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from datasets import Dataset, load_dataset, concatenate_datasets
from huggingface_hub import HfApi, HfFolder
import logging
from typing import Optional, Dict, List
import pandas as pd
from src.api.dependency import get_embedding_service, get_huggingface_service
from src.api.exceptions import (
DatasetNotFoundError,
DatasetPushError,
DatasetDeleteError,
)
# Set up structured logging
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)
class HuggingFaceService:
def __init__(self, hf_token: Optional[str] = None):
"""Initialize the HuggingFaceService with an optional token."""
self.hf_api = HfApi()
if hf_token:
HfFolder.save_token(hf_token) # Save the token for authentication
async def push_to_hub(self, df: pd.DataFrame, dataset_name: str) -> None:
"""Push the dataset to Hugging Face Hub."""
try:
logger.info(f"Creating Hugging Face Dataset: {dataset_name}...")
ds = Dataset.from_pandas(df)
ds.push_to_hub(dataset_name)
logger.info(f"Dataset pushed to Hugging Face Hub: {dataset_name}")
except Exception as e:
logger.error(f"Failed to push dataset to Hugging Face Hub: {e}")
raise DatasetPushError(f"Failed to push dataset: {e}")
async def read_dataset(self, dataset_name: str) -> Optional[pd.DataFrame]:
"""Read a dataset from Hugging Face Hub."""
try:
logger.info(f"Loading dataset from Hugging Face Hub: {dataset_name}...")
ds = load_dataset(dataset_name)
df = ds["train"].to_dict()
return df
except Exception as e:
logger.error(f"Failed to read dataset: {e}")
raise DatasetNotFoundError(f"Dataset not found: {e}")
# async def update_dataset(
# self, dataset_name: str, updates: Dict[str, List]
# ) -> Optional[pd.DataFrame]:
# """Update a dataset on Hugging Face Hub."""
# embedding_service = get_embedding_service()
# try:
# df_src = await self.read_dataset(dataset_name)
# df_src = Dataset.from_dict(df_src)
# df_update = Dataset.from_dict(updates)
# df = concatenate_datasets(df_src, df_update)
# # for column, values in updates.items():
# # if column in df.columns:
# # df[column] = values
# # else:
# # logger.warning(f"Column '{column}' not found in dataset.")
# # await self.push_to_hub(df, dataset_name)
# # return df
# except Exception as e:
# logger.error(f"Failed to update dataset: {e}")
# raise DatasetPushError(f"Failed to update dataset: {e}")
async def update_dataset(
self,
dataset_name: str,
updates: Dict[str, List],
target_column: str,
output_column: str = "embeddings",
) -> Optional[pd.DataFrame]:
"""Update a dataset on Hugging Face Hub by generating embeddings for new data and concatenating it with the existing dataset."""
try:
# Step 1: Load the existing dataset from Hugging Face Hub
logger.info(
f"Loading existing dataset from Hugging Face Hub: {dataset_name}..."
)
existing_ds = await self.read_dataset(dataset_name)
existing_df = pd.DataFrame(existing_ds)
# Step 2: Convert the new updates into a DataFrame
logger.info("Converting updates to DataFrame...")
new_df = pd.DataFrame(updates)
# Step 3: Generate embeddings for the new data
logger.info("Generating embeddings for the new data...")
embedding_service = get_embedding_service() # Get the embedding service
new_df = await embedding_service.create_embeddings(
new_df, target_column, output_column
)
# Step 4: Concatenate the existing DataFrame with the new DataFrame
logger.info("Concatenating existing dataset with new data...")
updated_df = pd.concat([existing_df, new_df], ignore_index=True)
# Step 5: Push the updated dataset back to Hugging Face Hub
logger.info(
f"Pushing updated dataset to Hugging Face Hub: {dataset_name}..."
)
await self.push_to_hub(updated_df, dataset_name)
# return updated_df
except Exception as e:
logger.error(f"Failed to update dataset: {e}")
raise DatasetPushError(f"Failed to update dataset: {e}")
async def delete_dataset(self, dataset_name: str) -> None:
"""Delete a dataset from Hugging Face Hub."""
try:
logger.info(f"Deleting dataset from Hugging Face Hub: {dataset_name}...")
self.hf_api.delete_repo(repo_id=dataset_name, repo_type="dataset")
logger.info(f"Dataset deleted from Hugging Face Hub: {dataset_name}")
except Exception as e:
logger.error(f"Failed to delete dataset: {e}")
raise DatasetDeleteError(f"Failed to delete dataset: {e}")
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