From e17ece3c21563ddaf699148b8d2b94f513abce76 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CYanhao?= Date: Tue, 24 Sep 2024 20:13:11 -0700 Subject: [PATCH 01/11] dry-run draft --- data_processing.py | 177 ++++++++++++++++++++++++++++++++++++--------- file_utils.py | 13 ++++ main.py | 91 +++++++++++++++++++---- 3 files changed, 235 insertions(+), 46 deletions(-) diff --git a/data_processing.py b/data_processing.py index 64982e5..27853c0 100644 --- a/data_processing.py +++ b/data_processing.py @@ -1,9 +1,18 @@ import re -from nexa.gguf import NexaVLMInference, NexaTextInference -from file_utils import sanitize_filename, create_folder import os import shutil +from nexa.gguf import NexaVLMInference, NexaTextInference +from file_utils import sanitize_filename, create_folder from output_filter import filter_specific_output # Import the context manager +# import nltk +from nltk.tokenize import word_tokenize +from nltk.corpus import stopwords +from nltk.probability import FreqDist + +# # Ensure NLTK resources are downloaded +# nltk.download('punkt', quiet=True) +# nltk.download('stopwords', quiet=True) +# nltk.download('punkt_tab') # Global variables to hold the models image_inference = None @@ -46,6 +55,7 @@ def initialize_models(): print("** Image inference model initialized **") print("** Text inference model initialized **") print("**----------------------------------------------**") + def get_text_from_generator(generator): """Extract text from the generator response.""" response_text = "" @@ -73,6 +83,7 @@ def generate_image_metadata(image_path): # Generate filename filename_prompt = f"""Based on the description below, generate a specific and descriptive filename (2-4 words) for the image. Do not include any data type words like 'image', 'jpg', 'png', etc. Use only letters and connect words with underscores. +Avoid using any special characters, symbols, markdown, or code formatting. Description: {description} @@ -86,20 +97,36 @@ def generate_image_metadata(image_path): filename_response = text_inference.create_completion(filename_prompt) filename = filename_response['choices'][0]['text'].strip() filename = filename.replace('Filename:', '').strip() + + # Remove markdown, code blocks, and special characters + filename = re.sub(r'[\*\`\n]', '', filename) + filename = filename.strip() + + # Check if the AI returned a generic or empty filename + if not filename or filename.lower() in ('untitled', 'unknown', '', 'describes'): + # Use the first few words of the description as the filename + filename = '_'.join(description.split()[:3]) + sanitized_filename = sanitize_filename(filename) - if not sanitized_filename: - sanitized_filename = 'untitled_image' + if not sanitized_filename or sanitized_filename.lower() in ('untitled', ''): + sanitized_filename = 'image_' + os.path.splitext(os.path.basename(image_path))[0] # Generate folder name from description - foldername_prompt = f"""Based on the description below, generate a general category or theme (1-2 words) for this image. -This will be used as the folder name. Do not include specific details or words from the filename. + foldername_prompt = f"""Based on the description below, generate a general category or theme (1-2 words) that best represents the main subject of this image. +This will be used as the folder name. Do not include specific details, words from the filename, any generic terms like 'untitled' or 'unknown', or any special characters, symbols, numbers, markdown, or code formatting. Description: {description} -Example: -Description: A photo of a sunset over the mountains. -Category: landscapes +Examples: +1. Description: A photo of a sunset over the mountains. + Category: landscapes + +2. Description: An image of a smartphone displaying a storage app with various icons and information. + Category: technology + +3. Description: A close-up of a blooming red rose with dew drops. + Category: nature Now generate the category. @@ -107,6 +134,27 @@ def generate_image_metadata(image_path): foldername_response = text_inference.create_completion(foldername_prompt) foldername = foldername_response['choices'][0]['text'].strip() foldername = foldername.replace('Category:', '').strip() + + # Remove markdown, code blocks, and special characters + foldername = re.sub(r'[\*\`\n]', '', foldername) + foldername = foldername.strip() + + # Check if the AI returned a generic or empty category + if not foldername or foldername.lower() in ('untitled', 'unknown', ''): + # Attempt to extract a keyword from the description + + + words = word_tokenize(description.lower()) + words = [word for word in words if word.isalpha()] + stop_words = set(stopwords.words('english')) + filtered_words = [word for word in words if word not in stop_words] + fdist = FreqDist(filtered_words) + most_common = fdist.most_common(1) + if most_common: + foldername = most_common[0][0] + else: + foldername = 'images' + sanitized_foldername = sanitize_filename(foldername) if not sanitized_foldername: @@ -152,7 +200,7 @@ def summarize_text_content(text): summary = response['choices'][0]['text'].strip() return summary -def generate_text_metadata(input_text): +def generate_text_metadata(input_text, file_path): """Generate description, folder name, and filename for a text document.""" initialize_models() @@ -160,14 +208,17 @@ def generate_text_metadata(input_text): description = summarize_text_content(input_text) # Generate filename - filename_prompt = f"""Based on the summary below, generate a specific and descriptive filename (2-4 words) for the document. -Do not include any data type words like 'text', 'document', 'pdf', etc. Use only letters and connect words with underscores. + filename_prompt = f"""Based on the summary below, generate a specific and descriptive filename (2-4 words) that captures the essence of the document. +Do not include any data type words like 'text', 'document', 'pdf', etc. Use only letters and connect words with underscores. Avoid generic terms like 'describes'. Summary: {description} -Example: -Summary: A research paper on the fundamentals of string theory. -Filename: string_theory_fundamentals +Examples: +1. Summary: A research paper on the fundamentals of string theory. + Filename: fundamentals_of_string_theory + +2. Summary: An article discussing the effects of climate change on polar bears. + Filename: climate_change_polar_bears Now generate the filename. @@ -175,20 +226,33 @@ def generate_text_metadata(input_text): filename_response = text_inference.create_completion(filename_prompt) filename = filename_response['choices'][0]['text'].strip() filename = filename.replace('Filename:', '').strip() + + # Remove markdown, code blocks, and special characters + filename = re.sub(r'[\*\`\n]', '', filename) + filename = filename.strip() + + # Check if the AI returned a generic or empty filename + if not filename or filename.lower() in ('untitled', 'unknown', '', 'describes'): + # Use the first few words of the summary as the filename + filename = '_'.join(description.split()[:3]) + sanitized_filename = sanitize_filename(filename) - if not sanitized_filename: - sanitized_filename = 'untitled_document' + if not sanitized_filename or sanitized_filename.lower() in ('untitled', ''): + sanitized_filename = 'document_' + os.path.splitext(os.path.basename(file_path))[0] # Generate folder name from summary - foldername_prompt = f"""Based on the summary below, generate a general category or theme (1-2 words) for this document. -This will be used as the folder name. Do not include specific details or words from the filename. + foldername_prompt = f"""Based on the summary below, generate a general category or theme (1-2 words) that best represents the main subject of this document. +This will be used as the folder name. Do not include specific details, words from the filename, or any generic terms like 'untitled' or 'unknown'. Summary: {description} -Example: -Summary: A research paper on the fundamentals of string theory. -Category: physics +Examples: +1. Summary: A research paper on the fundamentals of string theory. + Category: physics + +2. Summary: An article discussing the effects of climate change on polar bears. + Category: environment Now generate the category. @@ -196,6 +260,29 @@ def generate_text_metadata(input_text): foldername_response = text_inference.create_completion(foldername_prompt) foldername = foldername_response['choices'][0]['text'].strip() foldername = foldername.replace('Category:', '').strip() + + # Remove markdown, code blocks, and special characters + foldername = re.sub(r'[\*\`\n]', '', foldername) + foldername = foldername.strip() + + # Check if the AI returned a generic or empty category + if not foldername or foldername.lower() in ('untitled', 'unknown', ''): + # Attempt to extract a keyword from the summary + from nltk.tokenize import word_tokenize + from nltk.corpus import stopwords + from nltk.probability import FreqDist + + words = word_tokenize(description.lower()) + words = [word for word in words if word.isalpha()] + stop_words = set(stopwords.words('english')) + filtered_words = [word for word in words if word not in stop_words] + fdist = FreqDist(filtered_words) + most_common = fdist.most_common(1) + if most_common: + foldername = most_common[0][0] + else: + foldername = 'documents' + sanitized_foldername = sanitize_filename(foldername) if not sanitized_foldername: @@ -206,7 +293,7 @@ def generate_text_metadata(input_text): def process_single_text_file(args): """Process a single text file to generate metadata.""" file_path, text = args - foldername, filename, description = generate_text_metadata(text) + foldername, filename, description = generate_text_metadata(text, file_path) print(f"File: {file_path}") print(f"Description: {description}") print(f"Folder name: {foldername}") @@ -227,29 +314,53 @@ def process_text_files(text_tuples): results.append(data) return results -def copy_and_rename_files(data_list, new_path, renamed_files, processed_files): +def copy_and_rename_files(data_list, new_path, renamed_files, processed_files, use_hard_links=False, dry_run=False): """Copy and rename files based on generated metadata.""" + operations = [] for data in data_list: file_path = data['file_path'] if file_path in processed_files: continue processed_files.add(file_path) - # Use folder name which generated from the description - dir_path = create_folder(new_path, data['foldername']) - - # Use filename which generated from the description + # Prepare folder name and file name + folder_name = data['foldername'] new_file_name = data['filename'] + os.path.splitext(file_path)[1] + + # Prepare new file path + dir_path = os.path.join(new_path, folder_name) new_file_path = os.path.join(dir_path, new_file_name) # Handle duplicates counter = 1 - while new_file_path in renamed_files or os.path.exists(new_file_path): + original_new_file_name = new_file_name + while new_file_path in renamed_files or (not dry_run and os.path.exists(new_file_path)): new_file_name = f"{data['filename']}_{counter}" + os.path.splitext(file_path)[1] new_file_path = os.path.join(dir_path, new_file_name) counter += 1 - shutil.copy2(file_path, new_file_path) - renamed_files.add(new_file_path) - print(f"Copied and renamed to: {new_file_path}") - print("-" * 50) + # Record the operation + operation = { + 'source': file_path, + 'destination': new_file_path, + 'action': 'copy' if not use_hard_links else 'hardlink' + } + operations.append(operation) + + if not dry_run: + # Create directory + dir_path = create_folder(new_path, folder_name) + new_file_path = os.path.join(dir_path, new_file_name) + # Perform the file operation + try: + if use_hard_links: + os.link(file_path, new_file_path) + else: + shutil.copy2(file_path, new_file_path) + renamed_files.add(new_file_path) + print(f"{operation['action'].capitalize()}d to: {new_file_path}") + except Exception as e: + print(f"Error {operation['action']}ing file '{file_path}' to '{new_file_path}': {e}") + print("-" * 50) + + return operations # Return the list of operations for display or further processing diff --git a/file_utils.py b/file_utils.py index 0f95ecf..1ed02fa 100644 --- a/file_utils.py +++ b/file_utils.py @@ -127,3 +127,16 @@ def separate_files_by_type(file_paths): text_files = [fp for fp in file_paths if os.path.splitext(fp.lower())[1] in text_extensions] return image_files, text_files # Return only two values + +def check_hard_link_support(): + """Check if the filesystem supports hard links.""" + try: + test_file = 'test_hardlink.tmp' + with open(test_file, 'w') as f: + f.write('test') + os.link(test_file, test_file + '_link') + os.unlink(test_file) + os.unlink(test_file + '_link') + return True + except Exception: + return False diff --git a/main.py b/main.py index 1b91284..6602ff4 100644 --- a/main.py +++ b/main.py @@ -7,7 +7,8 @@ separate_files_by_type, read_text_file, read_pdf_file, - read_docx_file + read_docx_file, + check_hard_link_support ) from data_processing import ( @@ -17,6 +18,28 @@ copy_and_rename_files ) +def simulate_directory_tree(operations, base_path): + """Simulate the directory tree based on the proposed operations.""" + tree = {} + for op in operations: + rel_path = os.path.relpath(op['destination'], base_path) + parts = rel_path.split(os.sep) + current_level = tree + for part in parts: + if part not in current_level: + current_level[part] = {} + current_level = current_level[part] + return tree + +def print_simulated_tree(tree, prefix=''): + """Print the simulated directory tree.""" + pointers = ['├── '] * (len(tree) - 1) + ['└── '] if tree else [] + for pointer, key in zip(pointers, tree): + print(prefix + pointer + key) + if tree[key]: # If there are subdirectories or files + extension = '│ ' if pointer == '├── ' else ' ' + print_simulated_tree(tree[key], prefix + extension) + def main(): # Paths configuration print("-" * 50) @@ -54,20 +77,27 @@ def main(): print(f"Time taken to load file paths: {end_time - start_time:.2f} seconds") print("-" * 50) - print("Directory tree before renaming:") + print("Directory tree before organizing:") display_directory_tree(input_path) print("*" * 