app.py

import streamlit as st
import pandas as pd
import requests

st.title("Sales Total Price Prediction")

st.subheader("Single Prediction")

# Single-Prediction Inputs
# Store_Id and Product_Id are excluded from input as they are only required for identification, not useful for one prediction
product_weight = st.number_input("Product Weight", step=0.5)
product_sugar_content = st.selectbox("Product Sugar Content", ["No Sugar", "Low Sugar", "Regular"])
product_allocated_area = st.number_input("Product Allocated Area", step=0.05)
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", step=0.5)
store_establishment_year = int(st.number_input("Store Establishment Year", step=1))
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", ["Departmental Store", "Food Mart", "Supermarket Type1", "Supermarket Type2"])

if st.button("Predict"):
  with st.spinner("Predicting"):
    response = requests.post("https://UTAIML-SalesTotalPredictionBackend.hf.space/api/total", json=
                              {
                                "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_Establishment_Year": store_establishment_year,
                                "Store_Size": store_size,
                                "Store_Location_City_Type": store_location_city_type,
                                "Store_Type": store_type
                              }
                            )

    if response.status_code == 200:
      # Get and output prediction
      prediction = response.json()["Prediction"]
      st.success(f"Prediction: {prediction}")
    else:
      st.error("Something went wrong!")


# Batch Prediction
st.subheader("Batch Prediction")
st.subheader("Accepting Product_Id, Store_Id, and all single-prediction keys.")

# Get CSV file
csv_file = st.file_uploader("Upload CSV file", type=["csv"])

if csv_file is not None:
  if st.button("Batch Predict"):
    with st.spinner("Predicting"):
      response = requests.post("https://UTAIML-SalesTotalPredictionBackend.hf.space/api/totals", files={"file": csv_file})
      if response.status_code == 200:
        # Get and output predictions
        predictions = response.json()
        st.success("Completed Batch Predictions!")
        st.write(predictions)
      else:
        st.error("Something went wrong!")