app.py

import streamlit as st
import pandas as pd
import requests

st.title("SuperKart Store Sales Prediction")
st.subheader("Online Prediction")

# Collect user input
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 Sugar", "Regular", "No Sugar"])
product_type = st.selectbox("Product Type", [
    "Frozen Foods", "Dairy", "Canned", "Baking Goods", "Health and Hygiene", "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", [
    "Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"
])
store_id = st.text_input("Store Id", "OUT001")

# Prepare payload for backend
input_data = {
    "Product_Weight": product_weight,
    "Product_Allocated_Area": product_allocated_area,
    "Product_MRP": product_mrp,
    "Store_Establishment_Year": store_establishment_year,  # ✅ backend will convert to 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
}

# Prediction button
if st.button("Predict"):
    try:
        response = requests.post(
            "https://disha252001-sales-prediction-backend-final.hf.space/v1/sales",
            json=input_data
        )
        if response.status_code == 200:
            prediction = response.json()["Predicted Store Sales"]
            st.success(f"Predicted Store Sales: {prediction}")
        else:
            st.error(f"Error {response.status_code}: Could not get prediction. Check backend logs.")
    except Exception as e:
        st.error(f"Request failed: {e}")

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

if uploaded_file is not None:
    if st.button("Predict Batch"):
        try:
            response = requests.post(
                "https://disha252001-sales-prediction-backend-final.hf.space/v1/salesbatch",
                files={"file": uploaded_file}
            )
            if response.status_code == 200:
                predictions = response.json()
                st.success("Batch predictions completed!")
                st.write(predictions)
            else:
                st.error(f"Error {response.status_code}: Could not get batch prediction.")
        except Exception as e:
            st.error(f"Batch request failed: {e}")