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0fb7cf4 | 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 66 67 68 69 70 | import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("Super Kart Sales forecast 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, value=100.0)
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, value=1.0)
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")
Store_Id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1987, value=2009)
Store_Size = st.selectbox("Store Size", ["Small", "Medium", "Large"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
# Convert user input into a DataFrame
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://Krishna6559-SuperKartPredictionBackend.hf.space/v1/saletotal", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
if response.status_code == 200:
prediction = response.json()['Predicted total revenue generated by the sale']
st.success(f"Predicted Sale Total Price: {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://Krishna6559-SuperKartPredictionBackend.hf.space/v1/saletotalbatch", 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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