import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("Super Kart Prediction")

# Section for online prediction
st.subheader("Online Prediction")

# Collect user input for property features
Product_Id = st.number_input("Product Id")
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar","Regular","No Sugar","reg"])
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"])
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","Supermarket Type1","Departmental Store","Food Mart"])
Product_MRP = st.number_input("Product MRP", min_value=1,  step=0.01, value=2)
Product_Weight = st.number_input("Product Weight", min_value=1,  step=0.01, value=2)


# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'Product_Id': Product_Id,
    'Product_Sugar_Content': Product_Sugar_Content,
    'Product_Type': Product_Type,
    'Store_Size': Store_Size,
    'Store_Location_City_Type': Store_Location_City_Type,
    'Store_Type': Store_Type,
    'Product_MRP': Product_MRP,
    'Product_Weight': Product_Weight
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://ankitgoyal022/GT-space.hf.space/v1/superKartRevenuebatch", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()['Total Sales']
        st.success(f"Predicted Rental Price (in dollars): {prediction}")
    else:
        st.error("Error making prediction.")

# Section for batch prediction
st.subheader("Batch Prediction")

# Allow users to upload a CSV file for batch prediction
uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])

# Make batch prediction when the "Predict Batch" button is clicked
if uploaded_file is not None:
    if st.button("Predict Batch"):
        response = requests.post("https://ankitgoyal022/GT-space.hf.space/v1/superKartRevenuebatch", files={"file": uploaded_file})  # Send file to Flask API
        if response.status_code == 200:
            predictions = response.json()
            st.success("Batch predictions completed!")
            st.write(predictions)  # Display the predictions
        else:
            st.error("Error making batch prediction.")