import os import requests from bs4 import BeautifulSoup from urllib.parse import urljoin import pandas as pd import numpy as np import zipfile import textract import gradio as gr import shutil def browse_folder(url): if url.lower().endswith(('docs', 'docs/')): return gr.update(choices=[]) response = requests.get(url) response.raise_for_status() # This will raise an exception if there's an error soup = BeautifulSoup(response.text, 'html.parser') excel_links = [a['href'] + '/' for a in soup.find_all('a', href=True) if a['href'].startswith(url)] return gr.update(choices=excel_links) def extract_statuses(url): # Send a GET request to the webpage response = requests.get(url) # Parse the webpage content soup = BeautifulSoup(response.content, 'html.parser') # Find all links in the webpage links = soup.find_all('a') # Identify and download the Excel file for link in links: href = link.get('href') if href and (href.endswith('.xls') or href.endswith('.xlsx')): excel_url = href if href.startswith('http') else url + href excel_response = requests.get(excel_url) file_name = 'guide_status.xlsx' #excel_url.split('/')[-1] # Save the file with open(file_name, 'wb') as f: f.write(excel_response.content) # Read the Excel file df = pd.read_excel(file_name) # Check if 'TDoc Status' column exists and extract unique statuses if 'TDoc Status' in df.columns: unique_statuses = df['TDoc Status'].unique().tolist() print(f'Downloaded {file_name} and extracted statuses: {unique_statuses}') if 'withdrawn' in unique_statuses: unique_statuses.remove('withdrawn') return gr.update(choices=unique_statuses, value=unique_statuses) else: print(f"'TDoc Status' column not found in {file_name}") return [] import os import requests from bs4 import BeautifulSoup import pandas as pd import gradio as gr def scrape(url, excel_file, folder_name, status_list, progress=gr.Progress()): filenames = [] status_filenames = [] df = pd.DataFrame() # Initialize df to ensure it's always defined # Try to process the Excel file if provided and valid if excel_file and os.path.exists(excel_file): try: df = pd.read_excel(excel_file) print(f"Initial DataFrame size: {len(df)}") if 'TDoc Status' in df.columns and status_list: df = df[df['TDoc Status'].isin(status_list)] print(f"Filtered DataFrame size: {len(df)}") if not df.empty: if 'TDoc' in df.columns and not df['TDoc'].isnull().all(): status_filenames = [f"{url}{row['TDoc']}.zip" for index, row in df.iterrows()] elif 'URL' in df.columns and not df['URL'].isnull().all(): status_filenames = df['URL'].tolist() print(f"Filenames from Excel: {status_filenames}") except Exception as e: print(f"Error reading Excel file: {e}") # If no valid Excel file is given or no status_filenames are found, download zip files directly from the URL if not excel_file or not status_filenames: print("Downloading zip files directly from the URL...") response = requests.get(url) soup = BeautifulSoup(response.content, 'html.parser') zip_links = [a['href'] for a in soup.find_all('a', href=True) if a['href'].endswith('.zip')] # Construct absolute URLs for zip files status_filenames = [url + link if not link.startswith('http') else link for link in zip_links] print(f"Filenames from URL: {status_filenames}") download_directory = folder_name if not os.path.exists(download_directory): os.makedirs(download_directory) pourcentss = 0.05 # Proceed with downloading files for file_url in status_filenames: filename = os.path.basename(file_url) save_path = os.path.join(download_directory, filename) progress(pourcentss, desc='Downloading') pourcentss += 0.4 / max(len(status_filenames), 1) # Ensure non-zero division try: with requests.get(file_url, stream=True) as r: r.raise_for_status() with open(save_path, 'wb') as f: for chunk in r.iter_content(chunk_size=8192): f.write(chunk) except requests.exceptions.HTTPError as e: print(f"HTTP error occurred while downloading {file_url}: {e}") return True, len(status_filenames) def extractZip(url): # Répertoire où les fichiers zip sont déjà téléchargés nom_extract = url.split("/")[-3] + "_extraction" if os.path.exists(nom_extract): shutil.rmtree(nom_extract) extract_directory = nom_extract download_directory = url.split("/")[-3] + "_downloads" # Répertoire où le contenu des fichiers zip sera extrait # Extraire le contenu de tous les fichiers zip dans le répertoire de téléchargement for zip_file in os.listdir(download_directory): zip_path = os.path.join(download_directory, zip_file) # Vérifier si le fichier est un fichier zip if zip_file.endswith(".zip"): extract_dir = os.path.join(extract_directory, os.path.splitext(zip_file)[0]) # Supprimer l'extension .zip # Vérifier si le fichier zip existe if os.path.exists(zip_path): # Créer un répertoire pour extraire le contenu s'il n'existe pas if not os.path.exists(extract_dir): os.makedirs(extract_dir) # Extraire le contenu du fichier zip with zipfile.ZipFile(zip_path, 'r') as zip_ref: zip_ref.extractall(extract_dir) print(f"Extraction terminée pour {zip_file}") else: print(f"Fichier zip {zip_file} introuvable") print("Toutes les extractions sont terminées !") def excel3gpp(url): response = requests.get(url) response.raise_for_status() # This will raise an exception if there's an error # Use BeautifulSoup to parse the HTML content soup = BeautifulSoup(response.text, 'html.parser') # Look for Excel file links; assuming they have .xlsx or .xls extensions excel_links = [a['href'] for a in soup.find_all('a', href=True) if a['href'].endswith(('.xlsx', '.xls'))] # Download the first Excel file found (if any) if excel_links: excel_url = excel_links[0] # Assuming you want the first Excel file if not excel_url.startswith('http'): excel_url = os.path.join(url, excel_url) # Handle relative URLs # Download the Excel file excel_response = requests.get(excel_url) excel_response.raise_for_status() # Define the path where you want to save the file # Replace 'path_to_save_directory' with your desired path # Write the content of the Excel file to a local file # Write the content of the Excel file to a local file named 'guide.xlsx' nom_guide = 'guide.xlsx' # Directly specify the filename if os.path.exists(nom_guide): os.remove(nom_guide) filepath = nom_guide with open(filepath, 'wb') as f: f.write(excel_response.content) print(f'Excel file downloaded and saved as: {filepath}') def replace_line_breaks(text): return text.replace("\n", "/n") def remod_text(text): return text.replace("/n", "\n") def update_excel(data, excel_file, url): new_df_columns = ["URL", "File", "Type", "Title", "Source", "Status", "Content"] temp_df = pd.DataFrame(data, columns=new_df_columns) try: # Check if the Excel file already exists and append data to it if os.path.exists(excel_file): old_df = pd.read_excel(excel_file) df = pd.concat([old_df, temp_df], axis=0, ignore_index=True) else: df = temp_df # Save the updated data back to the Excel file df.to_excel(excel_file, index=False) except Exception as e: print(f"Error updating Excel file: {e}") def extractionPrincipale(url, excel_file=None, status_list=None, progress=gr.Progress()): nom_download = url.split("/")[-3] + "_downloads" if os.path.exists(nom_download): shutil.rmtree(nom_download) folder_name = nom_download nom_status = url.split("/")[-3] + "_status.xlsx" if os.path.exists(nom_status): os.remove(nom_status) temp_excel = nom_status progress(0.0,desc='Downloading') result, count = scrape(url, excel_file, folder_name, status_list) if result: print("Success") else: return(None) progress(0.4,desc='Extraction') extractZip(url) progress(0.5,desc='Extraction 2') excel3gpp(url) progress(0.6,desc='Creating Excel File') extract_directory = url.split("/")[-3] + "_extraction" categories = { "Other": ["URL", "File", "Type", "Title", "Source", "Content"], "CR": ["URL", "File", "Type", "Title", "Source", "Content"], "pCR":["URL", "File", "Type", "Title", "Source", "Content"], "LS": ["URL", "File", "Type", "Title", "Source", "Content"], "WID": ["URL", "File", "Type", "Title", "Source", "Content"], "SID": ["URL", "File", "Type", "Title", "Source", "Content"], "DISCUSSION": ["URL", "File", "Type", "Title", "Source", "Content"], "pdf": ["URL", "File", "Type", "Title", "Source", "Content"], "ppt": ["URL", "File", "Type", "Title", "Source", "Content"], "pptx": ["URL", "File", "Type", "Title", "Source", "Content"] } pourcents2=0.6 