import pandas as pd
import sqlite3
import csv
import json
import time

def is_file_done_saving(file_path):
    try:
        with open(file_path, 'r') as f:
            contents = f

        if contents:
            return True
        else:
            return False
    except PermissionError:
        return False

def get_delimiter(file_path, bytes = 4096):
    sniffer = csv.Sniffer()
    data = open(file_path, "r").read(bytes)
    delimiter = sniffer.sniff(data).delimiter
    return delimiter

def read_file(file):
    if file.endswith(('.csv', '.tsv', '.txt')) :
        df = pd.read_csv(file, sep=get_delimiter(file))
    elif file.endswith('.json'):
        with open(file, 'r') as f:
            contents = json.load(f)
        df = pd.json_normalize(contents)
    elif file.endswith('.ndjson'):
        with open(file, 'r') as f:
            contents = f.read()
        data = [json.loads(str(item)) for item in contents.strip().split('\n')]
        df = pd.json_normalize(data)
    elif file.endswith('.xml'):
        df = pd.read_xml(file)
    elif file.endswith(('.xls','xlsx')):
        df = pd.read_excel(file)
    else:
        raise ValueError(f'Unsupported filetype: {file}')
    return df

def process_data_upload(data_file, session_hash):
    total_time = 0
    while not is_file_done_saving(data_file):
        total_time += .5
        time.sleep(.5)
        if total_time > 10:
            break
        
    df = read_file(data_file)

    # Read each sheet and store data in a DataFrame
    #data = df.parse(sheet_name)
    # Process the data as needed
    # ...
    df.columns = df.columns.str.replace(' ', '_')
    df.columns = df.columns.str.replace('/', '_')

    for column in df.columns:
        if "date" in column.lower() or "time" in column.lower():
            df[column] = pd.to_datetime(df[column])
        if df[column].dtype == 'object' and isinstance(df[column].iloc[0], list):
            df[column] = df[column].explode()

    
    connection = sqlite3.connect(f'data_source_{session_hash}.db')
    print("Opened database successfully");
    print(df.columns)

    df.to_sql('data_source', connection, if_exists='replace', index = False)
    
    connection.commit()
    connection.close()