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a21f07c 6214905 791073f c8a84f0 a21f07c 791073f a21f07c 6214905 a21f07c 6214905 a21f07c | 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 | import requests
import streamlit as st
import pandas as pd
st.title("SuperKart Sales Prediction")
# Single Item Prediction
st.subheader("Single Item Prediction")
# Input fields for product and store data based on SuperKart dataset features
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=0.027)
product_type = st.selectbox("Product Type", ['Baking Goods', 'Breads', 'Breakfast', 'Canned', 'Dairy', '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, value=117.08)
store_id = st.selectbox("Store ID", ['OUT001', 'OUT002', 'OUT003', 'OUT004'])
store_establishment_year = st.number_input("Store Establishment Year", min_value=1985, max_value=datetime.now().year, value=2009)
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"])
item_data = {
'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,
}
# Replace with your Hugging Face Space URL for the backend
backend_url = "https://Amittripipathi-SuperKart.hf.space"
if st.button("Predict Sales", type='primary'):
response = requests.post(f"{backend_url}/v1/predict_sale", json=item_data)
if response.status_code == 200:
result = response.json()
predicted_sales = result["Predicted_Sales"]
st.write(f"The predicted sales for this item is: {predicted_sales:.2f}")
else:
st.error(f"Error in API request: {response.status_code} - {response.text}")
# Batch Prediction
st.subheader("Batch Prediction")
file = st.file_uploader("Upload CSV file for Batch Prediction", type=["csv"])
if file is not None:
if st.button("Predict for Batch", type='primary'):
response = requests.post(f"{backend_url}/v1/predict_sale_batch", files={"file": file})
if response.status_code == 200:
result = response.json()
st.header("Batch Prediction Results")
# Display batch predictions
predictions_df = pd.DataFrame(result['Batch_Predictions'], columns=['Predicted_Sales'])
st.dataframe(predictions_df)
else:
st.error(f"Error in API request: {response.status_code} - {response.text}")
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