50) print("The file upload was successful. Processing may take a few minutes.") print("*" * 50) - + + # Check for hard link support + hard_link_supported = check_hard_link_support() + if hard_link_supported: + print("Your filesystem supports hard links. The script will use hard links instead of copying files.") + else: + print("Your filesystem does not support hard links. The script will copy files instead.") + print("-" * 50) + + # Always perform a dry run first + dry_run = True # Separate files by type image_files, text_files = separate_files_by_type(file_paths) - # Process image files - data_images = process_image_files(image_files) - # Prepare text tuples for processing text_tuples = [] for fp in text_files: @@ -83,24 +113,59 @@ def main(): continue # Skip unsupported file formats text_tuples.append((fp, text_content)) - # Process text files + # Process files sequentially + data_images = process_image_files(image_files) data_texts = process_text_files(text_tuples) # Prepare for copying and renaming renamed_files = set() processed_files = set() - # Copy and rename image files - copy_and_rename_files(data_images, output_path, renamed_files, processed_files) + # Combine all data + all_data = data_images + data_texts + + # Copy and rename files (perform dry run) + operations = copy_and_rename_files( + all_data, + output_path, + renamed_files, + processed_files, + use_hard_links=hard_link_supported, + dry_run=True # Always perform dry run first + ) + + # Simulate and display the proposed directory tree + print("Proposed directory structure:") + simulated_tree = simulate_directory_tree(operations, output_path) + print(os.path.abspath(output_path)) + print_simulated_tree(simulated_tree) + print("-" * 50) + + # Ask user if they want to proceed + proceed = input("Would you like to proceed with these changes? (yes/no): ").strip().lower() + if proceed not in ('yes', 'y'): + print("Operation canceled by the user.") + return + + # Perform the actual file operations + print("Performing file operations...") + renamed_files = set() # Reset renamed files + processed_files = set() # Reset processed files - # Copy and rename text files - copy_and_rename_files(data_texts, output_path, renamed_files, processed_files) + copy_and_rename_files( + all_data, + output_path, + renamed_files, + processed_files, + use_hard_links=hard_link_supported, + dry_run=False # Now perform the operations + ) print("-" * 50) print("The folder contents have been renamed and organized successfully.") print("-" * 50) - print("Directory tree after copying and renaming:") - display_directory_tree(output_path) + print("Directory tree after organizing:") + # display_directory_tree(output_path) if __name__ == '__main__': main() From 76751e242e1dc6ef1a82758ae744a374e675c224 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CYanhao?= Date: Wed, 25 Sep 2024 19:27:04 -0700 Subject: [PATCH 02/11] improve dry-run in hardlink and symlink usage and add slient mode draft. --- .gitignore | 1 + data_processing.py | 115 +++++++++++++++++++------------- main.py | 163 +++++++++++++++++++++++++++++---------------- operation_log.txt | 108 ++++++++++++++++++++++++++++++ 4 files changed, 285 insertions(+), 102 deletions(-) create mode 100644 operation_log.txt diff --git a/.gitignore b/.gitignore index 1bf5171..8840ffe 100644 --- a/.gitignore +++ b/.gitignore @@ -20,3 +20,4 @@ venv/ # Ignore example output files organized_folder/ +operation_log.txt/ \ No newline at end of file diff --git a/data_processing.py b/data_processing.py index 27853c0..c77f245 100644 --- a/data_processing.py +++ b/data_processing.py @@ -2,18 +2,12 @@ import os import shutil from nexa.gguf import NexaVLMInference, NexaTextInference -from file_utils import sanitize_filename, create_folder +from file_utils import sanitize_filename from output_filter import filter_specific_output # Import the context manager -# import nltk from nltk.tokenize import word_tokenize from nltk.corpus import stopwords from nltk.probability import FreqDist -# # Ensure NLTK resources are downloaded -# nltk.download('punkt', quiet=True) -# nltk.download('stopwords', quiet=True) -# nltk.download('punkt_tab') - # Global variables to hold the models image_inference = None text_inference = None @@ -143,7 +137,6 @@ def generate_image_metadata(image_path): if not foldername or foldername.lower() in ('untitled', 'unknown', ''): # Attempt to extract a keyword from the description - words = word_tokenize(description.lower()) words = [word for word in words if word.isalpha()] stop_words = set(stopwords.words('english')) @@ -162,14 +155,16 @@ def generate_image_metadata(image_path): return sanitized_foldername, sanitized_filename, description -def process_single_image(image_path): +def process_single_image(image_path, silent=False, log_file=None): """Process a single image file to generate metadata.""" foldername, filename, description = generate_image_metadata(image_path) - print(f"File: {image_path}") - print(f"Description: {description}") - print(f"Folder name: {foldername}") - print(f"Generated filename: {filename}") - print("-" * 50) + message = f"File: {image_path}\nDescription: {description}\nFolder name: {foldername}\nGenerated filename: {filename}\n" + "-" * 50 + if silent: + if log_file: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) return { 'file_path': image_path, 'foldername': foldername, @@ -177,11 +172,11 @@ def process_single_image(image_path): 'description': description } -def process_image_files(image_paths): +def process_image_files(image_paths, silent=False, log_file=None): """Process image files sequentially.""" data_list = [] for image_path in image_paths: - data = process_single_image(image_path) + data = process_single_image(image_path, silent=silent, log_file=log_file) data_list.append(data) return data_list @@ -267,10 +262,6 @@ def generate_text_metadata(input_text, file_path): # Check if the AI returned a generic or empty category if not foldername or foldername.lower() in ('untitled', 'unknown', ''): - # Attempt to extract a keyword from the summary - from nltk.tokenize import word_tokenize - from nltk.corpus import stopwords - from nltk.probability import FreqDist words = word_tokenize(description.lower()) words = [word for word in words if word.isalpha()] @@ -290,15 +281,17 @@ def generate_text_metadata(input_text, file_path): return sanitized_foldername, sanitized_filename, description -def process_single_text_file(args): +def process_single_text_file(args, silent=False, log_file=None): """Process a single text file to generate metadata.""" file_path, text = args foldername, filename, description = generate_text_metadata(text, file_path) - print(f"File: {file_path}") - print(f"Description: {description}") - print(f"Folder name: {foldername}") - print(f"Generated filename: {filename}") - print("-" * 50) + message = f"File: {file_path}\nDescription: {description}\nFolder name: {foldername}\nGenerated filename: {filename}\n" + "-" * 50 + if silent: + if log_file: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) return { 'file_path': file_path, 'foldername': foldername, @@ -306,16 +299,16 @@ def process_single_text_file(args): 'description': description } -def process_text_files(text_tuples): +def process_text_files(text_tuples, silent=False, log_file=None): """Process text files sequentially.""" results = [] for args in text_tuples: - data = process_single_text_file(args) + data = process_single_text_file(args, silent=silent, log_file=log_file) results.append(data) return results -def copy_and_rename_files(data_list, new_path, renamed_files, processed_files, use_hard_links=False, dry_run=False): - """Copy and rename files based on generated metadata.""" +def compute_operations(data_list, new_path, renamed_files, processed_files): + """Compute the file operations based on generated metadata.""" operations = [] for data in data_list: file_path = data['file_path'] @@ -331,36 +324,68 @@ def copy_and_rename_files(data_list, new_path, renamed_files, processed_files, u dir_path = os.path.join(new_path, folder_name) new_file_path = os.path.join(dir_path, new_file_name) + # Ensure the directory for the new path exists before proceeding + os.makedirs(dir_path, exist_ok=True) + # Handle duplicates counter = 1 original_new_file_name = new_file_name - while new_file_path in renamed_files or (not dry_run and os.path.exists(new_file_path)): + while new_file_path in renamed_files or os.path.exists(new_file_path): new_file_name = f"{data['filename']}_{counter}" + os.path.splitext(file_path)[1] new_file_path = os.path.join(dir_path, new_file_name) counter += 1 + # Decide whether to use hardlink or symlink + if os.path.isdir(file_path): + link_type = 'symlink' + else: + source_dev = os.stat(file_path).st_dev + dest_dev = os.stat(os.path.dirname(new_file_path)).st_dev + if source_dev == dest_dev: + link_type = 'hardlink' + else: + link_type = 'symlink' + # Record the operation operation = { 'source': file_path, 'destination': new_file_path, - 'action': 'copy' if not use_hard_links else 'hardlink' + 'link_type': link_type, + 'folder_name': folder_name, + 'new_file_name': new_file_name } operations.append(operation) + renamed_files.add(new_file_path) - if not dry_run: - # Create directory - dir_path = create_folder(new_path, folder_name) - new_file_path = os.path.join(dir_path, new_file_name) - # Perform the file operation + return operations # Return the list of operations for display or further processing + +def execute_operations(operations, dry_run=False, silent=False, log_file=None): + """Execute the file operations.""" + for operation in operations: + source = operation['source'] + destination = operation['destination'] + link_type = operation['link_type'] + dir_path = os.path.dirname(destination) + + # Ensure the directory exists before performing the operation + os.makedirs(dir_path, exist_ok=True) + + if dry_run: + message = f"Dry run: would create {link_type} from '{source}' to '{destination}'" + else: try: - if use_hard_links: - os.link(file_path, new_file_path) + if link_type == 'hardlink': + os.link(source, destination) else: - shutil.copy2(file_path, new_file_path) - renamed_files.add(new_file_path) - print(f"{operation['action'].capitalize()}d to: {new_file_path}") + os.symlink(source, destination) + message = f"Created {link_type} from '{source}' to '{destination}'" except Exception as e: - print(f"Error {operation['action']}ing file '{file_path}' to '{new_file_path}': {e}") - print("-" * 50) + message = f"Error creating {link_type} from '{source}' to '{destination}': {e}" - return operations # Return the list of operations for display or further processing + # Silent mode handling + if silent: + if log_file: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) diff --git a/main.py b/main.py index 6602ff4..f0ca4c4 100644 --- a/main.py +++ b/main.py @@ -7,15 +7,15 @@ separate_files_by_type, read_text_file, read_pdf_file, - read_docx_file, - check_hard_link_support + read_docx_file ) from data_processing import ( initialize_models, process_image_files, process_text_files, - copy_and_rename_files + compute_operations, + execute_operations ) def simulate_directory_tree(operations, base_path): @@ -41,21 +41,38 @@ def print_simulated_tree(tree, prefix=''): print_simulated_tree(tree[key], prefix + extension) def main(): + # Ask the user if they want to enable silent mode at the beginning + silent_mode_input = input("Would you like to enable silent mode? (yes/no): ").strip().lower() + silent_mode = silent_mode_input in ('yes', 'y') + log_file = 'operation_log.txt' if silent_mode else None + # Paths configuration - print("-" * 50) - print("Checking if the model is already downloaded. If not, downloading it now.") + if not silent_mode: + print("-" * 50) + print("Checking if the model is already downloaded. If not, downloading it now.") # Initialize models once initialize_models() input_path = input("Enter the path of the directory you want to organize: ").strip() if not os.path.exists(input_path): - print(f"Input path {input_path} does not exist. Please create it and add the necessary files.") + message = f"Input path {input_path} does not exist. Please create it and add the necessary files." + if silent_mode: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) return # Confirm successful input path - print(f"Input path successfully uploaded: {input_path}") - print("-" * 50) + message = f"Input path successfully uploaded: {input_path}" + if silent_mode: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) + if not silent_mode: + print("-" * 50) # Default output path is a folder named "organized_folder" in the same directory as the input path output_path = input("Enter the path to store organized files and folders (press Enter to use 'organized_folder' in the input directory): ").strip() @@ -67,33 +84,37 @@ def main(): os.makedirs(output_path, exist_ok=True) # Confirm successful output path - print(f"Output path successfully uploaded: {output_path}") - print("-" * 50) + message = f"Output path successfully uploaded: {output_path}" + if silent_mode: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) + if not silent_mode: + print("-" * 50) # Start processing files start_time = time.time() file_paths = collect_file_paths(input_path) end_time = time.time() - print(f"Time taken to load file paths: {end_time - start_time:.2f} seconds") - print("-" * 50) - print("Directory tree before organizing:") - display_directory_tree(input_path) - - print("*" * 50) - print("The file upload was successful. Processing may take a few minutes.") - print("*" * 50) - - # Check for hard link support - hard_link_supported = check_hard_link_support() - if hard_link_supported: - print("Your filesystem supports hard links. The script will use hard links instead of copying files.") + message = f"Time taken to load file paths: {end_time - start_time:.2f} seconds" + if silent_mode: + with open(log_file, 'a') as f: + f.write(message + '\n') else: - print("Your filesystem does not support hard links. The script will copy files instead.") - print("-" * 50) + print(message) + if not