import pandas as pd
import requests
import streamlit as st

st.title('SuperKart Sale Prediction')

# Inputs for prediction
Product_Weight = st.number_input('Product_Weight', value=4.0)
Product_Sugar_Content = st.selectbox('Product_Sugar_Content', ['No Sugar', 'Low Sugar', 'Regular', 'reg'], index=0)
Product_Allocated_Area = st.number_input('Product_Allocated_Area', value=0.004)
Product_Type = st.selectbox('Product_Type', ['Household', 'Soft Drinks', 'Fruits and Vegetables',
                                             'Baking Goods', 'Meat', 'Dairy', 'Canned', 'Snack Foods',
                                             'Frozen Foods', 'Health and Hygiene', 'Breads', 'Hard Drinks',
                                             'Others', 'Starchy Foods', 'Breakfast', 'Seafood'], index=0)
Product_MRP = st.number_input('Product_MRP', value=31.00)
Store_Id = st.selectbox('Store_Id', ['OUT001', 'OUT003', 'OUT004', 'OUT002'], index=0)
Store_Size = st.selectbox('Store_Size', ['Small', 'Medium', 'High'], index=0)
Store_Location_City_Type = st.selectbox('Store_Location_City_Type', ['Tier 1', 'Tier 2', 'Tier 3'], index=1)
Store_Type = st.selectbox('Store_Type', ['Supermarket Type1', 'Departmental Store', 'Supermarket Type2', 'Food Mart'], index=0)



# Create input data as DataFrame
input_data = pd.DataFrame([{
    'Product_Weight': Product_Weight,
    'Product_Sugar_Content': Product_Sugar_Content,
    'Product_Allocated_Area': Product_Allocated_Area,
    'Product_Type': Product_Type,
    'Product_MRP': Product_MRP,
    'Store_Id': Store_Id,
    'Store_Size': Store_Size,
    'Store_Location_City_Type': Store_Location_City_Type,
    'Store_Type': Store_Type,

}])

# Single prediction
if st.button('Predict'):
    response = requests.post(
        'https://Andrew2505-SKmodel.hf.space/v1/superkart_single',
        json = input_data.to_dict(orient="records")[0]
    )
    if response.status_code == 200:
        prediction = response.json()
        st.success(f"Predicted Sale: {prediction['Sale']}")
    else:
        st.error(f"Error making prediction: {response.text}")

# Batch prediction
st.subheader('Batch Prediction')
uploaded_file = st.file_uploader('Upload a CSV file', type=['csv'])
if uploaded_file is not None:
    if st.button('Predict Batch'):
        response = requests.post(
            'https://Andrew2505-Skmodel.hf.space/v1/superkart_batch',
            files={'file': uploaded_file}
        )
        if response.status_code == 200:
            predictions = response.json()
            st.success("Batch predictions completed!")
            st.json(predictions)
        else:
            st.error(f"Error making batch prediction: {response.text}")