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d64786c 006dc02 2000d01 d64786c 0524c57 d64786c 36ad85f 87d8fcf d64786c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("Super Kart Store Total Sale 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=4.0)
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.004, max_value=0.298, step=0.01, value=0.004)
Product_MRP = st.number_input("Product MRP", min_value=31.0, max_value=266.0, step=0.5, value=31.0)
Store_Establishment_Year = st.selectbox("Store Establishment Year", [1987, 1998, 1999, 2009])
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
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", "Starchy Foods", "Breakfast", "Seafood", "Others"
])
Store_Size = st.selectbox("Store Size", ["Medium", "High", "Small"])
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"
])
# 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_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://hkbindhu-superkart.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
if response.status_code == 200:
prediction = response.json()['Predicted Sales Total (in dollars)']
st.success(f"Predicted Sales Total (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://hkbindhu-superkart.hf.space/v1/salesbatch", 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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