silent_mode: + print("-" * 50) + print("Directory tree before organizing:") + display_directory_tree(input_path) + + print("*" * 50) + print("The file upload was successful. Processing may take a few minutes.") + print("*" * 50) - # Always perform a dry run first - dry_run = True + # Prepare to collect link type statistics + link_type_counts = {'hardlink': 0, 'symlink': 0} # Separate files by type image_files, text_files = separate_files_by_type(file_paths) @@ -109,13 +130,18 @@ def main(): elif ext == '.pdf': text_content = read_pdf_file(fp) else: - print(f"Unsupported text file format: {fp}") + message = f"Unsupported text file format: {fp}" + if silent_mode: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) continue # Skip unsupported file formats text_tuples.append((fp, text_content)) # Process files sequentially - data_images = process_image_files(image_files) - data_texts = process_text_files(text_tuples) + data_images = process_image_files(image_files, silent=silent_mode, log_file=log_file) + data_texts = process_text_files(text_tuples, silent=silent_mode, log_file=log_file) # Prepare for copying and renaming renamed_files = set() @@ -124,48 +150,71 @@ def main(): # Combine all data all_data = data_images + data_texts - # Copy and rename files (perform dry run) - operations = copy_and_rename_files( + # Compute the operations + operations = compute_operations( all_data, output_path, renamed_files, - processed_files, - use_hard_links=hard_link_supported, - dry_run=True # Always perform dry run first + processed_files ) + # Count link types + for op in operations: + link_type_counts[op['link_type']] += 1 + + # Inform the user about link types being used + if not silent_mode: + print("-" * 50) + print("Link types to be used for organizing files:") + print(f"Hardlinks: {link_type_counts['hardlink']}") + print(f"Symlinks: {link_type_counts['symlink']}") + print("-" * 50) + # Simulate and display the proposed directory tree - print("Proposed directory structure:") - simulated_tree = simulate_directory_tree(operations, output_path) - print(os.path.abspath(output_path)) - print_simulated_tree(simulated_tree) - print("-" * 50) + message = "Proposed directory structure:" + if silent_mode: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) + print(os.path.abspath(output_path)) + simulated_tree = simulate_directory_tree(operations, output_path) + print_simulated_tree(simulated_tree) + print("-" * 50) # Ask user if they want to proceed proceed = input("Would you like to proceed with these changes? (yes/no): ").strip().lower() if proceed not in ('yes', 'y'): - print("Operation canceled by the user.") + message = "Operation canceled by the user." + if silent_mode: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) return # Perform the actual file operations - print("Performing file operations...") - renamed_files = set() # Reset renamed files - processed_files = set() # Reset processed files - - copy_and_rename_files( - all_data, - output_path, - renamed_files, - processed_files, - use_hard_links=hard_link_supported, - dry_run=False # Now perform the operations + message = "Performing file operations..." + if silent_mode: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) + execute_operations( + operations, + dry_run=False, + silent=silent_mode, + log_file=log_file ) - print("-" * 50) - print("The folder contents have been renamed and organized successfully.") - print("-" * 50) - print("Directory tree after organizing:") - # display_directory_tree(output_path) + message = "The files have been organized successfully using the determined link types." + if silent_mode: + with open(log_file, 'a') as f: + f.write("-" * 50 + '\n' + message + '\n' + "-" * 50 + '\n') + else: + print("-" * 50) + print(message) + print("-" * 50) if __name__ == '__main__': main() diff --git a/operation_log.txt b/operation_log.txt new file mode 100644 index 0000000..fb0b16e --- /dev/null +++ b/operation_log.txt @@ -0,0 +1,108 @@ +Input path successfully uploaded: /Users/q/local_file_organizer/Local-File-Organizer/sample_data +Output path successfully uploaded: /Users/q/local_file_organizer/Local-File-Organizer/organized_folder +Time taken to load file paths: 0.00 seconds +File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/logo.png +Description: The image features the Google Chrome browser logo. The logo consists of the word "Google" in lowercase letters, colored in red, and the word "Chrome" in uppercase letters, colored in blue. The logo is set against a light grey background. The image is a digital graphic, likely used for branding or marketing purposes. +Folder name: branding +Generated filename: google_chrome_logo_light_grey +-------------------------------------------------- +File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/IMG_0967.PNG +Description: The image shows a smartphone screen displaying a storage app with various icons and information. The main subject is the storage app, which is open and occupies the majority of the screen. The app displays the amount of storage available on the device, which is 1.2 GB, and the amount of storage used, which is 1.1 GB. There are icons for different types of files, such as photos, videos, and music, indicating the storage capacity for each category. The app also shows the time as 20:06 and the date as December 12, 2018. The phone's status bar at the top of the screen shows that the phone is connected to Wi-Fi and has a battery level of 80%. The image is a screenshot taken from a smartphone, and the interface elements suggest that the device is running an operating system that supports a file storage app. +Folder name: storage +Generated filename: storage_app_screenshot +-------------------------------------------------- +File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/sub_dir1/animal.jpg +Description: The image depicts two orca whales in mid-air, performing a leap over the ocean. The orcas are captured in a dynamic pose, with their bodies angled upwards and their tails and fins spread out, indicating movement and energy. The ocean beneath them is a deep blue, and the sky above is a clear, bright blue, suggesting a sunny day. In the background, there are snow-capped mountains, which provide a contrasting backdrop to the vibrant marine life in the foreground. The overall scene conveys a sense of freedom and power, as the orcas seem to be enjoying their time in the open water. +Folder name: wildlife +Generated filename: orca_leap_ocean +-------------------------------------------------- +File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/12222_777.docx +Description: The text presents a simple mathematical equation: 1 + 1 = 2. This equation demonstrates the fundamental principle of addition. The result, 2, confirms the fundamental operation of combining two numbers to produce a larger number. +Folder name: math +Generated filename: addition_2explanation_chosen_filename_addition +-------------------------------------------------- +File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/paper_1col.pdf +Description: This text is an introduction to string theory, covering topics like the bosonic string action, symmetries, and quantization methods. It builds on the foundations of classical mechanics and relativity before delving into the intricacies of string theory. The text provides a detailed explanation of the bosonic string action, its symmetries, and the techniques for quantization. +The book explains the BRST quantization method and its relation to physical states. The text also explores the significance of conformal field theory in string theory and the use of operator product expansions to study the theory. +Overall, this text serves as a foundational guide to understanding the core concepts and techniques of string theory. + +**Key details:** +- **Focus:** Introduction to String Theory +- **Content:** Covers topics like bosonic string action, symmetries, quantization, BRST quantization, conformal field theory, and operator product expansions. +- **Methodology:** Utilizes a combination of lecture notes, books, and other resources to provide a comprehensive overview of string theory. +- **Audience:** Students or individuals seeking a foundational understanding of string theory. + +**Main Ideas:** +- String theory provides a framework for understanding fundamental particles and forces. +- The bosonic string action is the key to understanding string theory, and its symmetries play a crucial role in the theory's development. +- Quantization techniques are used to describe the behavior of strings at a quantum level. +- Conformal field theory and operator product expansions are essential for analyzing string theory's predictions. +- BRST quantization method is an alternative approach to quantizing string theory. + + +**Final thought:** +The text provides a valuable resource for anyone interested in learning about string theory, offering a comprehensive overview of its fundamental concepts and methods. +Folder name: physics +Generated filename: string_theory_intro +-------------------------------------------------- +File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/text_files/dsflsdflj.txt +Description: This text describes a person who values work, family, and personal happiness. The individual is described as easygoing, hard-working, and dedicated to their job and responsibilities. They also prioritize caring for their friends and family, highlighting their supportive nature. The repetition of the same phrases and ideas emphasizes the central themes of the text, highlighting the person's consistency and commitment to these values. +Folder name: what_are_some_other_options +Generated filename: values_person +-------------------------------------------------- +File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/sub_dir2/BS.txt +Description: - Account activity for the period of February 1, 2018 – February 28, 2018. +- The account holder is Joe and Jane Smith. +- Account number is 123 456 789. + + +**Main Ideas:** +1. This document summarizes the account activity for a specific time period. +2. The account holder is Joe and Jane Smith, with the account number being 123 456 789. + + +**Key Details:** +- Account activity for February 1, 2018 – February 28, 2018. +- Account holder: Joe and Jane Smith. +- Account number: 123 456 789. + + +This document serves as an overview of the account activity and is not a complete financial statement. + + +**Summary:** +This is a summary of the account activity for Joe and Jane Smith for the period of February 1, 2018 – February 28, 2018. + + +**Please note:** this is a test document and may not reflect actual account information. + + + +**Additional Information:** +For a complete financial statement, please contact your bank. + + +**Main Ideas:** +1. This is a test document. +2. Real account information may be different. + + +**Key Details:** +- Test document. +- May not reflect actual account information. +- For a complete financial statement, contact your bank. +Folder name: finance +Generated filename: activity_smiths_account +-------------------------------------------------- +Proposed directory structure: +Performing file operations... +Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/logo.png' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/branding/google_chrome_logo_light_grey.png' +Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/IMG_0967.PNG' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/storage/storage_app_screenshot.PNG' +Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/sub_dir1/animal.jpg' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/wildlife/orca_leap_ocean.jpg' +Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/12222_777.docx' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/math/addition_2explanation_chosen_filename_addition.docx' +Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/paper_1col.pdf' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/physics/string_theory_intro.pdf' +Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/text_files/dsflsdflj.txt' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/what_are_some_other_options/values_person.txt' +Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/sub_dir2/BS.txt' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/finance/activity_smiths_account.txt' +-------------------------------------------------- +The files have been organized successfully using the determined link types. +-------------------------------------------------- From 8039671fa1e570103ed05a67c37ca2a95d7ff47d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CYanhao?= Date: Wed, 25 Sep 2024 19:36:23 -0700 Subject: [PATCH 03/11] improve .gitignore --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 8840ffe..f3b0d94 100644 --- a/.gitignore +++ b/.gitignore @@ -20,4 +20,4 @@ venv/ # Ignore example output files organized_folder/ -operation_log.txt/ \ No newline at end of file +operation_log.txt \ No newline at end of file From dc387698ff74f533159427ccdb7e722cdd23d307 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CYanhao?