| import streamlit as st |
| import pandas as pd |
| import requests |
|
|
| st.title("SuperKart Sales Prediction") |
|
|
| st.subheader("Online Prediction") |
| product_weight = st.number_input("Product Weight (kg)", min_value=0.0, value=10.0) |
| product_allocated_area = st.number_input("Allocated Shelf Area (sq m)", min_value=0.0, value=0.05) |
| product_mrp = st.number_input("Product MRP (INR)", min_value=1.0, value=100.0) |
| store_est_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2005) |
| product_sugar_content = st.selectbox("Sugar Content", ["Low Sugar", "No Sugar", "Regular"]) |
| product_type = st.selectbox("Product Type", [ |
| "Dairy", "Canned", "Baking Goods", "Frozen Foods", "Health and Hygiene", |
| "Snack Foods", "Soft Drinks", "Others" |
| ]) |
| store_size = st.selectbox("Store Size", ["Small", "Medium", "High"]) |
| store_location = st.selectbox("Store Location Type", ["Tier 1", "Tier 2", "Tier 3"]) |
| store_type = st.selectbox("Store Type", [ |
| "Supermarket Type1", "Supermarket Type2", "Supermarket Type3", |
| "Grocery Store", "Food Mart", "Departmental Store" |
| ]) |
|
|
| input_data = pd.DataFrame([{ |
| "Product_Weight": product_weight, |
| "Product_Allocated_Area": product_allocated_area, |
| "Product_MRP": product_mrp, |
| "Store_Establishment_Year": store_est_year, |
| "Product_Sugar_Content": product_sugar_content, |
| "Product_Type": product_type, |
| "Store_Size": store_size, |
| "Store_Location_City_Type": store_location, |
| "Store_Type": store_type |
| }]) |
|
|
| if st.button("Predict Sales"): |
| response = requests.post( |
| "https://dhani10-SuperKartSalesPredictionBackend.hf.space/v1/sales", |
| json=input_data.to_dict(orient='records')[0] |
| ) |
| if response.status_code == 200: |
| prediction = response.json()['predicted_sales'] |
| st.success(f"Predicted Sales: ₹{round(prediction, 2)}") |
| else: |
| st.error(f"Error making prediction: {response.status_code} - {response.text}") |
|
|
| st.subheader("Batch Prediction") |
| uploaded_file = st.file_uploader("Upload a CSV file for batch prediction", type=["csv"]) |
|
|
| if uploaded_file is not None: |
| if st.button("Predict Batch"): |
| response = requests.post( |
| "https://dhani10-SuperKartSalesPredictionBackend.hf.space/v1/salesbatch", |
| files={"file": uploaded_file} |
| ) |
| if response.status_code == 200: |
| predictions = response.json() |
| st.success("Batch predictions completed!") |
| st.dataframe(pd.DataFrame(predictions, columns=["Predicted Sales (INR)"])) |
| else: |
| st.error(f"Error making batch prediction: {response.status_code} - {response.text}") |
|
|
