from pydrive2.auth import GoogleAuth
from pydrive2.drive import GoogleDrive
import os
import gradio as gr
from datasets import load_dataset, Dataset, concatenate_datasets
import pandas as pd
from PIL import Image
from tqdm import tqdm
import logging
import yaml
from huggingface_hub import login
import json  # Add this import

# Authenticate with Hugging Face
HF_TOKEN = os.getenv('HF_TOKEN')
if HF_TOKEN:
    login(token=HF_TOKEN)
else:
    logger.warning("No Hugging Face token found. Please add HF_TOKEN to your Space secrets.")

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Load settings
if not os.path.exists("settings.yaml"):
    raise FileNotFoundError("settings.yaml file is missing. Please add it with 'client_secrets_file'.")

with open('settings.yaml', 'r') as file:
    settings = yaml.safe_load(file)

# Utility Functions
def safe_load_dataset(dataset_name):
    """Load Hugging Face dataset safely."""
    try:
        dataset = load_dataset(dataset_name)
        return dataset, len(dataset['train']) if 'train' in dataset else 0
    except Exception as e:
        logger.info(f"No existing dataset found. Starting fresh. Error: {str(e)}")
        return None, 0

def is_valid_image(file_path):
    """Check if a file is a valid image."""
    try:
        with Image.open(file_path) as img:
            img.verify()
            return True
    except Exception as e:
        logger.error(f"Invalid image: {file_path}. Error: {str(e)}")
        return False

def validate_input(folder_id, naming_convention):
    """Validate user input."""
    if not folder_id or not folder_id.strip():
        return False, "Folder ID cannot be empty"
    if not naming_convention or not naming_convention.strip():
        return False, "Naming convention cannot be empty"
    if not naming_convention.replace('_', '').isalnum():
        return False, "Naming convention should only contain letters, numbers, and underscores"
    return True, ""

