import pandas as pd
from sklearn.linear_model import LinearRegression
import numpy as np

def predict_expense(df):
    if len(df) < 5:
        return {"error": "Not enough data"}

    df['date'] = pd.to_datetime(df['date'])
    df['day'] = df['date'].dt.day
    df['month'] = df['date'].dt.month

    X = df[['day', 'month']]
    y = df['amount']

    model = LinearRegression()
    model.fit(X, y)

    future = pd.DataFrame({
        "day": [df['day'].max() + i for i in range(1, 8)],
        "month": [df['month'].iloc[-1]] * 7
    })

    preds = model.predict(future)

    return {
        "next_7_days": float(np.sum(preds)),
        "daily_prediction": preds.tolist()
    }