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
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()
            gauth.settings['client_config_file'] = settings['client_secrets_file']
            
            # Try to load saved credentials
            gauth.LoadCredentialsFile("credentials.txt")
            
            if gauth.credentials is None:
                gauth.LocalWebserverAuth()
            elif gauth.access_token_expired:
                gauth.Refresh()
            else:
                gauth.Authorize()
                
            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:
            query = f"'{drive_folder_id}' in parents and trashed=false"
            file_list = self.drive.ListFile({'q': query}).GetList()
            
            if not file_list:
                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 = []
            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)
                    
                    file.GetContentFile(file_path)
                    
                    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:
            df = pd.DataFrame(renamed_files)
            new_dataset = Dataset.from_pandas(df)
            
            try:
                existing_dataset = load_dataset(self.dataset_name)
                if 'train' in existing_dataset:
                    new_dataset = concatenate_datasets([existing_dataset['train'], new_dataset])
            except Exception:
                logger.info("Creating new dataset")
            
            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()
    
    auth_success, auth_message = manager.authenticate_drive()
    if not auth_success:
        return auth_message
    
    success, message, renamed_files = manager.download_and_rename_files(folder_id, naming_convention)
    if not success:
        return message
    
    success, hf_message = manager.update_huggingface_dataset(renamed_files)
    return f"{message}\n{hf_message}"

# Custom CSS for web-safe fonts and clean styling
custom_css = """
.gradio-container {
    font-family: Arial, sans-serif !important;
}

h1, h2, h3 {
    font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
    font-weight: 600 !important;
}

.gr-button {
    font-family: Arial, sans-serif !important;
}

.gr-input {
    font-family: 'Courier New', Courier, monospace !important;
}

.gr-box {
    border-radius: 8px !important;
    border: 1px solid #e5e5e5 !important;
}

.gr-padded {
    padding: 16px !important;
}
"""

# Gradio interface with custom theme
with gr.Blocks(css=custom_css) as demo:
    gr.Markdown("# Sports Cards Dataset Processor")
    
    with gr.Box():
        gr.Markdown("""
        ### Instructions
        1. Enter the Google Drive folder/file ID
        2. Choose a naming convention for your cards
        3. Click Process to start
        """)
    
    with gr.Row():
        with gr.Column():
            folder_id = gr.Textbox(
                label="Google Drive File/Folder ID",
                placeholder="Enter the ID from your Google Drive URL",
                value="151VOxPO91mg0C3ORiioGUd4hogzP1ujm"
            )
            naming = gr.Textbox(
                label="Naming Convention",
                placeholder="e.g., sports_card",
                value="sports_card"
            )
            process_btn = gr.Button("Process Images", variant="primary")
    
    with gr.Box():
        output = gr.Textbox(
            label="Processing Status",
            show_label=True,
            lines=5
        )
    
    process_btn.click(
        fn=process_pipeline,
        inputs=[folder_id, naming],
        outputs=output
    )

if __name__ == "__main__":
    demo.launch()