import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("Superkart Price Prediction")

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

# Collect user input for property features
product_weight = st.number_input("Product Weight", min_value=0.0)
product_sugar_content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0)
product_type = st.selectbox("Product Type", ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Snack Foods', 'Meat', 'Household', 'Hard Drinks', 'Fruits and Vegetables', 'Breads', 'Soft Drinks', 'Breakfast', 'Others', 'Starchy Foods', 'Seafood'])
product_mrp = st.number_input("Product MRP", min_value=0.0)
store_size = st.selectbox("Store Size", ['Medium', 'High', 'Small'])
store_location_city_type = st.selectbox("Store Location City Type", ['Tier 2', 'Tier 1', 'Tier 3'])
age_category = st.selectbox("Age_Category", ['0to20', '21to30', '31to50'])
type_of_food = st.selectbox("type of food", ['Perishable', 'Non-Consumables', 'Non-Perishable'])

# Convert user input into a 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_size': store_size,
    'store_location_city_type': store_location_city_type,
    'age_category': age_category,
    'type_of_food': type_of_food
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://RedRooster99-projectbackend.hf.space/v1/superkart", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()['Predicted Price']
        st.success(f"Superkart Price: {prediction}")
    else:
        st.error("Error making prediction.")