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import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("Superkart Revenue Prediction")
# Section for online prediction
st.subheader("Online Prediction")
# Collect user input for property features
product_weight = st.number_input("Product_Weight", min_value=0.0, max_value=1000.0, step=0.1, value=12.66)
product_sugar_content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "Regular", "No Sugar"])
product_allocated_area = st.number_input("Product_Allocated_Area", min_value=0.0, max_value=1.0, step=0.001, value=0.027)
product_type = st.selectbox("Product_Type", ["Frozen Foods", "Dairy", "Canned", "Baking Goods", "Health and Hygiene", "Snack Foods"])
product_mrp = st.number_input("Product_MRP", min_value=0.0, max_value=1000.0, step=0.1, value=117.08)
store_id = st.text_input("Store_Id", ["OUT001", "OUT002", "OUT003", "OUT004"])
store_establishment_year = st.number_input("Store_Establishment_Year", min_value=1900, max_value=2027, step=1, value=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"])
#product_store_sales_total = st.number_input("Product_Store_Sales_Total", min_value=0.0, max_value=10000.0, step=0.1, value=2842.4)
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_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://<username>-<repo_id>.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 (in dollars)']
st.success(f"Predicted Rental Revenue (in dollars): {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 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://<username>-<repo_id>.hf.space/v1/revenuebatch", 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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