Update InitialMarkups.py

InitialMarkups.py

@@ -2417,3 +2417,468 @@ def extract_section_under_header_tobebilled2marthe(multiplePDF_Paths):
     return pdf_bytes.getvalue(), docHighlights , json_output, Alltexttobebilled , filenames
 
 
+
+def extract_section_under_header_tobebilledMultiplePDFSmarthe(multiplePDF_Paths):
+    # keywordstoSkip=["installation", "execution", "miscellaneous items", "workmanship", "testing", "labeling"]
+    filenames=[]
+    keywords = {'installation', 'execution', 'miscellaneous items', 'workmanship', 'testing', 'labeling'}
+    top_margin = 70
+    bottom_margin = 50
+    arrayofPDFS=multiplePDF_Paths.split(',')
+    print(multiplePDF_Paths)
+    print(arrayofPDFS)
+    df = pd.DataFrame(columns=["PDF Name","NBSLink","Subject","Page","Author","Creation Date","Layer",'Code', 'head above 1', "head above 2","BodyText"])
+    for pdf_path in arrayofPDFS:
+        headertoContinue1 = False
+        headertoContinue2=False
+        Alltexttobebilled=''
+        parsed_url = urlparse(pdf_path)
+        filename = os.path.basename(parsed_url.path)
+        filename = unquote(filename)  # decode URL-encoded characters
+        filenames.append(filename)
+        # 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
+        )
+    
+        hierarchy = build_header_hierarchy(doc, toc_pages, most_common_font_size, most_common_color, most_common_font)
+        listofHeaderstoMarkup = get_leaf_headers_with_paths(hierarchy)
+        
+        # Precompute all children headers once
+        allchildrenheaders = [normalize_text(item['text']) for item, p in listofHeaderstoMarkup]
+        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","BodyText"])
+        dictionaryNBS={}
+        data_list_JSON = []
+        currentgroupname=''
+        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]
+    
+        
+    
+        # 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]
+    
+        for heading_to_searchDict, paths in listofHeaderstoMarkup:
+            heading_to_search = heading_to_searchDict['text']
+            heading_to_searchPageNum = heading_to_searchDict['page']
+    
+            # Initialize variables
+            headertoContinue1 = False
+            headertoContinue2 = False
+            matched_header_line = None
+            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)
+            paths_norm = [normalize_text(p) for p in paths[0]] if paths and paths[0] else []
+            for page_num in range(heading_to_searchPageNum,len(doc)):
+                # print(heading_to_search)
+                if paths[0].strip().lower() != currentgroupname.strip().lower():
+                    Alltexttobebilled+= paths[0] +'\n'
+                    currentgroupname=paths[0]
+                    # print(paths[0])
+    
+                    
+                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
+    
+                        # Check if we should continue processing
+                        if combined_line_norm and combined_line_norm in paths[0]:
+                            
+                            headertoContinue1 = combined_line_norm
+                        if combined_line_norm and combined_line_norm in paths[-2]:
+                        
+                            headertoContinue2 = combined_line_norm
+                        # if  'installation' in paths[-2].lower() or 'execution' in paths[-2].lower() or 'miscellaneous items' in paths[-2].lower() :
+                        last_path = paths[-2].lower()
+                        # if any(word in paths[-2].lower() for word in keywordstoSkip):
+                        # if  'installation' in paths[-2].lower() or 'execution' in paths[-2].lower() or 'miscellaneous items' in paths[-2].lower() or 'workmanship' in paths[-2].lower() or 'testing' in paths[-2].lower() or 'labeling' in paths[-2].lower():
+                        if any(keyword in last_path for keyword in keywords):
+                          stringtowrite='Not to be billed'
+                        else:
+                          stringtowrite='To be billed'
+                        if stringtowrite=='To be billed':
+                            # Alltexttobebilled+= combined_line_norm  #################################################
+                            if matched_header_line_norm in combined_line_norm:
+                                Alltexttobebilled+='\n'
+                            Alltexttobebilled+= ' '+combined_line_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
+                        )
+                        # substring_match = (
+                        #     heading_norm in combined_line_norm or
+                        #     combined_line_norm in heading_norm
+                        # )
+    
+                        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 and stringtowrite.startswith('To'):
+                                collecting = True
+                                # if stringtowrite=='To be billed':
+                                #     Alltexttobebilled+='\n'
+                                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"{baselink}{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
+    
+    
+                                    data_entry = {
+                                            "PDF Name":filename,
+                                            "NBSLink": final_url,
+                                            "Subject": heading_to_search,
+                                            "Page": str(pageNumberFound),
+                                            "Author": "ADR",
+                                            "Creation Date": formatted_time,
+                                            "Layer": "Initial",
+                                            "Code": stringtowrite,
+                                            "head above 1":  paths[-2],
+                                            "head above 2":  paths[0],
+                                            "BodyText":collected_lines,
+                                            "MC Connnection": 'Go to ' +  paths[0].strip().split()[0] +'/'+ heading_to_search.strip().split()[0] + ' in '+ filename
+                                        }
+                                    data_list_JSON.append(data_entry)
+    
+                                    # Convert list to JSON
+                                    json_output = json.dumps(data_list_JSON, indent=4)
+    
+                                    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) ) and stringtowrite.startswith('To'):
+                                    collecting = True
+                                    if stringtowrite=='To be billed':
+                                        Alltexttobebilled+='\n'
+                                    # if stringtowrite=='To be billed':
+                                    #     Alltexttobebilled+= ' '+ combined_line_norm
+                                    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"{baselink}{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
+    
+    
+                                        data_entry = {
+                                                "PDF Name":filename,
+                                                "NBSLink": final_url,
+                                                "Subject": heading_to_search,
+                                                "Page": str(pageNumberFound),
+                                                "Author": "ADR",
+                                                "Creation Date": formatted_time,
+                                                "Layer": "Initial",
+                                                "Code": stringtowrite,
+                                                "head above 1":  paths[-2],
+                                                "head above 2":  paths[0],
+                                                "BodyText":collected_lines,
+                                                "MC Connnection": 'Go to ' +  paths[0].strip().split()[0] +'/'+ heading_to_search.strip().split()[0] + ' in '+ filename
+                                            }
+                                        data_list_JSON.append(data_entry)
+    
+                                        # Convert list to JSON
+                                        json_output = json.dumps(data_list_JSON, indent=4)
+    
+                    
+                                        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:
+                                      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
+                                      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 paths[-2].lower() or 'execution' in paths[-2].lower() or 'miscellaneous items' in paths[-2].lower() :
+                    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)
+    
+    dbxTeam = tsadropboxretrieval.ADR_Access_DropboxTeam('user')
+    metadata = dbxTeam.sharing_get_shared_link_metadata(pdf_path)
+    dbPath = '/TSA JOBS/ADR Test/FIND/'
+    pdf_bytes = BytesIO()
+    docHighlights.save(pdf_bytes)
+    pdflink = tsadropboxretrieval.uploadanyFile(doc=docHighlights, path=dbPath, pdfname=filename)
+    json_output=changepdflinks(json_output,pdflink)
+    return pdf_bytes.getvalue(), docHighlights , json_output, Alltexttobebilled , filenames
+