Spaces:
Sleeping
Sleeping
File size: 3,147 Bytes
a7edfbc |
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("SuperKart Sales Prediction")
# Section for online prediction
st.subheader("Online Prediction")
# Collect user input for property features
Product_Id=st.text_input("Product_Id")
Product_Weight=st.number_input("Product_Weight", min_value=0.0, step=0.1, value=1.0)
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, step=0.1, value=1.0)
Product_Type=st.selectbox("Product_Type", ["Baking Goods", "Breads", "Breakfast", "Canned", "Diary", "Frozen Foods", "Fruits and Vegetables", "Hard Drinks","Health and Hygiene", "Household"," Meat", "Others", "Seafood", "Snack Foods", "Soft Drinks", "Starchy Foods"])
Product_MRP=st.number_input("Product_MRP", min_value=0.0, step=0.1, value=1.0)
Store_Id=st.selectbox("Store_Id", ["OUT001", "OUT002", "OUT003", "OUT004"])
Store_Establishment_Year=st.number_input("Store_Establishment_Year", min_value=0, step=1, value=1980)
Store_Size=st.selectbox("Store_Size", ["High", "Medium", "Small"])
Store_Location_City_Type=st.selectbox("Store_Location_City_Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type=st.selectbox("Store_Type", ["Supermarket Type1", "Grocery Store", "Departmental Store", "Food Mart", "Supermarket Type2"])
# Convert user input into a DataFrame
input_data = pd.DataFrame([{
'Product_Id': Product_Id,
'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://crdeepa-SuperKartPredictionBackend.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 (in dollars)']
st.success(f"Predicted Sales (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://crdeepa-SuperKartPredictionBackend.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.")
|