def initialize_dataset():
    """Initialize or verify the dataset structure."""
    try:
        # Check if the README.md exists, if not create it
        readme_content = """# Sports Cards Dataset
This dataset contains sports card images with structured metadata. Each image is named using a consistent convention and includes relevant information about the card.
## Dataset Structure
```
sports_card_{number}.jpg - Card images
```
## Features
- file_path: Path to the image file
- original_name: Original filename of the card
- new_name: Standardized filename
- image: Image data
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("GotThatData/sports-cards")
```
## License
This dataset is licensed under MIT.
## Creator
Created by GotThatData
"""
        # Create dataset info content
        dataset_info = {
            "description": "A collection of sports card images with metadata",
            "citation": "",
            "homepage": "https://huggingface.co/datasets/GotThatData/sports-cards",
            "license": "mit",
            "features": {
                "file_path": {"dtype": "string", "_type": "Value"},
                "original_name": {"dtype": "string", "_type": "Value"},
                "new_name": {"dtype": "string", "_type": "Value"},
                "image": {"dtype": "string", "_type": "Value"}
            },
            "splits": ["train"]
        }

        # Write files
        with open("README.md", "w") as f:
            f.write(readme_content)
        with open("dataset-info.json", "w") as f:
            json.dump(dataset_info, f, indent=2)

        # Upload files to repository
        upload_file(
            path_or_fileobj="README.md",
            path_in_repo="README.md",
            repo_id="GotThatData/sports-cards",
            repo_type="dataset"
        )
        upload_file(
            path_or_fileobj="dataset-info.json",
            path_in_repo="dataset-info.json",
            repo_id="GotThatData/sports-cards",
            repo_type="dataset"
        )
        
        return True, "Dataset structure initialized successfully"
    except Exception as e:
        return False, f"Failed to initialize dataset: {str(e)}"

# DatasetManager Class
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"
        os.makedirs(local_images_dir, exist_ok=True)
        
        # Initialize dataset structure
        success, message = initialize_dataset()
        if not success:
            logger.warning(f"Dataset initialization warning: {message}")

    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:
            logger.error(f"Authentication failed: {str(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:
                logger.warning(f"No files found in folder: {drive_folder_id}")
                return False, "No files found in the specified folder.", []

            existing_dataset, start_index = safe_load_dataset(self.dataset_name)
            renamed_files = []
            processed_count = 0
            error_count = 0

            for i, file in enumerate(tqdm(file_list, desc="Downloading files", unit="file")):
                if 'mimeType' in file and 'image' in file['mimeType'].lower():
                    new_filename = f"{naming_convention}_{start_index + processed_count + 1}.jpg"
                    file_path = os.path.join(self.local_images_dir, new_filename)
                    
                    try:
                        file.GetContentFile(file_path)
                        if is_valid_image(file_path):
                            renamed_files.append({
                                'file_path': file_path,
                                'original_name': file['title'],
                                'new_name': new_filename
                            })
                            processed_count += 1
                            logger.info(f"Successfully processed: {file['title']} -> {new_filename}")
                        else:
                            error_count += 1
                            if os.path.exists(file_path):
                                os.remove(file_path)
                    except Exception as e:
                        error_count += 1
                        logger.error(f"Error processing file {file['title']}: {str(e)}")
                        if os.path.exists(file_path):
                            os.remove(file_path)

            status_message = f"Processed {processed_count} images successfully"
            if error_count > 0:
                status_message += f" ({error_count} files failed)"
            
            return True, status_message, renamed_files
        except Exception as e:
            logger.error(f"Download error: {str(e)}")
            return False, f"Error during download: {str(e)}", []

    def update_huggingface_dataset(self, renamed_files):
        """Update Hugging Face dataset with new images."""
        if not renamed_files:
            return False, "No files to update"
            
        try:
            df = pd.DataFrame(renamed_files)
            new_dataset = Dataset.from_pandas(df)

            existing_dataset, _ = safe_load_dataset(self.dataset_name)
            if existing_dataset and 'train' in existing_dataset:
                combined_dataset = concatenate_datasets([existing_dataset['train'], new_dataset])
            else:
                combined_dataset = new_dataset
            
            combined_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:
            logger.error(f"Dataset update error: {str(e)}")
            return False, f"Error updating Hugging Face dataset: {str(e)}"

def process_pipeline(folder_id, naming_convention):
    """Main pipeline for processing images and updating dataset."""
    # Validate input
    is_valid, error_message = validate_input(folder_id, naming_convention)
    if not is_valid:
        return error_message, []

    manager = DatasetManager()

    # Step 1: Authenticate Google Drive
    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}", renamed_files

def process_ui(folder_id, naming_convention):
    """UI handler for the process pipeline"""
    status, renamed_files = process_pipeline(folder_id, naming_convention)
    table_data = [[file['original_name'], file['new_name'], file['file_path']] 
                 for file in renamed_files] if renamed_files else []
    return status, table_data

# Custom CSS for web-safe fonts and improved styling
custom_css = """
div.gradio-container {
    font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif !important;
}
div.gradio-container button {
    font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif !important;
}
div.gradio-container input {
    font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif !important;
}
.gr-form {
    background-color: #ffffff;
    border-radius: 8px;
    padding: 20px;
    box-shadow: 0 1px 3px rgba(0,0,0,0.1);
}
.gr-button {
    background-color: #2c5282;
    color: white;
}
.gr-button:hover {
    background-color: #2b6cb0;
}
.gr-input {
    border: 1px solid #e2e8f0;
}
.gr-input:focus {
    border-color: #4299e1;
    box-shadow: 0 0 0 1px #4299e1;
}
"""

# Gradio interface
demo = gr.Interface(
    fn=process_ui,
    inputs=[
        gr.Textbox(
            label="Google Drive Folder ID",
            placeholder="Enter the folder ID from the URL",
            info="Found in your Google Drive folder's URL"
        ),
        gr.Textbox(
            label="Naming Convention",
            placeholder="e.g., sports_card",
            value="sports_card",
            info="Use only letters, numbers, and underscores"
        )
    ],
    outputs=[
        gr.Textbox(
            label="Status",
            lines=3
        ),
        gr.Dataframe(
            headers=["Original Name", "New Name", "File Path"],
            wrap=True
        )
    ],
    title="Sports Cards Dataset Processor",
    description="""
    Instructions:
    1. Enter the Google Drive folder ID (found in the folder's URL)
    2. Specify a naming convention for the files (e.g., 'sports_card')
    3. Click submit to start processing
    
    Note: Only image files will be processed. Invalid images will be skipped.
    """,
    css=custom_css,
    theme="default"
)

if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860
    )