= Date: Wed, 25 Sep 2024 19:40:30 -0700 Subject: [PATCH 04/11] Remove ignored files from tracking --- operation_log.txt | 108 ---------------------------------------------- 1 file changed, 108 deletions(-) delete mode 100644 operation_log.txt diff --git a/operation_log.txt b/operation_log.txt deleted file mode 100644 index fb0b16e..0000000 --- a/operation_log.txt +++ /dev/null @@ -1,108 +0,0 @@ -Input path successfully uploaded: /Users/q/local_file_organizer/Local-File-Organizer/sample_data -Output path successfully uploaded: /Users/q/local_file_organizer/Local-File-Organizer/organized_folder -Time taken to load file paths: 0.00 seconds -File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/logo.png -Description: The image features the Google Chrome browser logo. The logo consists of the word "Google" in lowercase letters, colored in red, and the word "Chrome" in uppercase letters, colored in blue. The logo is set against a light grey background. The image is a digital graphic, likely used for branding or marketing purposes. -Folder name: branding -Generated filename: google_chrome_logo_light_grey --------------------------------------------------- -File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/IMG_0967.PNG -Description: The image shows a smartphone screen displaying a storage app with various icons and information. The main subject is the storage app, which is open and occupies the majority of the screen. The app displays the amount of storage available on the device, which is 1.2 GB, and the amount of storage used, which is 1.1 GB. There are icons for different types of files, such as photos, videos, and music, indicating the storage capacity for each category. The app also shows the time as 20:06 and the date as December 12, 2018. The phone's status bar at the top of the screen shows that the phone is connected to Wi-Fi and has a battery level of 80%. The image is a screenshot taken from a smartphone, and the interface elements suggest that the device is running an operating system that supports a file storage app. -Folder name: storage -Generated filename: storage_app_screenshot --------------------------------------------------- -File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/sub_dir1/animal.jpg -Description: The image depicts two orca whales in mid-air, performing a leap over the ocean. The orcas are captured in a dynamic pose, with their bodies angled upwards and their tails and fins spread out, indicating movement and energy. The ocean beneath them is a deep blue, and the sky above is a clear, bright blue, suggesting a sunny day. In the background, there are snow-capped mountains, which provide a contrasting backdrop to the vibrant marine life in the foreground. The overall scene conveys a sense of freedom and power, as the orcas seem to be enjoying their time in the open water. -Folder name: wildlife -Generated filename: orca_leap_ocean --------------------------------------------------- -File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/12222_777.docx -Description: The text presents a simple mathematical equation: 1 + 1 = 2. This equation demonstrates the fundamental principle of addition. The result, 2, confirms the fundamental operation of combining two numbers to produce a larger number. -Folder name: math -Generated filename: addition_2explanation_chosen_filename_addition --------------------------------------------------- -File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/paper_1col.pdf -Description: This text is an introduction to string theory, covering topics like the bosonic string action, symmetries, and quantization methods. It builds on the foundations of classical mechanics and relativity before delving into the intricacies of string theory. The text provides a detailed explanation of the bosonic string action, its symmetries, and the techniques for quantization. -The book explains the BRST quantization method and its relation to physical states. The text also explores the significance of conformal field theory in string theory and the use of operator product expansions to study the theory. -Overall, this text serves as a foundational guide to understanding the core concepts and techniques of string theory. - -**Key details:** -- **Focus:** Introduction to String Theory -- **Content:** Covers topics like bosonic string action, symmetries, quantization, BRST quantization, conformal field theory, and operator product expansions. -- **Methodology:** Utilizes a combination of lecture notes, books, and other resources to provide a comprehensive overview of string theory. -- **Audience:** Students or individuals seeking a foundational understanding of string theory. - -**Main Ideas:** -- String theory provides a framework for understanding fundamental particles and forces. -- The bosonic string action is the key to understanding string theory, and its symmetries play a crucial role in the theory's development. -- Quantization techniques are used to describe the behavior of strings at a quantum level. -- Conformal field theory and operator product expansions are essential for analyzing string theory's predictions. -- BRST quantization method is an alternative approach to quantizing string theory. - - -**Final thought:** -The text provides a valuable resource for anyone interested in learning about string theory, offering a comprehensive overview of its fundamental concepts and methods. -Folder name: physics -Generated filename: string_theory_intro --------------------------------------------------- -File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/text_files/dsflsdflj.txt -Description: This text describes a person who values work, family, and personal happiness. The individual is described as easygoing, hard-working, and dedicated to their job and responsibilities. They also prioritize caring for their friends and family, highlighting their supportive nature. The repetition of the same phrases and ideas emphasizes the central themes of the text, highlighting the person's consistency and commitment to these values. -Folder name: what_are_some_other_options -Generated filename: values_person --------------------------------------------------- -File: /Users/q/local_file_organizer/Local-File-Organizer/sample_data/sub_dir2/BS.txt -Description: - Account activity for the period of February 1, 2018 – February 28, 2018. -- The account holder is Joe and Jane Smith. -- Account number is 123 456 789. - - -**Main Ideas:** -1. This document summarizes the account activity for a specific time period. -2. The account holder is Joe and Jane Smith, with the account number being 123 456 789. - - -**Key Details:** -- Account activity for February 1, 2018 – February 28, 2018. -- Account holder: Joe and Jane Smith. -- Account number: 123 456 789. - - -This document serves as an overview of the account activity and is not a complete financial statement. - - -**Summary:** -This is a summary of the account activity for Joe and Jane Smith for the period of February 1, 2018 – February 28, 2018. - - -**Please note:** this is a test document and may not reflect actual account information. - - - -**Additional Information:** -For a complete financial statement, please contact your bank. - - -**Main Ideas:** -1. This is a test document. -2. Real account information may be different. - - -**Key Details:** -- Test document. -- May not reflect actual account information. -- For a complete financial statement, contact your bank. -Folder name: finance -Generated filename: activity_smiths_account --------------------------------------------------- -Proposed directory structure: -Performing file operations... -Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/logo.png' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/branding/google_chrome_logo_light_grey.png' -Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/IMG_0967.PNG' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/storage/storage_app_screenshot.PNG' -Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/sub_dir1/animal.jpg' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/wildlife/orca_leap_ocean.jpg' -Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/12222_777.docx' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/math/addition_2explanation_chosen_filename_addition.docx' -Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/paper_1col.pdf' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/physics/string_theory_intro.pdf' -Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/text_files/dsflsdflj.txt' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/what_are_some_other_options/values_person.txt' -Created hardlink from '/Users/q/local_file_organizer/Local-File-Organizer/sample_data/sub_dir2/BS.txt' to '/Users/q/local_file_organizer/Local-File-Organizer/organized_folder/finance/activity_smiths_account.txt' --------------------------------------------------- -The files have been organized successfully using the determined link types. --------------------------------------------------- From dad85511ff78c1e905436c0b6631ec612baeb871 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CYanhao?= Date: Thu, 26 Sep 2024 17:56:27 -0700 Subject: [PATCH 05/11] improve --- .gitignore | 2 +- data_processing.py | 24 ++++++++---------------- main.py | 14 ++++++++++---- 3 files changed, 19 insertions(+), 21 deletions(-) diff --git a/.gitignore b/.gitignore index f3b0d94..f08589b 100644 --- a/.gitignore +++ b/.gitignore @@ -20,4 +20,4 @@ venv/ # Ignore example output files organized_folder/ -operation_log.txt \ No newline at end of file +operation_log.txt diff --git a/data_processing.py b/data_processing.py index c77f245..b1b4187 100644 --- a/data_processing.py +++ b/data_processing.py @@ -324,27 +324,19 @@ def compute_operations(data_list, new_path, renamed_files, processed_files): dir_path = os.path.join(new_path, folder_name) new_file_path = os.path.join(dir_path, new_file_name) - # Ensure the directory for the new path exists before proceeding - os.makedirs(dir_path, exist_ok=True) + # Remove directory creation here + # os.makedirs(dir_path, exist_ok=True) # Handle duplicates counter = 1 original_new_file_name = new_file_name - while new_file_path in renamed_files or os.path.exists(new_file_path): + while new_file_path in renamed_files: new_file_name = f"{data['filename']}_{counter}" + os.path.splitext(file_path)[1] new_file_path = os.path.join(dir_path, new_file_name) counter += 1 # Decide whether to use hardlink or symlink - if os.path.isdir(file_path): - link_type = 'symlink' - else: - source_dev = os.stat(file_path).st_dev - dest_dev = os.stat(os.path.dirname(new_file_path)).st_dev - if source_dev == dest_dev: - link_type = 'hardlink' - else: - link_type = 'symlink' + link_type = 'hardlink' # Assume hardlink for now # Record the operation operation = { @@ -367,12 +359,12 @@ def execute_operations(operations, dry_run=False, silent=False, log_file=None): link_type = operation['link_type'] dir_path = os.path.dirname(destination) - # Ensure the directory exists before performing the operation - os.makedirs(dir_path, exist_ok=True) - if dry_run: message = f"Dry run: would create {link_type} from '{source}' to '{destination}'" else: + # Ensure the directory exists before performing the operation + os.makedirs(dir_path, exist_ok=True) + try: if link_type == 'hardlink': os.link(source, destination) @@ -388,4 +380,4 @@ def execute_operations(operations, dry_run=False, silent=False, log_file=None): with open(log_file, 'a') as f: f.write(message + '\n') else: - print(message) + print(message) \ No newline at end of file diff --git a/main.py b/main.py index f0ca4c4..ff203e3 100644 --- a/main.py +++ b/main.py @@ -41,6 +41,9 @@ def print_simulated_tree(tree, prefix=''): print_simulated_tree(tree[key], prefix + extension) def main(): + # Start with dry run set to True + dry_run = True + # Ask the user if they want to enable silent mode at the beginning silent_mode_input = input("Would you like to enable silent mode? (yes/no): ").strip().lower() silent_mode = silent_mode_input in ('yes', 'y') @@ -80,11 +83,11 @@ def main(): # Get the parent directory of the input path and append 'organized_folder' output_path = os.path.join(os.path.dirname(input_path), 'organized_folder') - # Ensure the output directory exists - os.makedirs(output_path, exist_ok=True) + # Do not create the output directory yet + # os.makedirs(output_path, exist_ok=True) # Confirm successful output path - message = f"Output path successfully uploaded: {output_path}" + message = f"Output path successfully set to: {output_path}" if silent_mode: with open(log_file, 'a') as f: f.write(message + '\n') @@ -193,6 +196,9 @@ def main(): print(message) return + # Create the output directory now + os.makedirs(output_path, exist_ok=True) + # Perform the actual file operations message = "Performing file operations..." if silent_mode: @@ -207,7 +213,7 @@ def main(): log_file=log_file ) - message = "The files have been organized successfully using the determined link types." + message = "The files have been organized successfully." if silent_mode: with open(log_file, 'a') as f: f.write("-" * 50 + '\n' + message + '\n' + "-" * 50 + '\n') From 3ddfefdd8958a162d44bbf8518b4177849e6f9a0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CYanhao?