data = [] errors_count = 0 processed_count = 0 # Counter for processed files pre_title_section = None try: df = pd.read_excel(temp_excel) except Exception as e: print(f"Initializing a new DataFrame because: {e}") df = pd.DataFrame(columns=["URL", "File", "Type", "Title", "Source", "Status", "Content"]) for folder in os.listdir(extract_directory): folder_path = os.path.join(extract_directory, folder) if os.path.isdir(folder_path): for file in os.listdir(folder_path): progress(min(pourcents2,0.99),desc='Creating Excel File') pourcents2+=0.4/count if file == "__MACOSX": continue file_path = os.path.join(folder_path, file) if file.endswith((".pptx", ".ppt", ".pdf", ".docx", ".doc", ".DOCX")): try: text = textract.process(file_path).decode('utf-8') except Exception as e: print(f"Error processing {file_path}: {e}") errors_count += 1 continue cleaned_text_lines = text.split('\n') cleaned_text = '\n'.join([line.strip('|').strip() for line in cleaned_text_lines if line.strip()]) title = "" debut = "" sections = cleaned_text.split("Title:") if len(sections) > 1: pre_title_section = sections[0].strip().split() title = sections[1].strip().split("\n")[0].strip() debut = sections[0].strip() category = "Other" if file.endswith(".pdf"): category = "pdf" elif file.endswith((".ppt", ".pptx")): category = "ppt" # assuming all ppt and pptx files go into the same category elif "CHANGE REQUEST" in debut: category = "CR" elif "Discussion" in title: category = "DISCUSSION" elif "WID" in title: category = "WID" elif "SID" in title: category = "SID" elif "LS" in title: category = "LS" elif pre_title_section and pre_title_section[-1] == 'pCR': category = "pCR" elif "Pseudo-CR" in title: category = "pCR" contenu = "" # This will hold the concatenated content for 'Contenu' column if category in categories: columns = categories[category] extracted_content = [] if category == "CR": reason_for_change = "" summary_of_change = "" if len(sections) > 1: reason_for_change = sections[1].split("Reason for change", 1)[-1].split("Summary of change")[0].strip() summary_of_change = sections[1].split("Summary of change", 1)[-1].split("Consequences if not")[0].strip() extracted_content.append(f"Reason for change: {reason_for_change}") extracted_content.append(f"Summary of change: {summary_of_change}") elif category == "pCR": if len(sections) > 1:# Handle 'pCR' category-specific content extraction pcr_specific_content = sections[1].split("Introduction", 1)[-1].split("First Change")[0].strip() extracted_content.append(f"Introduction: {pcr_specific_content}") elif category == "LS": overall_review = "" if len(sections) > 1: overall_review = sections[1].split("Overall description", 1)[-1].strip() extracted_content.append(f"Overall review: {overall_review}") elif category in ["WID", "SID"]: objective = "" start_index = cleaned_text.find("Objective") end_index = cleaned_text.find("Expected Output and Time scale") if start_index != -1 and end_index != -1: objective = cleaned_text[start_index + len("Objective"):end_index].strip() extracted_content.append(f"Objective: {objective}") elif category == "DISCUSSION": Discussion = "" extracted_text = replace_line_breaks(cleaned_text) start_index_doc_for = extracted_text.find("Document for:") if start_index_doc_for != -1: start_index_word_after_doc_for = start_index_doc_for + len("Document for:") end_index_word_after_doc_for = start_index_word_after_doc_for + extracted_text[start_index_word_after_doc_for:].find("/n") word_after_doc_for = extracted_text[start_index_word_after_doc_for:end_index_word_after_doc_for].strip() result_intro = '' result_conclusion = '' result_info = '' if word_after_doc_for.lower() == "discussion": start_index_intro = extracted_text.find("Introduction") end_index_intro = extracted_text.find("Discussion", start_index_intro) intro_text = "" if start_index_intro != -1 and end_index_intro != -1: intro_text = extracted_text[start_index_intro + len("Introduction"):end_index_intro].strip() result_intro = remod_text(intro_text) # Convert back line breaks