Upload streamlit_app.py
Browse files- streamlit_app.py +48 -0
streamlit_app.py
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import streamlit as st
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import requests
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API_URL = "https://nohafx-superkart-predictor.hf.space/predict"
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st.title("π SuperKart Product Sales Predictor")
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st.write("Enter product and store details to predict sales.")
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product_weight = st.number_input("Product Weight", min_value=0.0, value=12.5)
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product_sugar = st.selectbox("Product Sugar Content", ["Low Fat", "Regular", "Low Sugar"])
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product_area = st.number_input("Product Allocated Area", min_value=0.0, max_value=1.0, value=0.05)
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product_type = st.selectbox("Product Type", [
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"Snack Foods", "Dairy", "Fruits and Vegetables",
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"Frozen Foods", "Household", "Baking Goods",
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"Canned", "Health and Hygiene", "Soft Drinks",
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"Meat", "Breads", "Hard Drinks", "Others",
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"Starchy Foods", "Breakfast", "Seafood"
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])
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product_mrp = st.number_input("Product MRP", min_value=0.0, value=150.75)
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store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
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store_location = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
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store_type = st.selectbox("Store Type", [
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"Supermarket Type1", "Supermarket Type2",
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"Supermarket Type3", "Grocery Store"
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])
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store_age = st.number_input("Store Age (Years)", min_value=0, value=15)
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if st.button("π Predict Sales"):
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input_data = {
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"Product_Weight": product_weight,
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"Product_Sugar_Content": product_sugar,
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"Product_Allocated_Area": product_area,
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"Product_Type": product_type,
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"Product_MRP": product_mrp,
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"Store_Size": store_size,
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"Store_Location_City_Type": store_location,
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"Store_Type": store_type,
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"Store_Age": store_age
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}
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try:
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response = requests.post(API_URL, json=input_data)
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if response.status_code == 200:
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prediction = response.json()["prediction"]
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st.success(f"β
Predicted Sales: **${prediction:,.2f}**")
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else:
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st.error(f"β Error: {response.text}")
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except Exception as e:
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st.error(f"β Connection Error: {str(e)}")
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