import pandas as pd
import json
import os

DATA_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'data')

def load_sales_data():
    path = os.path.join(DATA_DIR, 'sales_data.csv')
    if not os.path.exists(path):
        raise FileNotFoundError(f"{path} not found.")
    df = pd.read_csv(path)
    df['Date'] = pd.to_datetime(df['Date'])
    return df

def load_web_logs():
    path = os.path.join(DATA_DIR, 'web_logs.json')
    if not os.path.exists(path):
        raise FileNotFoundError(f"{path} not found.")
    with open(path, 'r') as f:
        data = json.load(f)
    df = pd.DataFrame(data)
    df['timestamp'] = pd.to_datetime(df['timestamp'])
    return df

def load_reviews():
    path = os.path.join(DATA_DIR, 'customer_reviews.csv')
    if not os.path.exists(path):
        raise FileNotFoundError(f"{path} not found.")
    df = pd.read_csv(path)
    df['Date'] = pd.to_datetime(df['Date'])
    return df

def get_integrated_data():
    """
    Simulates integration by merging Sales and Reviews on Product?
    Or mostly just providing a unified access point.
    Returns a dictionary of dataframes.
    """
    sales = load_sales_data()
    logs = load_web_logs()
    reviews = load_reviews()
    
    return {
        'sales': sales,
        'logs': logs,
        'reviews': reviews
    }

def clean_sales_data(df):
    # Example cleaning: Remove transactions with 0 or negative quantity/price (not expected in synthetic but good for "Real" scenario)
    df = df[(df['Quantity'] > 0) & (df['UnitPrice'] > 0)]
    return df