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import streamlit as st
import requests
st.title("Super Kart Sales Predictor")
# Input fields for product and store data
Product_Weight = st.slider("Product Weight", min_value=0.0, value=12.0, max_value = 22.0)
Product_MRP = st.slider("Product MRP", min_value=30.0, value=145.0, max_value=260.0)
Product_Allocated_Area = st.slider("Product Allocated Area", min_value=0.0, value=0.05, max_value=0.3)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
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", ["Departmental Store ", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
Store_Age_Years = st.slider("Store Age (Years)", min_value=0, value=15, max_value=40)
Product_Id_prefix = st.selectbox("Product ID Prefix", ["FD", "DR", "NC"])
Product_FD_perishable = st.selectbox("Product FD Perishable flag", ["Perishables", "Non Perishables"])
product_data = {
"Product_Weight": Product_Weight,
"Product_MRP": Product_MRP,
"Product_Allocated_Area": Product_Allocated_Area,
"Product_Sugar_Content": Product_Sugar_Content,
"Store_Size": Store_Size,
"Store_Location_City_Type": Store_Location_City_Type,
"Store_Type": Store_Type,
"Store_Age_Years": Store_Age_Years,
"Product_Id_prefix": Product_Id_prefix,
"Product_FD_perishable": Product_FD_perishable,
}
# Make prediction when the "Predict" button is clicked
if st.button("Predict", type='primary'):
response = requests.post("https://AlbertoNuin-SuperKartBackend.hf.space/v1/predict", json=product_data)
if response.status_code == 200:
result = response.json()
predicted_sales = result["Sales"]
st.write(f"Predicted Product Store Sales Total: {predicted_sales:.2f}")
else:
st.error("Error in API request")
# 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://AlbertoNuin-SuperKartBackend.hf.space/v1/batch", 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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