| import requests
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| import streamlit as st
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| import pandas as pd
|
|
|
| st.title("SuperKart Sales Prediction")
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|
|
|
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| st.subheader("Online Sales Prediction")
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|
|
|
|
| Product_Id = st.text_input("Product ID", max_chars=10, value='FD007')
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| Product_Weight = st.number_input("Product Weight", min_value=1.0, max_value=50.0, value=12.6)
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| Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.003, max_value=0.5, value=0.05)
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| Product_MRP = st.number_input("Product MRP", min_value=1.0, max_value=500.0, value=146.0)
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| Product_Type = st.selectbox("Product Type", ['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Snack Foods', 'Meat',
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| 'Household', 'Hard Drinks', 'Fruits and Vegetables', 'Breads', 'Soft Drinks', 'Breakfast',
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| 'Others', 'Starchy Foods', 'Seafood'])
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| Store_Id = st.selectbox("Store ID", ['OUT004', 'OUT003', 'OUT001', 'OUT002'])
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| Store_Establishment_Year = st.selectbox("Store Establishment Year", ['2009', '1999', '1987', '1998'])
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| Store_Type = st.selectbox("Store Type", ['Supermarket Type2', 'Departmental Store', 'Supermarket Type1', 'Food Mart'])
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| Item_Type = st.selectbox("Item Type", ['Food', 'Non_Consumables', 'Drink'])
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| Product_Sugar_Content = st.selectbox("Product Sugar Content", ['Low Sugar', 'Regular', 'No Sugar'])
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| Store_Size = st.selectbox("Store Size", ['Medium', 'High', 'Small'])
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| Store_Location_City_Type = st.selectbox("Store Location City Type", ['Tier 2', 'Tier 1', 'Tier 3'])
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|
|
| product_data = {
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| 'Product_Weight': Product_Weight,
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| 'Product_Allocated_Area': Product_Allocated_Area,
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| 'Product_MRP': Product_MRP,
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| 'Product_Type': Product_Type,
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| 'Store_Id': Store_Id,
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| 'Store_Establishment_Year': Store_Establishment_Year,
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| 'Store_Type': Store_Type,
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| 'Item_Type': Item_Type,
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| 'Product_Sugar_Content': Product_Sugar_Content,
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| 'Store_Size': Store_Size,
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| 'Store_Location_City_Type': Store_Location_City_Type
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| }
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|
|
| if st.button("Predict", type='primary'):
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| response = requests.post("https://subhash33-flask-superkartsales-backend.hf.space/v1/product", json=product_data)
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|
|
| if response.status_code == 200:
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| result = response.json()
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| sales_prediction = result["Prediction"]
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| st.write(f"Based on the information provided, the sales of the product {Product_Id} for the store {Store_Id} is likely going to be \
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| {sales_prediction} and the number of products needed will be {int(round(sales_prediction/Product_MRP, 0))}.")
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| else:
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| st.error("Error in API request")
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|
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|
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| st.subheader("Batch Prediction")
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|
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| file = st.file_uploader("Upload CSV file", type=["csv"])
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| if file is not None:
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| if st.button("Predict for Batch", type='primary'):
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| response = requests.post("https://subhash33-flask-superkartsales-backend.hf.space/v1/productbatch", files={"file": file})
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| if response.status_code == 200:
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| result = response.json()
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| st.header("Batch Prediction Results")
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| st.write(result)
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| else:
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| st.error("Error in API request")
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|
|
