%%writefile frontend_files/app.py
import streamlit as st
import pandas as pd
import requests

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

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

# Collect user input for product/store features
Product_Weight = st.number_input("Product Weight (kg)", min_value=0.0, value=12.5)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular", "reg"])
Product_Allocated_Area = st.number_input("Allocated Shelf Area", min_value=0.0, value=0.05)
Product_Type = st.selectbox("Product Type", [
    "Fruits and Vegetables", "Dairy", "Canned", "Baking Goods",
    "Snack Foods", "Health and Hygiene", "Household", "Frozen Foods", "Meat", "Soft Drinks", "Breads", "Hard Drinks", "Starchy Foods", "Breakfast", "Seafood"
])
Product_MRP = st.number_input("Product MRP (₹)", min_value=0.0, value=150.0)
Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
Store_Location_City_Type = st.selectbox("City Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Food Mart", "Departmental Store"])
Store_Establishment_Year = st.slider("Store Establishment Year", min_value=1987, max_value=2025, value=2005)
Store_Age = 2025 - Store_Establishment_Year

# 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,
    'Store_Type': Store_Type,
    'Store_Age': Store_Age
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post(
        "https://PStark-SuperKartSalesPrediction-backend.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 Product 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 a CSV file for batch sales prediction", type=["csv"])

if uploaded_file is not None:
    if st.button("Predict Batch"):
        response = requests.post(
            "https://PStark-SuperKartSalesPrediction.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("Error making batch prediction.")