= Date: Thu, 26 Sep 2024 18:43:05 -0700 Subject: [PATCH 06/11] add file progress display --- data_processing.py | 135 ++++++++++++++++++++++++++++++--------------- 1 file changed, 91 insertions(+), 44 deletions(-) diff --git a/data_processing.py b/data_processing.py index b1b4187..40edbfb 100644 --- a/data_processing.py +++ b/data_processing.py @@ -1,12 +1,14 @@ import re import os import shutil +import time # Import time to measure processing time from nexa.gguf import NexaVLMInference, NexaTextInference from file_utils import sanitize_filename from output_filter import filter_specific_output # Import the context manager from nltk.tokenize import word_tokenize from nltk.corpus import stopwords from nltk.probability import FreqDist +from rich.progress import Progress, TextColumn, BarColumn, TimeElapsedColumn, SpinnerColumn # Global variables to hold the models image_inference = None @@ -65,16 +67,20 @@ def get_text_from_generator(generator): pass return response_text -def generate_image_metadata(image_path): +def generate_image_metadata(image_path, progress, task_id): """Generate description, folder name, and filename for an image file.""" initialize_models() - # Generate description + # Total steps in processing an image + total_steps = 3 + + # Step 1: Generate description description_prompt = "Please provide a detailed description of this image, focusing on the main subject and any important details." description_generator = image_inference._chat(description_prompt, image_path) description = get_text_from_generator(description_generator).strip() + progress.update(task_id, advance=1 / total_steps) - # Generate filename + # Step 2: Generate filename filename_prompt = f"""Based on the description below, generate a specific and descriptive filename (2-4 words) for the image. Do not include any data type words like 'image', 'jpg', 'png', etc. Use only letters and connect words with underscores. Avoid using any special characters, symbols, markdown, or code formatting. @@ -95,6 +101,7 @@ def generate_image_metadata(image_path): # Remove markdown, code blocks, and special characters filename = re.sub(r'[\*\`\n]', '', filename) filename = filename.strip() + progress.update(task_id, advance=1 / total_steps) # Check if the AI returned a generic or empty filename if not filename or filename.lower() in ('untitled', 'unknown', '', 'describes'): @@ -106,7 +113,7 @@ def generate_image_metadata(image_path): if not sanitized_filename or sanitized_filename.lower() in ('untitled', ''): sanitized_filename = 'image_' + os.path.splitext(os.path.basename(image_path))[0] - # Generate folder name from description + # Step 3: Generate folder name from description foldername_prompt = f"""Based on the description below, generate a general category or theme (1-2 words) that best represents the main subject of this image. This will be used as the folder name. Do not include specific details, words from the filename, any generic terms like 'untitled' or 'unknown', or any special characters, symbols, numbers, markdown, or code formatting. @@ -132,11 +139,11 @@ def generate_image_metadata(image_path): # Remove markdown, code blocks, and special characters foldername = re.sub(r'[\*\`\n]', '', foldername) foldername = foldername.strip() + progress.update(task_id, advance=1 / total_steps) # Check if the AI returned a generic or empty category if not foldername or foldername.lower() in ('untitled', 'unknown', ''): # Attempt to extract a keyword from the description - words = word_tokenize(description.lower()) words = [word for word in words if word.isalpha()] stop_words = set(stopwords.words('english')) @@ -157,8 +164,21 @@ def generate_image_metadata(image_path): def process_single_image(image_path, silent=False, log_file=None): """Process a single image file to generate metadata.""" - foldername, filename, description = generate_image_metadata(image_path) - message = f"File: {image_path}\nDescription: {description}\nFolder name: {foldername}\nGenerated filename: {filename}\n" + "-" * 50 + start_time = time.time() + + # Create a Progress instance for this file + with Progress( + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TimeElapsedColumn() + ) as progress: + task_id = progress.add_task(f"Processing {os.path.basename(image_path)}", total=1.0) + foldername, filename, description = generate_image_metadata(image_path, progress, task_id) + + end_time = time.time() + time_taken = end_time - start_time + + message = f"File: {image_path}\nTime taken: {time_taken:.2f} seconds\nDescription: {description}\nFolder name: {foldername}\nGenerated filename: {filename}\n" if silent: if log_file: with open(log_file, 'a') as f: @@ -195,14 +215,18 @@ def summarize_text_content(text): summary = response['choices'][0]['text'].strip() return summary -def generate_text_metadata(input_text, file_path): +def generate_text_metadata(input_text, file_path, progress, task_id): """Generate description, folder name, and filename for a text document.""" initialize_models() - # Generate description + # Total steps in processing a text file + total_steps = 3 + + # Step 1: Generate description description = summarize_text_content(input_text) + progress.update(task_id, advance=1 / total_steps) - # Generate filename + # Step 2: Generate filename filename_prompt = f"""Based on the summary below, generate a specific and descriptive filename (2-4 words) that captures the essence of the document. Do not include any data type words like 'text', 'document', 'pdf', etc. Use only letters and connect words with underscores. Avoid generic terms like 'describes'. @@ -225,6 +249,7 @@ def generate_text_metadata(input_text, file_path): # Remove markdown, code blocks, and special characters filename = re.sub(r'[\*\`\n]', '', filename) filename = filename.strip() + progress.update(task_id, advance=1 / total_steps) # Check if the AI returned a generic or empty filename if not filename or filename.lower() in ('untitled', 'unknown', '', 'describes'): @@ -236,7 +261,7 @@ def generate_text_metadata(input_text, file_path): if not sanitized_filename or sanitized_filename.lower() in ('untitled', ''): sanitized_filename = 'document_' + os.path.splitext(os.path.basename(file_path))[0] - # Generate folder name from summary + # Step 3: Generate folder name from summary foldername_prompt = f"""Based on the summary below, generate a general category or theme (1-2 words) that best represents the main subject of this document. This will be used as the folder name. Do not include specific details, words from the filename, or any generic terms like 'untitled' or 'unknown'. @@ -259,6 +284,7 @@ def generate_text_metadata(input_text, file_path): # Remove markdown, code blocks, and special characters foldername = re.sub(r'[\*\`\n]', '', foldername) foldername = foldername.strip() + progress.update(task_id, advance=1 / total_steps) # Check if the AI returned a generic or empty category if not foldername or foldername.lower() in ('untitled', 'unknown', ''): @@ -284,8 +310,21 @@ def generate_text_metadata(input_text, file_path): def process_single_text_file(args, silent=False, log_file=None): """Process a single text file to generate metadata.""" file_path, text = args - foldername, filename, description = generate_text_metadata(text, file_path) - message = f"File: {file_path}\nDescription: {description}\nFolder name: {foldername}\nGenerated filename: {filename}\n" + "-" * 50 + start_time = time.time() + + # Create a Progress instance for this file + with Progress( + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TimeElapsedColumn() + ) as progress: + task_id = progress.add_task(f"Processing {os.path.basename(file_path)}", total=1.0) + foldername, filename, description = generate_text_metadata(text, file_path, progress, task_id) + + end_time = time.time() + time_taken = end_time - start_time + + message = f"File: {file_path}\nTime taken: {time_taken:.2f} seconds\nDescription: {description}\nFolder name: {foldername}\nGenerated filename: {filename}\n" if silent: if log_file: with open(log_file, 'a') as f: @@ -324,9 +363,6 @@ def compute_operations(data_list, new_path, renamed_files, processed_files): dir_path = os.path.join(new_path, folder_name) new_file_path = os.path.join(dir_path, new_file_name) - # Remove directory creation here - # os.makedirs(dir_path, exist_ok=True) - # Handle duplicates counter = 1 original_new_file_name = new_file_name @@ -353,31 +389,42 @@ def compute_operations(data_list, new_path, renamed_files, processed_files): def execute_operations(operations, dry_run=False, silent=False, log_file=None): """Execute the file operations.""" - for operation in operations: - source = operation['source'] - destination = operation['destination'] - link_type = operation['link_type'] - dir_path = os.path.dirname(destination) - - if dry_run: - message = f"Dry run: would create {link_type} from '{source}' to '{destination}'" - else: - # Ensure the directory exists before performing the operation - os.makedirs(dir_path, exist_ok=True) - - try: - if link_type == 'hardlink': - os.link(source, destination) - else: - os.symlink(source, destination) - message = f"Created {link_type} from '{source}' to '{destination}'" - except Exception as e: - message = f"Error creating {link_type} from '{source}' to '{destination}': {e}" - - # Silent mode handling - if silent: - if log_file: - with open(log_file, 'a') as f: - f.write(message + '\n') - else: - print(message) \ No newline at end of file + total_operations = len(operations) + + with Progress( + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TimeElapsedColumn(), + transient=True + ) as progress: + task = progress.add_task("Organizing Files...", total=total_operations) + for operation in operations: + source = operation['source'] + destination = operation['destination'] + link_type = operation['link_type'] + dir_path = os.path.dirname(destination) + + if dry_run: + message = f"Dry run: would create {link_type} from '{source}' to '{destination}'" + else: + # Ensure the directory exists before performing the operation + os.makedirs(dir_path, exist_ok=True) + + try: + if link_type == 'hardlink': + os.link(source, destination) + else: + os.symlink(source, destination) + message = f"Created {link_type} from '{source}' to '{destination}'" + except Exception as e: + message = f"Error creating {link_type} from '{source}' to '{destination}': {e}" + + progress.advance(task) + + # Silent mode handling + if silent: + if log_file: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) From 481e4216bcf9544e13ce91500ab5960a9adcf73d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CYanhao?= Date: Sat, 28 Sep 2024 00:27:32 -0700 Subject: [PATCH 07/11] add collect modes and restructure the code, have bug when read ebook --- data_processing.py | 430 ------------------ data_processing_common.py | 204 +++++++++ file_utils.py | 115 ++--- image_data_processing.py | 151 ++++++ main.py | 205 ++++++--- output_filter.py | 54 +-- requirements.txt | 7 +- sample_data/1.xlsx | Bin 0 -> 79265 bytes .../Around the World in 28 Languages.azw3 | Bin 0 -> 3431001 bytes .../c8fed35720671596882b0c6a2ad9586d.gif | Bin 0 -> 260720 bytes .../Around the World in 28 Languages.epub | Bin 0 -> 3246310 bytes .../Around the World in 28 Languages.mobi | Bin 0 -> 447076 bytes sample_data/text_files/ccc.md | 5 + sample_data/text_files/dsflsdflj.txt | 1 - text_data_processing.py | 151 ++++++ 15 files changed, 708 insertions(+), 615 deletions(-) delete mode 100644 data_processing.py create mode 100644 data_processing_common.py create mode 100644 image_data_processing.py create mode 100644 sample_data/1.xlsx create mode 100644 sample_data/Around the World in 28 Languages.azw3 create mode 100644 sample_data/c8fed35720671596882b0c6a2ad9586d.gif create mode 100644 sample_data/sub_dir1/Around the World in 28 Languages.epub create mode 100644 sample_data/text_files/Around the World in 28 Languages.mobi create mode 100644 sample_data/text_files/ccc.md delete mode 100644 sample_data/text_files/dsflsdflj.txt create mode 100644 text_data_processing.py diff --git a/data_processing.py b/data_processing.py deleted file mode 100644 index 40edbfb..0000000 --- a/data_processing.py +++ /dev/null @@ -1,430 +0,0 @@ -import re -import os -import shutil -import time # Import time to measure processing time -from nexa.gguf import NexaVLMInference, NexaTextInference -from file_utils import sanitize_filename -from output_filter import filter_specific_output # Import the context manager -from nltk.tokenize import word_tokenize -from nltk.corpus import stopwords -from nltk.probability import FreqDist -from rich.progress import Progress, TextColumn, BarColumn, TimeElapsedColumn, SpinnerColumn - -# Global variables to hold the models -image_inference = None -text_inference = None - -def initialize_models(): - """Initialize the models if they haven't been initialized yet.""" - global image_inference, text_inference - if image_inference is None or text_inference is None: - # Initialize the models - model_path = "llava-v1.6-vicuna-7b:q4_0" - model_path_text = "gemma-2-2b-instruct:q4_0" - - # Use the filter_specific_output context manager - with filter_specific_output(): - # Initialize the image inference model - image_inference = NexaVLMInference( - model_path=model_path, - local_path=None, - stop_words=[], - temperature=0.3, - max_new_tokens=256, # Reduced to speed up processing - top_k=3, - top_p=0.2, - profiling=False - ) - - # Initialize the text inference model - text_inference = NexaTextInference( - model_path=model_path_text, - local_path=None, - stop_words=[], - temperature=0.5, - max_new_tokens=256, # Reduced to speed up processing - top_k=3, - top_p=0.3, - profiling=False - ) - print("**----------------------------------------------**") - print("** Image inference model initialized **") - print("** Text inference model initialized **") - print("**----------------------------------------------**") - -def get_text_from_generator(generator): - """Extract text from the generator response.""" - response_text = "" - try: - while True: - response = next(generator) - choices = response.get('choices', []) - for choice in choices: - delta = choice.get('delta', {}) - if 'content' in delta: - response_text += delta['content'] - except StopIteration: - pass - return response_text - -def generate_image_metadata(image_path, progress, task_id): - """Generate description, folder name, and filename for an image file.""" - initialize_models() - - # Total steps in processing an image - total_steps = 3 - - # Step 1: Generate description - description_prompt = "Please provide a detailed description of this image, focusing on the main subject and any important details." - description_generator = image_inference._chat(description_prompt, image_path) - description = get_text_from_generator(description_generator).strip() - progress.update(task_id, advance=1 / total_steps) - - # Step 2: Generate filename - filename_prompt = f"""Based on the description below, generate a specific and descriptive filename (2-4 words) for the image. -Do not include any data type words like 'image', 'jpg', 'png', etc. Use only letters and connect words with underscores. -Avoid using any special characters, symbols, markdown, or code formatting. - -Description: {description} - -Example: -Description: A photo of a sunset over the mountains. -Filename: sunset_over_mountains - -Now generate the filename. - -Filename:""" - filename_response = text_inference.create_completion(filename_prompt) - filename = filename_response['choices'][0]['text'].strip() - filename = filename.replace('Filename:', '').strip() - - # Remove markdown, code blocks, and special characters - filename = re.sub(r'[\*\`\n]', '', filename) - filename = filename.strip() - progress.update(task_id, advance=1 / total_steps) - - # Check if the AI returned a generic or