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import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("Product Store Sales Prediction")
# Section for online prediction
st.subheader("Online Prediction")
# Collect user input for property features
Product_Weight = st.number_input("Product Weight", min_value=4, value =12)
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.004, value=0.056)
Product_MRP = st.number_input("Product MRP", min_value=31, step=1, value=146)
Store_Establishment_Year = st.number_input("Store Establishment Year", value=2009)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular","No Sugar","reg"])
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"])
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"])
# Convert user input into a DataFrame
input_data = pd.DataFrame([{
'Product_Weight': Product_Weight,
'Product_Allocated_Area': Product_Allocated_Area,
'Product_MRP': Product_MRP,
'Store_Establishment_Year': Store_Establishment_Year,
'Product_Sugar_Content': Product_Sugar_Content,
'Product_Type': Product_Type,
'Store_Location_City_Type': Store_Location_City_Type,
'Store_Size': Store_Size,
'Store_Type': Store_Type,
}])
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
response = requests.post("https://wash9968-ProductStoreSalesPredictionBackend.hf.space//v1/productstoresalesprediction", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
if response.status_code == 200:
prediction = response.json()['Predicted Price (in dollars)']
st.success(f"Predicted Product Store Sales: {prediction}")
else:
st.error("Error making prediction.")
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