app.py

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
import shutil
from tqdm import tqdm
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

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()
            gauth.LocalWebserverAuth()
            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}")
                # Get the current count of images to continue numbering
                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  # Adding image column for dataset
                        })
                    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 from the new files
            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"  # Pre-filled with provided ID
        ),
        gr.Textbox(
            label="Naming Convention",
            placeholder="e.g., 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()