heading_to_searchDict['head above 1']
-            
-        # print('hereeeeeeeeeeeeeee0',heading_to_searchPageNum)
-        done = False
-        collecting = False
-        collected_lines = []
-        page_highlights = {}
-        current_bbox = {}
-        last_y1s = {}
-        mainHeader = ''
-        subHeader = ''
-        matched_header_line_norm = heading_to_search
-        break_collecting = False
-        heading_norm = normalize_text(heading_to_search)
-
-        for page_num in range(heading_to_searchPageNum,len(doc)):
-            
-            if page_num in toc_pages:
-              continue
-            if break_collecting:
-                break
-            page=doc[page_num]
-            page_height = page.rect.height
-            blocks = page.get_text("dict")["blocks"]
-
-            for block in blocks:
-                if break_collecting:
-                    break
-
-                lines = block.get("lines", [])
-                i = 0
-                while i < len(lines):
-                    if break_collecting:
-                        break
-
-                    spans = lines[i].get("spans", [])
-                    if not spans:
-                        i += 1
-                        continue
-
-                    y0 = spans[0]["bbox"][1]
-                    y1 = spans[0]["bbox"][3]
-                    if y0 < top_margin or y1 > (page_height - bottom_margin):
-                        i += 1
-                        continue
-
-                    line_text = get_spaced_text_from_spans(spans).lower()
-                    line_text_norm = normalize_text(line_text)
-
-                    # Combine with next line if available
-                    if i + 1 < len(lines):
-                        next_spans = lines[i + 1].get("spans", [])
-                        next_line_text = get_spaced_text_from_spans(next_spans).lower()
-                        combined_line_norm = normalize_text(line_text + " " + next_line_text)
-                    else:
-                        combined_line_norm = line_text_norm
-                    # Optimized header matching
-                    existsfull = (
-                        ( combined_line_norm in allchildrenheaders_set or
-                        combined_line_norm in allchildrenheaders ) and heading_to_search in combined_line_norm
-                    )
-
-                    # New word-based matching
-                    current_line_words = set(combined_line_norm.split())
-                    heading_words = set(heading_norm.split())
-                    all_words_match = current_line_words.issubset(heading_words) and len(current_line_words) > 0
-
-                    substring_match = (
-                        heading_norm in combined_line_norm or
-                        combined_line_norm in heading_norm or
-                        all_words_match  # Include the new word-based matching
-                    )
-
-                    if (substring_match and existsfull and not collecting and
-                        len(combined_line_norm) > 0 ):#and (headertoContinue1 or headertoContinue2) ):
-
-                        # Check header conditions more efficiently
-                        header_spans = [
-                            span for span in spans
-                            if (is_header(span, most_common_font_size, most_common_color, most_common_font)
-                                # and span['size'] >= subsubheaderFontSize
-                                and span['size'] < mainHeaderFontSize)
-                        ]
-                        if header_spans:
-                            collecting = True
-                            matched_header_font_size = max(span["size"] for span in header_spans)
-                            # print(f"📥 Start collecting after header: {combined_line_norm} (Font size: {matched_header_font_size})")
-
-                            collected_lines.append(line_text)
-                            valid_spans = [span for span in spans if span.get("bbox")]
-
-                            if valid_spans:
-                                x0s = [span["bbox"][0] for span in valid_spans]
-                                x1s = [span["bbox"][2] for span in valid_spans]
-                                y0s = [span["bbox"][1] for span in valid_spans]
-                                y1s = [span["bbox"][3] for span in valid_spans]
-
-                                header_bbox = [min(x0s), min(y0s), max(x1s), max(y1s)]
-
-                                if page_num in current_bbox:
-                                    cb = current_bbox[page_num]
-                                    current_bbox[page_num] = [
-                                        min(cb[0], header_bbox[0]),
-                                        min(cb[1], header_bbox[1]),
-                                        max(cb[2], header_bbox[2]),
-                                        max(cb[3], header_bbox[3])
-                                    ]
-                                else:
-                                    current_bbox[page_num] = header_bbox
-                                last_y1s[page_num] = header_bbox[3]
-                                x0, y0, x1, y1 = header_bbox
-
-                                zoom = 200
-                                left = int(x0)
-                                top = int(y0)
-                                zoom_str = f"{zoom},{left},{top}"
-                                pageNumberFound = page_num + 1
-
-                              # Build the query parameters
-                                params = {
-                                    'pdfLink': pdf_path,  # Your PDF link
-                                    'keyword': heading_to_search,  # 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"{newlink}{encoded_link}#page={str(pageNumberFound)}&zoom={zoom_str}"
-
-                                # Get current date and time
-                                now = datetime.now()
-
-                                # Format the output
-                                formatted_time = now.strftime("%d/%m/%Y %I:%M:%S %p")
-                                # Optionally, add the URL to a DataFrame
-                                new_url= final_url
-                                if type(heading_to_searchDict) != str:
-                                    heading_to_searchDict['NBSLink']=new_url
-                                newjsonList.append(heading_to_searchDict)
-                                # print("Final URL:", final_url)
-                                i += 2
-                                continue
-                    else:
-                        if (substring_match and not collecting and
-                            len(combined_line_norm) > 0): # and (headertoContinue1 or headertoContinue2) ):
-
-                            # Calculate word match percentage
-                            word_match_percent = words_match_ratio(heading_norm, combined_line_norm) * 100
-
-                            # Check if at least 70% of header words exist in this line
-                            meets_word_threshold = word_match_percent >= 100
-
-                            # Check header conditions (including word threshold)
-                            header_spans = [
-                                span for span in spans
-                                if (is_header(span, most_common_font_size, most_common_color, most_common_font)
-                                    # and span['size'] >= subsubheaderFontSize
-                                    and span['size'] < mainHeaderFontSize)
-                            ]
-
-                            if header_spans and (meets_word_threshold or same_start_word(heading_to_search, combined_line_norm) ):
-                                collecting = True
-                                matched_header_font_size = max(span["size"] for span in header_spans)
-                              
-                                collected_lines.append(line_text)
-                                valid_spans = [span for span in spans if span.get("bbox")]
-
-                                if valid_spans:
-                                    x0s = [span["bbox"][0] for span in valid_spans]
-                                    x1s = [span["bbox"][2] for span in valid_spans]
-                                    y0s = [span["bbox"][1] for span in valid_spans]
-                                    y1s = [span["bbox"][3] for span in valid_spans]
-
-                                    header_bbox = [min(x0s), min(y0s), max(x1s), max(y1s)]
-
-                                    if page_num in current_bbox:
-                                        cb = current_bbox[page_num]
-                                        current_bbox[page_num] = [
-                                            min(cb[0], header_bbox[0]),
-                                            min(cb[1], header_bbox[1]),
-                                            max(cb[2], header_bbox[2]),
-                                            max(cb[3], header_bbox[3])
-                                        ]
-                                    else:
-                                        current_bbox[page_num] = header_bbox
-
-                                    last_y1s[page_num] = header_bbox[3]
-                                    x0, y0, x1, y1 = header_bbox
-                                    zoom = 200
-                                    left = int(x0)
-                                    top = int(y0)
-                                    zoom_str = f"{zoom},{left},{top}"
-                                    pageNumberFound = page_num + 1
-
-                                  # Build the query parameters
-                                    params = {
-                                        'pdfLink': pdf_path,  # Your PDF link
-                                        'keyword': heading_to_search,  # 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"{newlink}{encoded_link}#page={str(pageNumberFound)}&zoom={zoom_str}"
-                                    new_url= final_url
-                                    if type(heading_to_searchDict) != str:
-                                        heading_to_searchDict['NBSLink']=new_url
-                                    newjsonList.append(heading_to_searchDict)                                
-                                    i += 2
-                                    continue
-                    if collecting:
-                        norm_line = normalize_text(line_text)
-
-                        # Optimized URL check
-                        if url_pattern.match(norm_line):
-                            line_is_header = False
-                        else:
-                            line_is_header = any(is_header(span, most_common_font_size, most_common_color, most_common_font) for span in spans)
-
-                        if line_is_header:
-                            header_font_size = max(span["size"] for span in spans)
-                            is_probably_real_header = (
-                                header_font_size >= matched_header_font_size and
-                                is_header(spans[0], most_common_font_size, most_common_color, most_common_font) and
-                                len(line_text.strip()) > 2
-                            )
-
-                            if (norm_line != matched_header_line_norm and
-                                norm_line != heading_norm and
-                                is_probably_real_header):
-                                if line_text not in heading_norm:
-                                #   print(f"🛑 Stop at header with same or larger font: '{line_text}' ({header_font_size} ≥ {matched_header_font_size})")
-                                  collecting = False
-                                  done = True
-                                  headertoContinue1 = False
-                                  headertoContinue2=False
-                                  for page_num, bbox in current_bbox.items():
-                                      bbox[3] = last_y1s.get(page_num, bbox[3])
-                                      page_highlights[page_num] = bbox
-                                  
-                                  if  'installation' in incomingheader or 'execution' in incomingheader or 'miscellaneous items' in incomingheader :
-                                    stringtowrite='Not to be billed'
-                                  else:
-                                    stringtowrite='To be billed'
-                                  highlight_boxes(docHighlights, page_highlights,stringtowrite)
-
-                                  break_collecting = True
-                                  break
-
-                        if break_collecting:
-                            break
-
-                        collected_lines.append(line_text)
-                        valid_spans = [span for span in spans if span.get("bbox")]
-                        if valid_spans:
-                            x0s = [span["bbox"][0] for span in valid_spans]
-                            x1s = [span["bbox"][2] for span in valid_spans]
-                            y0s = [span["bbox"][1] for span in valid_spans]
-                            y1s = [span["bbox"][3] for span in valid_spans]
-
-                            line_bbox = [min(x0s), min(y0s), max(x1s), max(y1s)]
-
-                            if page_num in current_bbox:
-                                cb = current_bbox[page_num]
-                                current_bbox[page_num] = [
-                                    min(cb[0], line_bbox[0]),
-                                    min(cb[1], line_bbox[1]),
-                                    max(cb[2], line_bbox[2]),
-                                    max(cb[3], line_bbox[3])
-                                ]
-                            else:
-                                current_bbox[page_num] = line_bbox
-
-                            last_y1s[page_num] = line_bbox[3]
-                    i += 1
-
-        if not done:
-            for page_num, bbox in current_bbox.items():
-                bbox[3] = last_y1s.get(page_num, bbox[3])
-                page_highlights[page_num] = bbox
-            if  'installation' in incomingheader or 'execution' in incomingheader or 'miscellaneous items' in incomingheader :
-                stringtowrite='Not to be billed'
-            else:
-                stringtowrite='To be billed'            
-            highlight_boxes(docHighlights, page_highlights,stringtowrite)
-
-    # docHighlights.save("highlighted_output.pdf", garbage=4, deflate=True)
-
-    pdf_bytes = BytesIO()
-    docHighlights.save(pdf_bytes)
-    return pdf_bytes.getvalue(), docHighlights , newjsonList
-
- 
-