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| from pydrive2.auth import GoogleAuth | |
| from pydrive2.drive import GoogleDrive | |
| import os | |
| import gradio as gr | |
| from datasets import load_dataset, Dataset | |
| import pandas as pd | |
| from PIL import Image | |
| from tqdm import tqdm | |
| import logging | |
| import yaml | |
| # Set up logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Load settings | |
| with open('settings.yaml', 'r') as file: | |
| settings = yaml.safe_load(file) | |
| class DatasetManager: | |
| def __init__(self, local_images_dir="downloaded_cards"): | |
| self.local_images_dir = local_images_dir | |
| self.drive = None | |
| self.dataset_name = "GotThatData/sports-cards" | |
| # Create local directory if it doesn't exist | |
| os.makedirs(local_images_dir, exist_ok=True) | |
| def authenticate_drive(self): | |
| """Authenticate with Google Drive""" | |
| try: | |
| gauth = GoogleAuth() | |
| # Use the settings from yaml file | |
| gauth.settings['client_config_file'] = settings['client_secrets_file'] | |
| # Try to load saved credentials | |
| gauth.LoadCredentialsFile("credentials.txt") | |
| if gauth.credentials is None: | |
| # Authenticate if no credentials found | |
| gauth.LocalWebserverAuth() | |
| elif gauth.access_token_expired: | |
| # Refresh them if expired | |
| gauth.Refresh() | |
| else: | |
| # Initialize the saved credentials | |
| gauth.Authorize() | |
| # Save the credentials for future use | |
| gauth.SaveCredentialsFile("credentials.txt") | |
| self.drive = GoogleDrive(gauth) | |
| return True, "Successfully authenticated with Google Drive" | |
| except Exception as e: | |
| return False, f"Authentication failed: {str(e)}" | |
| def download_and_rename_files(self, drive_folder_id, naming_convention): | |
| """Download files from Google Drive and rename them""" | |
| if not self.drive: | |
| return False, "Google Drive not authenticated", [] | |
| try: | |
| # List files in the folder | |
| query = f"'{drive_folder_id}' in parents and trashed=false" | |
| file_list = self.drive.ListFile({'q': query}).GetList() | |
| if not file_list: | |
| # Try to get single file if folder is empty | |
| file = self.drive.CreateFile({'id': drive_folder_id}) | |
| if file: | |
| file_list = [file] | |
| else: | |
| return False, "No files found with the specified ID", [] | |
| renamed_files = [] | |
| existing_dataset = None | |
| try: | |
| existing_dataset = load_dataset(self.dataset_name) | |
| logger.info(f"Loaded existing dataset: {self.dataset_name}") | |
| start_index = len(existing_dataset['train']) if 'train' in existing_dataset else 0 | |
| except Exception as e: | |
| logger.info(f"No existing dataset found, starting fresh: {str(e)}") | |
| start_index = 0 | |
| for i, file in enumerate(tqdm(file_list, desc="Downloading files")): | |
| if file['mimeType'].startswith('image/'): | |
| new_filename = f"{naming_convention}_{start_index + i + 1}.jpg" | |
| file_path = os.path.join(self.local_images_dir, new_filename) | |
| # Download file | |
| file.GetContentFile(file_path) | |
| # Verify the image can be opened | |
| try: | |
| with Image.open(file_path) as img: | |
| img.verify() | |
| renamed_files.append({ | |
| 'file_path': file_path, | |
| 'original_name': file['title'], | |
| 'new_name': new_filename, | |
| 'image': file_path | |
| }) | |
| except Exception as e: | |
| logger.error(f"Error processing image {file['title']}: {str(e)}") | |
| if os.path.exists(file_path): | |
| os.remove(file_path) | |
| return True, f"Successfully processed {len(renamed_files)} images", renamed_files | |
| except Exception as e: | |
| return False, f"Error downloading files: {str(e)}", [] | |
| def update_huggingface_dataset(self, renamed_files): | |
| """Update the sports-cards dataset with new images""" | |
| try: | |
| # Create a DataFrame with the file information | |
| df = pd.DataFrame(renamed_files) | |
| # Create a Hugging Face Dataset | |
| new_dataset = Dataset.from_pandas(df) | |
| try: | |
| # Try to load existing dataset | |
| existing_dataset = load_dataset(self.dataset_name) | |
| # Concatenate with existing dataset if it exists | |
| if 'train' in existing_dataset: | |
| new_dataset = concatenate_datasets([existing_dataset['train'], new_dataset]) | |
| except Exception: | |
| logger.info("Creating new dataset") | |
| # Push to Hugging Face Hub | |
| new_dataset.push_to_hub(self.dataset_name, split="train") | |
| return True, f"Successfully updated dataset '{self.dataset_name}' with {len(renamed_files)} new images" | |
| except Exception as e: | |
| return False, f"Error updating Hugging Face dataset: {str(e)}" | |
| def process_pipeline(folder_id, naming_convention): | |
| """Main pipeline to process images and update dataset""" | |
| manager = DatasetManager() | |
| # Step 1: Authenticate | |
| auth_success, auth_message = manager.authenticate_drive() | |
| if not auth_success: | |
| return auth_message | |
| # Step 2: Download and rename files | |
| success, message, renamed_files = manager.download_and_rename_files(folder_id, naming_convention) | |
| if not success: | |
| return message | |
| # Step 3: Update Hugging Face dataset | |
| success, hf_message = manager.update_huggingface_dataset(renamed_files) | |
| return f"{message}\n{hf_message}" | |
| # Gradio interface | |
| demo = gr.Interface( | |
| fn=process_pipeline, | |
| inputs=[ | |
| gr.Textbox( | |
| label="Google Drive File/Folder ID", | |
| placeholder="Enter the ID from your Google Drive URL", | |
| value="151VOxPO91mg0C3ORiioGUd4hogzP1ujm" | |
| ), | |
| gr.Textbox( | |
| label="Naming Convention", | |
| placeholder="e.g., sports_card", | |
| value="sports_card" | |
| ) | |
| ], | |
| outputs=gr.Textbox(label="Status"), | |
| title="Sports Cards Dataset Processor", | |
| description="Download card images from Google Drive and add them to the sports-cards dataset" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |