app.py

import streamlit as st
import pandas as pd
import requests

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

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

# Collect user input for property features
Product_Id = st.text_input("Product Id")
Product_Weight = st.number_input("Product Weight", min_value=0.0)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0)
Product_Type = st.selectbox("Product Type", ["Fruits and Vegetables", "Snack Foods","Frozen Foods","Dairy",
                                             "Household","Baking Goods","Canned","Health and Hygiene",
                                             "Meat","Soft Drinks","Breads","Hard Drinks","Others",
                                             "Starchy Foods","Breakfast","Seafood"])
Product_MRP = st.number_input("Product MRP", min_value=0.0)
Store_Id = st.text_input("Store Id")
Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=0)
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 Type2", "Supermarket Type1", "Departmental Store","Food Mart"])


# Convert user input into a DataFrame
input_data = pd.DataFrame([{'Product_Id': Product_Id,
        '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_Id': Store_Id,
        'Store_Establishment_Year': Store_Establishment_Year,
        'Store_Size': Store_Size,
        'Store_Location_City_Type': Store_Location_City_Type,
        'Store_Type': Store_Type}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://pragmat-SalesRevenuePredictionBackend.hf.space/v1/revenue", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()['predicted_revenue']
        st.success(f"Predicted Sales Revenue (in dollars): {prediction}")
    else:
        st.error("Error making prediction.")