import numpy as np
import pandas as pd
import streamlit as st
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from scipy import stats
import pickle

from sklearn.model_selection import train_test_split, cross_validate
import optuna
from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from optuna.samplers import TPESampler
from optuna.visualization import plot_param_importances,plot_optimization_history


from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, BaggingRegressor, VotingRegressor
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error


def load_model():
    with open("regression .pkl", "rb") as f:
        model = pickle.load(f)
    return model

model = load_model()

st.set_page_config(page_title="Walmart Sales Predictor", page_icon="🛒", layout="centered")

st.title("🛒 Walmart Sales Prediction")
st.markdown("""
### 📊 Predict Weekly Sales for Walmart Stores  
Just enter the store details below, and our AI model will predict the weekly sales! 💰
""")

st.title("🛒 Walmart Sales Prediction")
st.write("Enter the input features below to predict the weekly sales.")

# Store input (float64)
store = st.number_input("Enter Store ID (1-50)", min_value=1.0, max_value=50.0, step=1.0, format="%.1f")

# Holiday_Flag input (object, but should be categorical)
holiday_flag = st.selectbox("Is it a Holiday?", [0,1])

# Temperature input (float64)
temperature = st.number_input("Enter Temperature (°C)", value=20.0, format="%.2f")

# Fuel Price input (float64)
fuel_price = st.number_input("Enter Fuel Price", value=3.5, format="%.3f")

# CPI input (float64)
cpi = st.number_input("Enter CPI", value=200.0, format="%.6f")

# Unemployment input (float64)
unemployment = st.number_input("Enter Unemployment Rate", value=5.0, format="%.3f")

# Month input (float64)
month = st.number_input("Enter Month (1-12)", min_value=1.0, max_value=12.0, step=1.0, format="%.1f")


# Prediction button
if st.button("Predict Sales"):
    #input_features = [[store, holiday_flag, temperature, fuel_price, cpi, unemployment,month]]
    
    try:
        prediction = model.predict([[store, holiday_flag, temperature, fuel_price, cpi, unemployment, month]])
        prediction
        
        st.success(f"Predicted Weekly Sales: ${prediction[0]:,.2f}")
    except Exception as e:
        st.error(f"Error during prediction: {e}")