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import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("Store Total 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=4.0, max_value=22.0, step=0.1, value=5.0)
product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar", "reg"])
product_allocated_area = st.number_input("Product Allocated Area", min_value=0.004, max_value=0.298000, step=0.1, value=0.01)
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", min_value=31.0, max_value=266.0, step=5.0, value=50.0)
store_id = st.selectbox("Store Id ", ["OUT001", "OUT002", "OUT003", "OUT004"])
store_establishment_year = st.selectbox("Store Establishment Year ", ["1987", "1998", "1999", "2009"])
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 = {
'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://karora1804-StoreTotalSalesPredictionBackend.hf.space/v1/storeSales", json=input_data) # Send data to Flask API
if response.status_code == 200:
prediction = response.json()['Predicted_Store_Total_Sales']
st.success(f"Predicted Store Total Sales: {prediction}")
else:
st.error("Error making prediction.")
st.write(response.status_code)
st.write(response.text)
# Section for batch prediction
st.subheader("Batch Prediction")
# Allow users to upload a CSV file for batch prediction
uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
# Make batch prediction when the "Predict Batch" button is clicked
if uploaded_file is not None:
if st.button("Predict Batch"):
response = requests.post("https://karora1804-StoreTotalSalesPredictionBackend.hf.space/v1/storeSalesbatch", files={"file": uploaded_file}) # Send file to Flask API
if response.status_code == 200:
predictions = response.json()
st.success("Batch predictions completed!")
st.write(predictions) # Display the predictions
else:
st.error("Error making batch prediction.")
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