empty filename - if not filename or filename.lower() in ('untitled', 'unknown', '', 'describes'): - # Use the first few words of the description as the filename - filename = '_'.join(description.split()[:3]) - - sanitized_filename = sanitize_filename(filename) - - if not sanitized_filename or sanitized_filename.lower() in ('untitled', ''): - sanitized_filename = 'image_' + os.path.splitext(os.path.basename(image_path))[0] - - # Step 3: Generate folder name from description - foldername_prompt = f"""Based on the description below, generate a general category or theme (1-2 words) that best represents the main subject of this image. -This will be used as the folder name. Do not include specific details, words from the filename, any generic terms like 'untitled' or 'unknown', or any special characters, symbols, numbers, markdown, or code formatting. - -Description: {description} - -Examples: -1. Description: A photo of a sunset over the mountains. - Category: landscapes - -2. Description: An image of a smartphone displaying a storage app with various icons and information. - Category: technology - -3. Description: A close-up of a blooming red rose with dew drops. - Category: nature - -Now generate the category. - -Category:""" - foldername_response = text_inference.create_completion(foldername_prompt) - foldername = foldername_response['choices'][0]['text'].strip() - foldername = foldername.replace('Category:', '').strip() - - # Remove markdown, code blocks, and special characters - foldername = re.sub(r'[\*\`\n]', '', foldername) - foldername = foldername.strip() - progress.update(task_id, advance=1 / total_steps) - - # Check if the AI returned a generic or empty category - if not foldername or foldername.lower() in ('untitled', 'unknown', ''): - # Attempt to extract a keyword from the description - words = word_tokenize(description.lower()) - words = [word for word in words if word.isalpha()] - stop_words = set(stopwords.words('english')) - filtered_words = [word for word in words if word not in stop_words] - fdist = FreqDist(filtered_words) - most_common = fdist.most_common(1) - if most_common: - foldername = most_common[0][0] - else: - foldername = 'images' - - sanitized_foldername = sanitize_filename(foldername) - - if not sanitized_foldername: - sanitized_foldername = 'images' - - return sanitized_foldername, sanitized_filename, description - -def process_single_image(image_path, silent=False, log_file=None): - """Process a single image file to generate metadata.""" - start_time = time.time() - - # Create a Progress instance for this file - with Progress( - TextColumn("[progress.description]{task.description}"), - BarColumn(), - TimeElapsedColumn() - ) as progress: - task_id = progress.add_task(f"Processing {os.path.basename(image_path)}", total=1.0) - foldername, filename, description = generate_image_metadata(image_path, progress, task_id) - - end_time = time.time() - time_taken = end_time - start_time - - message = f"File: {image_path}\nTime taken: {time_taken:.2f} seconds\nDescription: {description}\nFolder name: {foldername}\nGenerated filename: {filename}\n" - if silent: - if log_file: - with open(log_file, 'a') as f: - f.write(message + '\n') - else: - print(message) - return { - 'file_path': image_path, - 'foldername': foldername, - 'filename': filename, - 'description': description - } - -def process_image_files(image_paths, silent=False, log_file=None): - """Process image files sequentially.""" - data_list = [] - for image_path in image_paths: - data = process_single_image(image_path, silent=silent, log_file=log_file) - data_list.append(data) - return data_list - -def summarize_text_content(text): - """Summarize the given text content.""" - initialize_models() - - prompt = f"""Provide a concise and accurate summary of the following text, focusing on the main ideas and key details. -Limit your summary to a maximum of 150 words. - -Text: {text} - -Summary:""" - - response = text_inference.create_completion(prompt) - summary = response['choices'][0]['text'].strip() - return summary - -def generate_text_metadata(input_text, file_path, progress, task_id): - """Generate description, folder name, and filename for a text document.""" - initialize_models() - - # Total steps in processing a text file - total_steps = 3 - - # Step 1: Generate description - description = summarize_text_content(input_text) - progress.update(task_id, advance=1 / total_steps) - - # Step 2: Generate filename - filename_prompt = f"""Based on the summary below, generate a specific and descriptive filename (2-4 words) that captures the essence of the document. -Do not include any data type words like 'text', 'document', 'pdf', etc. Use only letters and connect words with underscores. Avoid generic terms like 'describes'. - -Summary: {description} - -Examples: -1. Summary: A research paper on the fundamentals of string theory. - Filename: fundamentals_of_string_theory - -2. Summary: An article discussing the effects of climate change on polar bears. - Filename: climate_change_polar_bears - -Now generate the filename. - -Filename:""" - filename_response = text_inference.create_completion(filename_prompt) - filename = filename_response['choices'][0]['text'].strip() - filename = filename.replace('Filename:', '').strip() - - # Remove markdown, code blocks, and special characters - filename = re.sub(r'[\*\`\n]', '', filename) - filename = filename.strip() - progress.update(task_id, advance=1 / total_steps) - - # Check if the AI returned a generic or empty filename - if not filename or filename.lower() in ('untitled', 'unknown', '', 'describes'): - # Use the first few words of the summary as the filename - filename = '_'.join(description.split()[:3]) - - sanitized_filename = sanitize_filename(filename) - - if not sanitized_filename or sanitized_filename.lower() in ('untitled', ''): - sanitized_filename = 'document_' + os.path.splitext(os.path.basename(file_path))[0] - - # Step 3: Generate folder name from summary - foldername_prompt = f"""Based on the summary below, generate a general category or theme (1-2 words) that best represents the main subject of this document. -This will be used as the folder name. Do not include specific details, words from the filename, or any generic terms like 'untitled' or 'unknown'. - -Summary: {description} - -Examples: -1. Summary: A research paper on the fundamentals of string theory. - Category: physics - -2. Summary: An article discussing the effects of climate change on polar bears. - Category: environment - -Now generate the category. - -Category:""" - foldername_response = text_inference.create_completion(foldername_prompt) - foldername = foldername_response['choices'][0]['text'].strip() - foldername = foldername.replace('Category:', '').strip() - - # Remove markdown, code blocks, and special characters - foldername = re.sub(r'[\*\`\n]', '', foldername) - foldername = foldername.strip() - progress.update(task_id, advance=1 / total_steps) - - # Check if the AI returned a generic or empty category - if not foldername or foldername.lower() in ('untitled', 'unknown', ''): - - words = word_tokenize(description.lower()) - words = [word for word in words if word.isalpha()] - stop_words = set(stopwords.words('english')) - filtered_words = [word for word in words if word not in stop_words] - fdist = FreqDist(filtered_words) - most_common = fdist.most_common(1) - if most_common: - foldername = most_common[0][0] - else: - foldername = 'documents' - - sanitized_foldername = sanitize_filename(foldername) - - if not sanitized_foldername: - sanitized_foldername = 'documents' - - return sanitized_foldername, sanitized_filename, description - -def process_single_text_file(args, silent=False, log_file=None): - """Process a single text file to generate metadata.""" - file_path, text = args - start_time = time.time() - - # Create a Progress instance for this file - with Progress( - TextColumn("[progress.description]{task.description}"), - BarColumn(), - TimeElapsedColumn() - ) as progress: - task_id = progress.add_task(f"Processing {os.path.basename(file_path)}", total=1.0) - foldername, filename, description = generate_text_metadata(text, file_path, progress, task_id) - - end_time = time.time() - time_taken = end_time - start_time - - message = f"File: {file_path}\nTime taken: {time_taken:.2f} seconds\nDescription: {description}\nFolder name: {foldername}\nGenerated filename: {filename}\n" - if silent: - if log_file: - with open(log_file, 'a') as f: - f.write(message + '\n') - else: - print(message) - return { - 'file_path': file_path, - 'foldername': foldername, - 'filename': filename, - 'description': description - } - -def process_text_files(text_tuples, silent=False, log_file=None): - """Process text files sequentially.""" - results = [] - for args in text_tuples: - data = process_single_text_file(args, silent=silent, log_file=log_file) - results.append(data) - return results - -def compute_operations(data_list, new_path, renamed_files, processed_files): - """Compute the file operations based on generated metadata.""" - operations = [] - for data in data_list: - file_path = data['file_path'] - if file_path in processed_files: - continue - processed_files.add(file_path) - - # Prepare folder name and file name - folder_name = data['foldername'] - new_file_name = data['filename'] + os.path.splitext(file_path)[1] - - # Prepare new file path - dir_path = os.path.join(new_path, folder_name) - new_file_path = os.path.join(dir_path, new_file_name) - - # Handle duplicates - counter = 1 - original_new_file_name = new_file_name - while new_file_path in renamed_files: - new_file_name = f"{data['filename']}_{counter}" + os.path.splitext(file_path)[1] - new_file_path = os.path.join(dir_path, new_file_name) - counter += 1 - - # Decide whether to use hardlink or symlink - link_type = 'hardlink' # Assume hardlink for now - - # Record the operation - operation = { - 'source': file_path, - 'destination': new_file_path, - 'link_type': link_type, - 'folder_name': folder_name, - 'new_file_name': new_file_name - } - operations.append(operation) - renamed_files.add(new_file_path) - - return operations # Return the list of operations for display or further processing - -def execute_operations(operations, dry_run=False, silent=False, log_file=None): - """Execute the file operations.""" - total_operations = len(operations) - - with Progress( - TextColumn("[progress.description]{task.description}"), - BarColumn(), - TimeElapsedColumn(), - transient=True - ) as progress: - task = progress.add_task("Organizing Files...", total=total_operations) - for operation in operations: - source = operation['source'] - destination = operation['destination'] - link_type = operation['link_type'] - dir_path = os.path.dirname(destination) - - if dry_run: - message = f"Dry run: would create {link_type} from '{source}' to '{destination}'" - else: - # Ensure the directory exists before performing the operation - os.makedirs(dir_path, exist_ok=True) - - try: - if link_type == 'hardlink': - os.link(source, destination) - else: - os.symlink(source, destination) - message = f"Created {link_type} from '{source}' to '{destination}'" - except Exception as e: - message = f"Error creating {link_type} from '{source}' to '{destination}': {e}" - - progress.advance(task) - - # Silent mode handling - if silent: - if log_file: - with open(log_file, 'a') as f: - f.write(message + '\n') - else: - print(message) diff --git a/data_processing_common.py b/data_processing_common.py new file mode 100644 index 0000000..b166b35 --- /dev/null +++ b/data_processing_common.py @@ -0,0 +1,204 @@ +import os +import re +import datetime # Import datetime for date operations +from rich.progress import Progress, TextColumn, BarColumn, TimeElapsedColumn + +def sanitize_filename(name, max_length=50, max_words=5): + """Sanitize the filename by removing unwanted words and characters.""" + # Remove file extension if present + name = os.path.splitext(name)[0] + # Remove unwanted words and data type words + name = re.sub( + r'\b(jpg|jpeg|png|gif|bmp|txt|pdf|docx|xls|xlsx|image|picture|photo|this|that|these|those|here|there|' + r'please|note|additional|notes|folder|name|sure|heres|a|an|the|and|of|in|' + r'to|for|on|with|your|answer|should|be|only|summary|summarize|text|category)\b', + '', + name, + flags=re.IGNORECASE + ) + # Remove non-word characters except underscores + sanitized = re.sub(r'[^\w\s]', '', name).strip() + # Replace multiple underscores or spaces with a single underscore + sanitized = re.sub(r'[\s_]+', '_', sanitized) + # Convert to lowercase + sanitized = sanitized.lower() + # Remove leading/trailing underscores + sanitized = sanitized.strip('_') + # Split into words and limit the number of words + words = sanitized.split('_') + limited_words = [word for word in words if word] # Remove empty strings + limited_words = limited_words[:max_words] + limited_name = '_'.join(limited_words) + # Limit length + return limited_name[:max_length] if limited_name else 'untitled' + +def process_files_by_date(file_paths, output_path, dry_run=False, silent=False, log_file=None): + """Process files to organize them by date.""" + operations = [] + for file_path in file_paths: + # Get the modification time + mod_time = os.path.getmtime(file_path) + # Convert to datetime + mod_datetime = datetime.datetime.fromtimestamp(mod_time) + year = mod_datetime.strftime('%Y') + month = mod_datetime.strftime('%B') # e.g., 'January', or use '%m' for month number + # Create directory path + dir_path = os.path.join(output_path, year, month) + # Prepare new file path + new_file_name = os.path.basename(file_path) + new_file_path = os.path.join(dir_path, new_file_name) + # Decide whether to use hardlink or symlink + link_type = 'hardlink' # Assume hardlink for now + # Record the operation + operation = { + 'source': file_path, + 'destination': new_file_path, + 'link_type': link_type, + } + operations.append(operation) + return operations + +def process_files_by_type(file_paths, output_path, dry_run=False, silent=False, log_file=None): + """Process files to organize them by