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e869b32 | 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 | import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("Super Kart Prediction")
# Section for online prediction
st.subheader("Online Prediction")
# Collect user input for property features
Product_Id = st.number_input("Product Id")
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", ["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", ["Supermarket Type2","Supermarket Type1","Departmental Store","Food Mart"])
Product_MRP = st.number_input("Product MRP", min_value=1, step=0.01, value=2)
Product_Weight = st.number_input("Product Weight", min_value=1, step=0.01, value=2)
# Convert user input into a DataFrame
input_data = pd.DataFrame([{
'Product_Id': Product_Id,
'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,
'Product_MRP': Product_MRP,
'Product_Weight': Product_Weight
}])
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
response = requests.post("https://ankitgoyal022/GT-space.hf.space/v1/superKartRevenuebatch", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
if response.status_code == 200:
prediction = response.json()['Total Sales']
st.success(f"Predicted Rental Price (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://ankitgoyal022/GT-space.hf.space/v1/superKartRevenuebatch", 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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