import streamlit as st
import pandas as pd
import requests

# Streamlit UI for SuperKart Outlet Sales Revenue Prediction
st.title("SuperKart Outlet Sales Revenue Predictor")
st.write("This app generates a forecast for the total store sales revenue of its outlets for the upcoming quarter.")
st.write("Please enter the product and store details below to get a prediction.")

# Collect user input
st.subheader("Product Details")
Product_Id = st.text_input("Product ID", "FD6114")
Product_Weight = st.slider("Product Weight", 4.0, 22.0, 12.66, 0.01)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
Product_Allocated_Area = st.slider("Product Allocated Area", 0.00, 0.30, 0.027, 0.001)
Product_Type = st.selectbox("Product Type", [
    'Fruits and Vegetables', 'Snack Foods', 'Frozen Foods', 'Dairy',
    'Household', 'Baking Goods', 'Canned', 'Health and Hygiene',
    'Meat', 'Soft Drinks', 'Breads', 'Hard Drinks', 'Others',
    'Starchy Foods', 'Breakfast', 'Seafood'
])
Product_MRP = st.slider("Product MRP (Maximum Retail Price)", 30.0, 270.0, 117.08, 0.01)

st.subheader("Store Details")
Store_Id = st.text_input("Store ID", "OUT004")
Store_Establishment_Year = st.slider("Store Establishment Year", 1980, 2010, 2009, 1)
Store_Size = st.selectbox("Store Size", ['Medium', 'High', 'Small'])
Store_Location_City_Type = st.selectbox("Store Location City Type", ['Tier 1', 'Tier 2', 'Tier 3'])
Store_Type = st.selectbox("Store Type", ['Supermarket Type2', 'Departmental Store', 'Supermarket Type1', 'Food Mart'])

# Create input DataFrame
input_data = {
    'Product_Id': Product_Id,
    '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_Establishment_Year': Store_Establishment_Year,
    'Store_Size': Store_Size,
    'Store_Location_City_Type': Store_Location_City_Type,
    'Store_Type': Store_Type
}

if st.button("Predict Sales Revenue", type='primary'):
    response = requests.post("https://chandrachurhghosh-Backend.hf.space/v1/outlet", json=input_data)
    if response.status_code == 200:
        result = response.json()
        predicted_sales = result["Predicted_Product_Store_Sales_Total"]
        st.success(f"💰 Predicted Product-Store Sales Total: **{predicted_sales:.2f}**")
    else:
        st.error(f"Error in API request: {response.status_code} - {response.text}")

# Batch Prediction
st.subheader("Batch Prediction")

file = st.file_uploader("Upload CSV file for Batch Prediction", type=["csv"])
if file is not None:
    if st.button("Predict for Batch", type='primary'):
        response = requests.post("https://chandrachurhghosh-Backend.hf.space/v1/outletbatch", files={"file": file})
        if response.status_code == 200:
            result = response.json()
            st.header("Batch Prediction Results")
            # Convert list of dicts to DataFrame for better display
            df_results = pd.DataFrame(result)
            st.dataframe(df_results)
        else:
            st.error(f"Error in API request for batch prediction: {response.status_code} - {response.text}")