type, first separating into text-based and image-based files.""" + operations = [] + + # Define extensions + image_extensions = ('.png', '.jpg', '.jpeg', '.gif', '.bmp', '.tiff') + text_extensions = ('.txt', '.md', '.docx', '.doc', '.pdf', '.xls', '.xlsx', '.epub', '.mobi', '.azw', '.azw3') + + for file_path in file_paths: + # Exclude hidden files (additional safety) + if os.path.basename(file_path).startswith('.'): + continue + + # Get the file extension + ext = os.path.splitext(file_path)[1].lower() + + # Check if it's an image file + if ext in image_extensions: + # Image-based files + top_folder = 'image_files' + # You can add subcategories here if needed + folder_name = top_folder + + elif ext in text_extensions: + # Text-based files + top_folder = 'text_files' + # Map extensions to subfolders + if ext in ('.txt', '.md'): + sub_folder = 'plain_text_files' + elif ext in ('.doc', '.docx'): + sub_folder = 'doc_files' + elif ext == '.pdf': + sub_folder = 'pdf_files' + elif ext in ('.xls', '.xlsx'): + sub_folder = 'xls_files' + elif ext in ('.epub', '.mobi', '.azw', '.azw3'): + sub_folder = 'ebooks' + else: + sub_folder = 'others' + folder_name = os.path.join(top_folder, sub_folder) + + else: + # Other types + folder_name = 'others' + + # Create directory path + dir_path = os.path.join(output_path, folder_name) + # Prepare new file path + new_file_name = os.path.basename(file_path) + new_file_path = os.path.join(dir_path, new_file_name) + # Decide whether to use hardlink or symlink + link_type = 'hardlink' # Assume hardlink for now + # Record the operation + operation = { + 'source': file_path, + 'destination': new_file_path, + 'link_type': link_type, + } + operations.append(operation) + + return operations + +def compute_operations(data_list, new_path, renamed_files, processed_files): + """Compute the file operations based on generated metadata.""" + operations = [] + for data in data_list: + file_path = data['file_path'] + if file_path in processed_files: + continue + processed_files.add(file_path) + + # Prepare folder name and file name + folder_name = data['foldername'] + new_file_name = data['filename'] + os.path.splitext(file_path)[1] + + # Prepare new file path + dir_path = os.path.join(new_path, folder_name) + new_file_path = os.path.join(dir_path, new_file_name) + + # Handle duplicates + counter = 1 + original_new_file_name = new_file_name + while new_file_path in renamed_files: + new_file_name = f"{data['filename']}_{counter}" + os.path.splitext(file_path)[1] + new_file_path = os.path.join(dir_path, new_file_name) + counter += 1 + + # Decide whether to use hardlink or symlink + link_type = 'hardlink' # Assume hardlink for now + + # Record the operation + operation = { + 'source': file_path, + 'destination': new_file_path, + 'link_type': link_type, + 'folder_name': folder_name, + 'new_file_name': new_file_name + } + operations.append(operation) + renamed_files.add(new_file_path) + + return operations # Return the list of operations for display or further processing + +def execute_operations(operations, dry_run=False, silent=False, log_file=None): + """Execute the file operations.""" + total_operations = len(operations) + + with Progress( + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TimeElapsedColumn(), + transient=True + ) as progress: + task = progress.add_task("Organizing Files...", total=total_operations) + for operation in operations: + source = operation['source'] + destination = operation['destination'] + link_type = operation['link_type'] + dir_path = os.path.dirname(destination) + + if dry_run: + message = f"Dry run: would create {link_type} from '{source}' to '{destination}'" + else: + # Ensure the directory exists before performing the operation + os.makedirs(dir_path, exist_ok=True) + + try: + if link_type == 'hardlink': + os.link(source, destination) + else: + os.symlink(source, destination) + message = f"Created {link_type} from '{source}' to '{destination}'" + except Exception as e: + message = f"Error creating {link_type} from '{source}' to '{destination}': {e}" + + progress.advance(task) + + # Silent mode handling + if silent: + if log_file: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) diff --git a/file_utils.py b/file_utils.py index 1ed02fa..fbb209c 100644 --- a/file_utils.py +++ b/file_utils.py @@ -5,35 +5,18 @@ import pytesseract import fitz # PyMuPDF import docx +import pandas as pd # Import pandas to read Excel files -def sanitize_filename(name, max_length=50, max_words=5): - """Sanitize the filename by removing unwanted words and characters.""" - # Remove file extension if present - name = os.path.splitext(name)[0] - # Remove unwanted words and data type words - name = re.sub( - r'\b(jpg|jpeg|png|gif|bmp|txt|pdf|docx|image|picture|photo|this|that|these|those|here|there|' - r'please|note|additional|notes|folder|name|sure|heres|a|an|the|and|of|in|' - r'to|for|on|with|your|answer|should|be|only|summary|summarize|text|category)\b', - '', - name, - flags=re.IGNORECASE - ) - # Remove non-word characters except underscores - sanitized = re.sub(r'[^\w\s]', '', name).strip() - # Replace multiple underscores or spaces with a single underscore - sanitized = re.sub(r'[\s_]+', '_', sanitized) - # Convert to lowercase - sanitized = sanitized.lower() - # Remove leading/trailing underscores - sanitized = sanitized.strip('_') - # Split into words and limit the number of words - words = sanitized.split('_') - limited_words = [word for word in words if word] # Remove empty strings - limited_words = limited_words[:max_words] - limited_name = '_'.join(limited_words) - # Limit length - return limited_name[:max_length] if limited_name else 'untitled' +def read_text_file(file_path): + """Read text content from a text file.""" + max_chars = 3000 # Limit processing time + try: + with open(file_path, 'r', encoding='utf-8', errors='ignore') as file: + text = file.read(max_chars) + return text + except Exception as e: + print(f"Error reading text file {file_path}: {e}") + return None def read_docx_file(file_path): """Read text content from a .docx file.""" @@ -43,7 +26,7 @@ def read_docx_file(file_path): return '\n'.join(full_text) except Exception as e: print(f"Error reading DOCX file {file_path}: {e}") - return "" + return None def read_pdf_file(file_path): """Read text content from a PDF file.""" @@ -59,28 +42,31 @@ def read_pdf_file(file_path): return pdf_content except Exception as e: print(f"Error reading PDF file {file_path}: {e}") - return "" + return None -def read_image_file(file_path): - """Extract text from an image file using OCR.""" +def read_excel_file(file_path): + """Read text content from an Excel file.""" try: - image = Image.open(file_path) - text = pytesseract.image_to_string(image) + df = pd.read_excel(file_path) + text = df.to_string() return text except Exception as e: - print(f"Error reading image file {file_path}: {e}") - return "" + print(f"Error reading Excel file {file_path}: {e}") + return None -def read_text_file(file_path): - """Read text content from a text file.""" - max_chars = 3000 # Limit processing time - try: - with open(file_path, 'r', encoding='utf-8', errors='ignore') as file: - text = file.read(max_chars) - return text - except Exception as e: - print(f"Error reading text file {file_path}: {e}") - return "" +def read_file_data(file_path): + """Read content from a file based on its extension.""" + ext = os.path.splitext(file_path.lower())[1] + if ext in ['.txt', '.md', '.epub', '.mobi', '.azw', '.azw3']: + return read_text_file(file_path) + elif ext == '.docx': + return read_docx_file(file_path) + elif ext == '.pdf': + return read_pdf_file(file_path) + elif ext in ['.xls', '.xlsx']: + return read_excel_file(file_path) + else: + return None # Unsupported file type def display_directory_tree(path): """Display the directory tree in a format similar to the 'tree' command, including the full path.""" @@ -99,44 +85,25 @@ def tree(dir_path, prefix=''): else: print(os.path.abspath(path)) -def create_folder(base_path, foldername): - """Create a directory for the given folder name.""" - sanitized_folder_name = sanitize_filename(foldername) - dir_path = os.path.join(base_path, sanitized_folder_name) - os.makedirs(dir_path, exist_ok=True) - return dir_path - def collect_file_paths(base_path): - """Collect all file paths from the base directory or single file.""" + """Collect all file paths from the base directory or single file, excluding hidden files.""" if os.path.isfile(base_path): return [base_path] else: - file_paths = [ - os.path.join(root, file) - for root, _, files in os.walk(base_path) - for file in files - ] + file_paths = [] + for root, _, files in os.walk(base_path): + for file in files: + if not file.startswith('.'): # Exclude hidden files + file_paths.append(os.path.join(root, file)) return file_paths + def separate_files_by_type(file_paths): """Separate files into images and text files based on their extensions.""" - image_extensions = ('.png', '.jpg', '.jpeg', '.gif', '.bmp') - text_extensions = ('.txt', '.docx', '.pdf') + image_extensions = ('.png', '.jpg', '.jpeg', '.gif', '.bmp', '.tiff') + text_extensions = ('.txt', '.docx', '.pdf', '.md', '.epub', '.mobi', '.azw', '.azw3', '.xls', '.xlsx') # Added Excel extensions image_files = [fp for fp in file_paths if os.path.splitext(fp.lower())[1] in image_extensions] text_files = [fp for fp in file_paths if os.path.splitext(fp.lower())[1] in text_extensions] return image_files, text_files # Return only two values - -def check_hard_link_support(): - """Check if the filesystem supports hard links.""" - try: - test_file = 'test_hardlink.tmp' - with open(test_file, 'w') as f: - f.write('test') - os.link(test_file, test_file + '_link') - os.unlink(test_file) - os.unlink(test_file + '_link') - return True - except Exception: - return False diff --git a/image_data_processing.py b/image_data_processing.py new file mode 100644 index 0000000..5adab70 --- /dev/null +++ b/image_data_processing.py @@ -0,0 +1,151 @@ +import re +import os +import time +from nltk.tokenize import word_tokenize +from nltk.corpus import stopwords +from nltk.probability import FreqDist +from rich.progress import Progress, TextColumn, BarColumn, TimeElapsedColumn +from data_processing_common import sanitize_filename # Import sanitize_filename + +def get_text_from_generator(generator): + """Extract text from the generator response.""" + response_text = "" + try: + while True: + response = next(generator) + choices = response.get('choices', []) + for choice in choices: + delta = choice.get('delta', {}) + if 'content' in delta: + response_text += delta['content'] + except StopIteration: + pass + return response_text + +def process_single_image(image_path, image_inference, text_inference, silent=False, log_file=None): + """Process a single image file to generate metadata.""" + start_time = time.time() + + # Create a Progress instance for this file + with Progress( + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TimeElapsedColumn() + ) as progress: + task_id = progress.add_task(f"Processing {os.path.basename(image_path)}", total=1.0) + foldername, filename, description = generate_image_metadata(image_path, progress, task_id, image_inference, text_inference) + + end_time = time.time() + time_taken = end_time - start_time + + message = f"File: {image_path}\nTime taken: {time_taken:.2f} seconds\nDescription: {description}\nFolder name: {foldername}\nGenerated filename: {filename}\n" + if silent: + if log_file: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) + return { + 'file_path': image_path, + 'foldername': foldername, + 'filename': filename, + 'description': description + } + +def process_image_files(image_paths, image_inference, text_inference, silent=False, log_file=None): + """Process image files sequentially.""" + data_list = [] + for image_path in image_paths: + data = process_single_image(image_path, image_inference, text_inference, silent=silent, log_file=log_file) + data_list.append(data) + return data_list + +def generate_image_metadata(image_path, progress, task_id, image_inference, text_inference): + """Generate description, folder name, and filename for an image file.""" + + # Total steps in processing an image + total_steps = 3 + + # Step 1: Generate description using image_inference + description_prompt = "Please provide a detailed description of this image, focusing on the main subject and any important details." + description_generator = image_inference._chat(description_prompt, image_path) + description = get_text_from_generator(description_generator).strip() + progress.update(task_id, advance=1 / total_steps) + + # Step 2: Generate filename using text_inference + filename_prompt = f"""Based on the description below, generate a specific and descriptive filename (2-4 words) for the image. +Do not include any data type words like 'image', 'jpg', 'png', etc. Use only letters and connect words with underscores. +Avoid using any special characters, symbols, markdown, or code formatting. + +Description: {description} + +Example: +Description: A photo of a sunset over the mountains. +Filename: sunset_over_mountains + +Now generate the filename. + +Filename:""" + filename_response = text_inference.create_completion(filename_prompt) + filename = filename_response['choices'][0]['text'].strip() + filename = filename.replace('Filename:', '').strip() + progress.update(task_id, advance=1 / total_steps) + + # Step 3: Generate folder name from description using text_inference + foldername_prompt = f"""Based on the description below, generate a general category or theme (1-2 words) that best represents the main subject of this image. +This will be used as the folder name. Do not include specific details, words from the filename, any generic terms like 'untitled' or 'unknown', or any special characters, symbols, numbers, markdown, or code formatting. + +Description: {description} + +Examples: +1. Description: A photo of a sunset over the mountains. + Category: landscapes + +2. Description: An image of a smartphone displaying a storage app with various icons and information. + Category: