import os
import pandas as pd
import numpy as np
import json
from src.utils import Utils


class Reader:

    def __init__(self, config):
        self.config = config
        self.utils = Utils()
        self.cache_dir = config.get("cache_dir", "./cache")  # default cache directory

    def read(self, input_path=None, reader_config=None):
        # If reader_config is None, use the class-level config
        if reader_config is None:
            reader_config = self.config

        file_format = reader_config.get("format", None)
        input_path = input_path or reader_config.get("input_path", "")

        # Decide which method to use based on file format
        if file_format == "parquet":
            return self._read_dataframe_from_parquet(input_path, reader_config)
        elif file_format == "csv":
            return self._read_dataframe_from_csv(input_path)
        elif file_format == "s3_csv":
            return self._read_dataframe_from_csv_s3(input_path, reader_config)
        elif file_format == "json_folder":
            return self._read_json_files_to_dataframe(input_path)
        else:
            raise ValueError(f"Unsupported file format: {file_format}")

    def _read_dataframe_from_parquet(self, input_path=None, reader_config=None):
        if reader_config is None:
            reader_config = self.config

        input_path = input_path or reader_config.get("input_path", "")

        if input_path.startswith("s3://"):
            # Check if the file is cached
            local_cache_path = os.path.join(self.cache_dir, os.path.basename(input_path))

            if os.path.exists(local_cache_path):
                print("reading from cache")
                print(local_cache_path)
                return pd.read_parquet(local_cache_path)

            print("reading from s3")

            credentials = reader_config.get("credentials", {})
            storage_options = {
                'key': credentials.get("access_key_id", ""),
                'secret': credentials.get("secret_access_key", ""),
                'client_kwargs': {'endpoint_url': credentials.get("endpoint_url", "")}
            }

            # Read from S3 and cache locally
            df = pd.read_parquet(input_path, storage_options=storage_options)
            os.makedirs(self.cache_dir, exist_ok=True)  # Check and create if not exists
            df.to_parquet(local_cache_path)  # Save to cache
            return df
        else:
            return pd.read_parquet(input_path)

    def _read_dataframe_from_csv(self, file_path):
        return self.utils.read_dataframe_from_csv(file_path)

    def _read_json_files_to_dataframe(self, folder_path):
        self.utils.load_json_files_to_dataframe(folder_path)

    def _read_dataframe_from_csv_s3(self, input_path, reader_config):
        credentials = reader_config.get("credentials", {})
        endpoint_url = credentials.get("endpoint_url", "")
        access_key_id = credentials.get("access_key_id", "")
        secret_access_key = credentials.get("secret_access_key", "")

        # Constructing the storage options for s3fs
        storage_options = {
            'key': access_key_id,
            'secret': secret_access_key,
            'client_kwargs': {'endpoint_url': endpoint_url}
        }

        # Use pandas to read the CSV file directly from S3
        try:
            df = pd.read_csv(input_path, storage_options=storage_options)
            return df
        except Exception as e:
            print(f"An error occurred while reading the CSV file from S3: {e}")
            return None