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import streamlit as st
import requests
st.title("SuperKart Sales Prediction App")
# Input fields for product and store data
Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)
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=10.0
)
Product_MRP = st.number_input(
"Product MRP",
min_value=0.0,
value=100.0
)
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",
["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"]
)
Product_Id_char = st.text_input(
"Product ID Character",
value="P001"
)
Store_Age_Years = st.number_input(
"Store Age (Years)",
min_value=0,
value=10
)
Product_Type_Category = st.selectbox(
"Product Type Category",
["Food", "Drinks", "Household", "Health", "Other"]
)
product_data = {
"Product_Weight": Product_Weight,
"Product_Sugar_Content": Product_Sugar_Content,
"Product_Allocated_Area": Product_Allocated_Area,
"Product_MRP": Product_MRP,
"Store_Size": Store_Size,
"Store_Location_City_Type": Store_Location_City_Type,
"Store_Type": Store_Type,
"Product_Id_char": Product_Id_char,
"Store_Age_Years": Store_Age_Years,
"Product_Type_Category": Product_Type_Category
}
if st.button("Predict", type='primary'):
response = requests.post(
"https://scottizi-greatlearningdeploymentproject.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")
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