else: result_intro = "Introduction section not found." # Attempt to find "Conclusion" start_index_conclusion = extracted_text.find("Conclusion", end_index_intro) end_index_conclusion = extracted_text.find("Proposal", start_index_conclusion if start_index_conclusion != -1 else end_index_intro) conclusion_text = "" if start_index_conclusion != -1 and end_index_conclusion != -1: conclusion_text = extracted_text[start_index_conclusion + len("Conclusion"):end_index_conclusion].strip() result_conclusion = remod_text(conclusion_text) elif start_index_conclusion == -1: # Conclusion not found, look for Proposal directly start_index_proposal = extracted_text.find("Proposal", end_index_intro) if start_index_proposal != -1: end_index_proposal = len(extracted_text) # Assuming "Proposal" section goes till the end if present proposal_text = extracted_text[start_index_proposal + len("Proposal"):end_index_proposal].strip() result_conclusion = remod_text(proposal_text) # Using "Proposal" content as "Conclusion" else: result_conclusion = "Conclusion/Proposal section not found." else: # Handle case where "Conclusion" exists but no "Proposal" to mark its end conclusion_text = extracted_text[start_index_conclusion + len("Conclusion"):].strip() result_conclusion = remod_text(conclusion_text) Discussion=f"Introduction: {result_intro}\nConclusion/Proposal: {result_conclusion}" elif word_after_doc_for.lower() == "information": start_index_info = extracted_text.find(word_after_doc_for) if start_index_info != -1: info_to_end = extracted_text[start_index_info + len("Information"):].strip() result_info = remod_text(info_to_end) Discussion = f"Discussion:{result_info}" else: Discussion = "The word after 'Document for:' is not 'Discussion', 'DISCUSSION', 'Information', or 'INFORMATION'." else: Discussion = "The phrase 'Document for:' was not found." # Since DISCUSSION category handling requires more specific processing, adapt as necessary # Here's a simplified example discussion_details = Discussion extracted_content.append(discussion_details) # Add more categories as needed contenu = "\n".join(extracted_content) # Assuming 'source' needs to be filled from the guide.xlsx mapping # Placeholder for source value calculation source = "" # Update this with actual source determination logic status = "" data.append([url+ "/" + folder + '.zip', folder , category, title, source,status, contenu]) # After processing all files and directories # Read the guide.xlsx file into a DataFrame to map 'TDoc' to 'Source' guide_df = None # Attempt to load the guide.xlsx file if it exists guide_file_path = 'guide.xlsx' if os.path.exists(guide_file_path): guide_df = pd.read_excel(guide_file_path, usecols=['Source', 'TDoc', 'TDoc Status']) else: print(f"Warning: {guide_file_path} not found.") # Proceed with the rest of the function, ensuring guide_df is checked before use if guide_df is not None: tdoc_source_map = {row['TDoc']: row['Source'] for index, row in guide_df.iterrows()} # Use tdoc_source_map as needed else: print("Error: guide_df is not initialized. Exiting function.") return tdoc_source_map = {row['TDoc']: row['Source'] for index, row in guide_df.iterrows()} tdoc_status_map = {row['TDoc']: row['TDoc Status'] for index, row in guide_df.iterrows()} # Update the 'Source' in your data based on matching 'Nom du fichier' with 'TDoc' for item in data: nom_du_fichier = item[1] # Assuming 'Nom du fichier' is the first item in your data list if nom_du_fichier in tdoc_source_map: item[4] = tdoc_source_map[nom_du_fichier] # Update the 'Source' field, assuming it's the fourth item item[5] = tdoc_status_map[nom_du_fichier] processed_count += 1 # Check if it's time to update the Excel file if processed_count % 20 == 0: update_excel(data, temp_excel, url) print(f"Updated after processing {processed_count} files.") data = [] # Clear the data list after updating if data: # This final call ensures that any remaining data is processed and saved. update_excel(data, temp_excel, url) print(f"Final update after processing all files.") file_name = temp_excel # Save the updated DataFrame to Excel return file_name