technology + +3. Description: A close-up of a blooming red rose with dew drops. + Category: nature + +Now generate the category. + +Category:""" + foldername_response = text_inference.create_completion(foldername_prompt) + foldername = foldername_response['choices'][0]['text'].strip() + foldername = foldername.replace('Category:', '').strip() + progress.update(task_id, advance=1 / total_steps) + + # Remove markdown, code blocks, and special characters from filename and foldername + filename = re.sub(r'[\*\`\n]', '', filename).strip() + foldername = re.sub(r'[\*\`\n]', '', foldername).strip() + + # Check if the AI returned a generic or empty filename + if not filename or filename.lower() in ('untitled', 'unknown', '', 'describes'): + # Use the first few words of the description as the filename + filename = '_'.join(description.split()[:3]) + + sanitized_filename = sanitize_filename(filename) + + if not sanitized_filename or sanitized_filename.lower() in ('untitled', ''): + sanitized_filename = 'image_' + os.path.splitext(os.path.basename(image_path))[0] + + # Check if the AI returned a generic or empty category + if not foldername or foldername.lower() in ('untitled', 'unknown', ''): + # Attempt to extract a keyword from the description + words = word_tokenize(description.lower()) + words = [word for word in words if word.isalpha()] + stop_words = set(stopwords.words('english')) + filtered_words = [word for word in words if word not in stop_words] + fdist = FreqDist(filtered_words) + most_common = fdist.most_common(1) + if most_common: + foldername = most_common[0][0] + else: + foldername = 'images' + + sanitized_foldername = sanitize_filename(foldername) + + if not sanitized_foldername: + sanitized_foldername = 'images' + + return sanitized_foldername, sanitized_filename, description diff --git a/main.py b/main.py index ff203e3..1543a34 100644 --- a/main.py +++ b/main.py @@ -5,19 +5,69 @@ display_directory_tree, collect_file_paths, separate_files_by_type, - read_text_file, - read_pdf_file, - read_docx_file + read_file_data # Import read_file_data ) -from data_processing import ( - initialize_models, - process_image_files, - process_text_files, +from data_processing_common import ( compute_operations, - execute_operations + execute_operations, + process_files_by_date, + process_files_by_type, ) +from text_data_processing import ( + process_text_files +) + +from image_data_processing import ( + process_image_files +) + +from output_filter import filter_specific_output # Import the context manager +from nexa.gguf import NexaVLMInference, NexaTextInference # Import model classes + +# Initialize models +image_inference = None +text_inference = None + +def initialize_models(): + """Initialize the models if they haven't been initialized yet.""" + global image_inference, text_inference + if image_inference is None or text_inference is None: + # Initialize the models + model_path = "llava-v1.6-vicuna-7b:q4_0" + model_path_text = "gemma-2-2b-instruct:q4_0" + + # Use the filter_specific_output context manager + with filter_specific_output(): + # Initialize the image inference model + image_inference = NexaVLMInference( + model_path=model_path, + local_path=None, + stop_words=[], + temperature=0.3, + max_new_tokens=3000, + top_k=3, + top_p=0.2, + profiling=False + ) + + # Initialize the text inference model + text_inference = NexaTextInference( + model_path=model_path_text, + local_path=None, + stop_words=[], + temperature=0.5, + max_new_tokens=3000, # Adjust as needed + top_k=3, + top_p=0.3, + profiling=False + ) + print("**----------------------------------------------**") + print("** Image inference model initialized **") + print("** Text inference model initialized **") + print("**----------------------------------------------**") + def simulate_directory_tree(operations, base_path): """Simulate the directory tree based on the proposed operations.""" tree = {} @@ -52,10 +102,6 @@ def main(): # Paths configuration if not silent_mode: print("-" * 50) - print("Checking if the model is already downloaded. If not, downloading it now.") - - # Initialize models once - initialize_models() input_path = input("Enter the path of the directory you want to organize: ").strip() if not os.path.exists(input_path): @@ -113,65 +159,88 @@ def main(): display_directory_tree(input_path) print("*" * 50) - print("The file upload was successful. Processing may take a few minutes.") - print("*" * 50) - # Prepare to collect link type statistics - link_type_counts = {'hardlink': 0, 'symlink': 0} - - # Separate files by type - image_files, text_files = separate_files_by_type(file_paths) - - # Prepare text tuples for processing - text_tuples = [] - for fp in text_files: - ext = os.path.splitext(fp.lower())[1] - if ext == '.txt': - text_content = read_text_file(fp) - elif ext == '.docx': - text_content = read_docx_file(fp) - elif ext == '.pdf': - text_content = read_pdf_file(fp) - else: - message = f"Unsupported text file format: {fp}" - if silent_mode: - with open(log_file, 'a') as f: - f.write(message + '\n') - else: - print(message) - continue # Skip unsupported file formats - text_tuples.append((fp, text_content)) - - # Process files sequentially - data_images = process_image_files(image_files, silent=silent_mode, log_file=log_file) - data_texts = process_text_files(text_tuples, silent=silent_mode, log_file=log_file) - - # Prepare for copying and renaming - renamed_files = set() - processed_files = set() - - # Combine all data - all_data = data_images + data_texts - - # Compute the operations - operations = compute_operations( - all_data, - output_path, - renamed_files, - processed_files - ) + # Ask the user to choose the mode + print("Please choose the mode to organize your files:") + print("1. By Content") + print("2. By Date") + print("3. By Type") + mode_input = input("Enter 1, 2, or 3: ").strip() - # Count link types - for op in operations: - link_type_counts[op['link_type']] += 1 + if mode_input == '1': + mode = 'content' + elif mode_input == '2': + mode = 'date' + elif mode_input == '3': + mode = 'type' + else: + print("Invalid selection. Defaulting to Content mode.") + mode = 'content' - # Inform the user about link types being used - if not silent_mode: - print("-" * 50) - print("Link types to be used for organizing files:") - print(f"Hardlinks: {link_type_counts['hardlink']}") - print(f"Symlinks: {link_type_counts['symlink']}") - print("-" * 50) + + + if mode == 'content': + # Proceed with content mode + # Initialize models once + if not silent_mode: + print("Checking if the model is already downloaded. If not, downloading it now.") + initialize_models() + + if not silent_mode: + print("*" * 50) + print("The file upload was successful. Processing may take a few minutes.") + print("*" * 50) + + # Prepare to collect link type statistics + link_type_counts = {'hardlink': 0, 'symlink': 0} + + # Separate files by type + image_files, text_files = separate_files_by_type(file_paths) + + # Prepare text tuples for processing + text_tuples = [] + for fp in text_files: + # Use read_file_data to read the file content + text_content = read_file_data(fp) + if text_content is None: + message = f"Unsupported or unreadable text file format: {fp}" + if silent_mode: + with open(log_file, 'a') as f: + f.write(message + '\n') + else: + print(message) + continue # Skip unsupported or unreadable files + text_tuples.append((fp, text_content)) + + # Process files sequentially + data_images = process_image_files(image_files, image_inference, text_inference, silent=silent_mode, log_file=log_file) + data_texts = process_text_files(text_tuples, text_inference, silent=silent_mode, log_file=log_file) + + + # Prepare for copying and renaming + renamed_files = set() + processed_files = set() + + # Combine all data + all_data = data_images + data_texts + + # Compute the operations + operations = compute_operations( + all_data, + output_path, + renamed_files, + processed_files + ) + + elif mode == 'date': + # Process files by date + operations = process_files_by_date(file_paths, output_path, dry_run=False, silent=silent_mode, log_file=log_file) + elif mode == 'type': + # Process files by type + operations = process_files_by_type(file_paths, output_path, dry_run=False, silent=silent_mode, log_file=log_file) + else: + print("Invalid mode selected.") + return # Simulate and display the proposed directory tree message = "Proposed directory structure:" diff --git a/output_filter.py b/output_filter.py index 88234f2..a666327 100644 --- a/output_filter.py +++ b/output_filter.py @@ -1,49 +1,21 @@ -import sys -import re -from contextlib import contextmanager - -class FilteredStream: - def __init__(self, original_stream): - self.original_stream = original_stream - self.buffer = '' - # Pattern to match messages we want to suppress - self.pattern = re.compile(r"Model .* already exists at .*") - - def write(self, message): - # Append the message to the buffer - self.buffer += message - # Check for newlines to process complete lines - while '\n' in self.buffer: - line, self.buffer = self.buffer.split('\n', 1) - self.process_line(line + '\n') - # If progress bar uses carriage return, process those lines as well - if '\r' in self.buffer: - line, self.buffer = self.buffer.split('\r', 1) - self.process_line(line + '\r') - - def flush(self): - if self.buffer: - self.process_line(self.buffer) - self.buffer = '' - self.original_stream.flush() +# output_filter.py - def process_line(self, line): - if self.pattern.match(line.strip()): - # Suppress this line - return - else: - self.original_stream.write(line) - self.original_stream.flush() +import sys +import contextlib -@contextmanager +@contextlib.contextmanager def filter_specific_output(): - """A context manager that filters out specific output during model initialization.""" + """A context manager to filter out specific unwanted output.""" + # Store the original stdout original_stdout = sys.stdout - original_stderr = sys.stderr - sys.stdout = FilteredStream(original_stdout) - sys.stderr = FilteredStream(original_stderr) + + # Create a dummy file-like object that discards writes + class DummyFile: + def write(self, x): pass + def flush(self): pass + + sys.stdout = DummyFile() try: yield finally: sys.stdout = original_stdout - sys.stderr = original_stderr diff --git a/requirements.txt b/requirements.txt index 583104b..38f6c80 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,9 @@ cmake pytesseract PyMuPDF -python-docx \ No newline at end of file +python-docx +pandas +openpyxl +xlrd +nltk +rich diff --git a/sample_data/1.xlsx b/sample_data/1.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..fa43310423e4351d642a76b143974e0d015ad280 GIT binary patch literal 79265 zcmeEs1yo$yvS8yHB)EHU2*I@>1b24`A-Dy18c2Wu0Yb3g?(Po3-5r9vyF>SM?!8a` zd-whI|E!r=Yv#@Ay-#nceRfst+O@0d9A!B;cw7J?02u%PPyvbuQuRh*0DuN)6$gL} zt0!)6=WJ@{Y^3h-!PLos&E3|9G7}z_AsqkA>;jRS@$pk| z8A3Z8F)Y1o;Ajy%bNXw#64}2Vbb%k(EPEMXyF;lm2vnXoa8^}iYlbDIa8J{8Ww%(* zX$YI+@$JRB+IIR1d#CxBX}n&GdATgsdHr>6`24A!syLf5G7ZAUA z{Zbc|Hsf);fPj{>&CnWUV6cR4S0E7ibq)rgq(FSCi|_;{>bcyP8%Hq8SS#f;PZ%4{ z69=|@qx|l8NFue|tAa@?AIc^}l~{8_w{^msXj#IVfPKX`k5nvo-EKj^yLuguwt~@* z`c~!@HMOss%$Lk>2iLm>KX6xkO*VodAM>wf_m4ktM?o<2d%3nOnz(QcE70{b1bLs_ zvbQc8?UukxG&ppfQEs(2>8zE)KS-8Ub5p2SNWNtWbr;ZvE4l3I%=Ll|jB-QT_VdN% zbse`p&0N#2IPpn43fG3%n`e2W8Y>ki;~#_`Z%QaxeCiWeYI~w{5#etx^9Rh&^DE|jNfMeKd3JdL7fo^j&s2(tK zG_`SJXZv;k|NH;nc+>wK>BZj^eez-QiDd zQ+QBx#Tm}Dl`(fE5?Z{T`H77V;z#HrrP&=I+@NpCryc7^sCy}kSxBcJyG0hXLaaQ@ z)jcAOEa(K6DRSF<*UZDKP-Z#kY2Tki!~8Y(&B#}s+pqJl_b<3JH8XN(+sl0i_C60h zP@BM!)9XH^?VebVtEx)U1t!9!Y~LO7AyhYKOE}?xekMqI@4Re&g_);q$KIcL!%-n@ zGNLug`+Q`%@*c{Cf8H__3`)-RvE$-`U$+|2j5{)orbz>Jlt6 z2O4{mk*2Q~vBCF3MrJ(*p;2X~^hsM?FGRv-P}*@_`F_RCalg45U6wamT7=B^xM4?; zx4!rSy(B?hA6HzSjr5B)cGxF^K`OA1n<)Yzqe9pxv&^^R&8DbE5v{&NHlOr`Q7T>o zl=G<+YuLmWH5~>V3)$ZEqn@&S8Y)0kW3$zemBfjCm3uMdO4eKFG=SDDj=UxHvg=*D z34TcZfKG-RieoigzPLrAo6Har1%p{H7@cg2o&Ze& zABA2~%nEZ{uY-HBgBkJU^7VfIo~ny`Z8K0+bt^%1L__^evh5U=gGuJSKS9VG7m;n` zGVkyioRYH@*9FO_36qrj050HHh`r!;XxMtTMc(~gW9OH;SZTmS(JM>tHr)C{>E(-ZQl zyE8bRZ)asB*S!6Q$i2JsJD3mhRfP9FHqX-h`0OaMv~eBi=*m+AdM!`~s&tK*mk#s4 zk;YJ`avt;hj5?a7lx`vT%%V0&SSz&jrMP%iS@7&7l5?2wJW_NbHYPZCy84o=GsfO4 zPPikeK?wBZ^8<31Wl+4SR^n=we7W!8{!HkF%GUQ8py$LMJODrpK!$+|{qIM`-;4fV zjtv;7*$9RG_r98A6s$X6q6eLPdcB_iQod$fy( zSCuDHH;4Xt<&pPu^)Kdcs9i^6JgLN-(iSk}^QUGkBdDC89o5gBAzDP4%I)V8XA6zX z^Wf3gsRe=;ThUV3trr{Z5Sn!>X5XI5qcL5Rmw+djK0D3=UVY0oiK|8Sj6!_>dUTRy zJNs;|=n7BHd))Lcs>@g?_rG-7A8|9BWxc4tB_;WTuCJV9I8A2S-Ao`P=oZa z007B!0DurGKz}G#Cks$yNki zROQC|q^0+Z^GalrnJkJ7Ds+rS3wxjyIMwZEx?26#=DK>m&-{>npq+k?WJraamZOvi z0u=)+1nokq>;}*4!Yqhv?0ZWf-j0hO<6^UyUH1aVXDI5(pR%tyyNw`@z7*X?-)c_s z@axJIdP-apk0nzSE43d;*!ja;!EvCf(Dm^6^T;z>`uXnGx*;apj)+57uas~C8$A!S ztlJo#%xNa=+bWBSmD@!wd)jMp_O07!mvXsO-9;8fOS_G3qwA(?>V?DlkWn$h>`0v~ zR_iK4gB821xd&3mHi}Q@_%q@|5E*bHndClIE8+DG?_UGy-J%etrZo93SDN$&;hfrG5QmbBue&)05}{Yi1p(n+rbeRp(hs`5=^ z65At;tNtR-x3AmY(>vR!itvsuc$RU!sMR-gdKkYecux18*`6YobZNBxl|iIl=S*~u zn9*JPk-*m5-J$LF$zu{$3UlAew~J1E<}uH@sZkq@!B)1gExz-$%i`BhBKGRL`!e_W z!b4T%bM%(g{G`ge7GaG-YdQvCu==bB%{PPRl{OzEPvuFxKNW}jjt3p4YFIffj!tct zyeJx88Qet*2c(|BM;eO&go*Pri8RBg#&BXHb$Jvx*{|3}Hg=cSw|Z0LTvL~ax};D2 zZv$B(;+6C@8IlMiUY*EsXh`kI%pWEP+C8cAjX@S3eIWpBcVDHm6^=fg}?S{24XBa8mE7DxB(3GpCq7 z8yZpQn8|xmohsWSe6_mwXDnL z^ekFG%40h&2PycKaB5F%&8f4m6KkPJ{$gH!b!F zoAo9-e&mw@C3r-xTp;6elk6Dm+$C13R&j(KUvL)`GbUC!=g*h9HhzXR2DlwRdR8O9*XlstMz``BHU2%g1VVYj`{q9rdrQ@6t$4uH1!Dd zPjs64IEvb}rSDNXYWd8R>e9$4JPKFhe5DT|M`$Ci&8{l(y%b@cmEarUpL-e7{L7r; z4(Q$7F&-KP^AewrJd6}N5kxe!QAy-HK1St%U@^m@`272VmQ4MK6x2) z0b|0-rP%im6D7_U8cwo%8TM7p70M-sWc7uq8%(H^W___{xF}RTF-@Qt9H&a~o>$xs zpHcK3?h@NHEo`x2icsnKjo_QgAs?fHc#l*~1Jul@0R{e~`-_W?w0H+ai(cW3p~t&d zS7*#s!&<~`LPkXmU47)|uQcMh@TAh5Nd(Q*60`=VJPV>1#b4YCvm(;{FF zefH~ijq240l7!y6;G`XxnsIJgDVip;`XH{d*uuaf9b@jm+x-uBuxBu*svAP#T+@7} zR0?NtU(|P6FbYNliBwbK1yoaJq5nRS>gE)%(|Ce71tWHE3aj9v_P-zCZVUM8!Q%>T z3j{VO8keelKd>2c;n@}-aY_kq80pbBHz}>+n4SNlxsiPPKT9IpjsHo4*>3zx5_qcB zp)4YOTT1fx>0K$wzfOb4t^oYz-FK}x21D+dVf;$Y90w?^^a%4WB?vgvcHbRC+kG?9 z?v>hFP|wnQH=e6gT&DTTp*wqzf$on8#?Hhbd%Vb^y+}`aX|PXuq7Z*d zaX(sdhA|N3AQt5i7UeJ&

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