import streamlit as st
import pandas as pd
import requests

st.title("SuperKart Sales Prediction")

st.subheader("Online Prediction")
product_weight = st.number_input("Product Weight (kg)", min_value=0.0, value=10.0)
product_allocated_area = st.number_input("Allocated Shelf Area (sq m)", min_value=0.0, value=0.05)
product_mrp = st.number_input("Product MRP (INR)", min_value=1.0, value=100.0)
store_est_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2005)
product_sugar_content = st.selectbox("Sugar Content", ["Low Sugar", "No Sugar", "Regular"])
product_type = st.selectbox("Product Type", [
    "Dairy", "Canned", "Baking Goods", "Frozen Foods", "Health and Hygiene",
    "Snack Foods", "Soft Drinks", "Others"
])
store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
store_location = st.selectbox("Store Location Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", [
    "Supermarket Type1", "Supermarket Type2", "Supermarket Type3",
    "Grocery Store", "Food Mart", "Departmental Store"
])

input_data = pd.DataFrame([{
    "Product_Weight": product_weight,
    "Product_Allocated_Area": product_allocated_area,
    "Product_MRP": product_mrp,
    "Store_Establishment_Year": store_est_year,
    "Product_Sugar_Content": product_sugar_content,
    "Product_Type": product_type,
    "Store_Size": store_size,
    "Store_Location_City_Type": store_location,
    "Store_Type": store_type
}])

if st.button("Predict Sales"):
    response = requests.post(
        "https://dhani10-SuperKartSalesPredictionBackend.hf.space/v1/sales",
        json=input_data.to_dict(orient='records')[0]
    )
    if response.status_code == 200:
        prediction = response.json()['predicted_sales']
        st.success(f"Predicted Sales: ₹{round(prediction, 2)}")
    else:
        st.error(f"Error making prediction: {response.status_code} - {response.text}")

st.subheader("Batch Prediction")
uploaded_file = st.file_uploader("Upload a CSV file for batch prediction", type=["csv"])

if uploaded_file is not None:
    if st.button("Predict Batch"):
        response = requests.post(
            "https://dhani10-SuperKartSalesPredictionBackend.hf.space/v1/salesbatch",
            files={"file": uploaded_file}
        )
        if response.status_code == 200:
            predictions = response.json()
            st.success("Batch predictions completed!")
            st.dataframe(pd.DataFrame(predictions, columns=["Predicted Sales (INR)"]))
        else:
            st.error(f"Error making batch prediction: {response.status_code} - {response.text}")