import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("SuperKart Store Sales Prediction")

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

# Collect user input for store/product features
product_weight = st.number_input("Product Weight", min_value=0.0, value=1.0)
product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0, value=10.0)
product_mrp = st.number_input("Product MRP", min_value=0.0, value=50.0)
store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2015)
product_sugar_content = st.selectbox("Product Sugar Content", ["Low", "Medium", "High"])
product_type = st.selectbox("Product Type", ["Dairy", "Beverages", "Snacks", "Others"])
store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
store_location_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", ["Type 1", "Type 2", "Type 3", "Type 4"])
store_id = st.text_input("Store Id", "S001")

# Feature engineering for Store_Age
store_age = 2025 - store_establishment_year

# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'Product_Weight': product_weight,
    'Product_Allocated_Area': product_allocated_area,
    'Product_MRP': product_mrp,
    'Store_Age': store_age,
    '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,
    'Store_Id': store_id
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post(
        "https://Disha252001-SuperKart-Frontend.hf.space/v1/sale",
        json=input_data.to_dict(orient='records')[0]
    )
    if response.status_code == 200:
        prediction = response.json()['Predicted Store Sales']
        st.success(f"Predicted Store Sales: {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://Disha252001-SuperKart-Frontend.hf.space/v1/salebatch",
            files={"file": uploaded_file}
        )
        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.")