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| import streamlit as st | |
| import pandas as pd | |
| import requests | |
| st.title("π SuperKart Quarterly Sales Predictor") | |
| # Input form | |
| st.subheader("π Predict Store's Quarterly Sales") | |
| store_id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"]) | |
| product_type = st.selectbox("Product Type", ["Dairy", "Soft Drinks", "Meat", "Canned", "Frozen Foods"]) | |
| sugar_content = st.selectbox("Product Sugar Content", ["Low", "Medium", "High", "No Added Sugar"]) | |
| store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Grocery Store", "Food Mart"]) | |
| city_type = st.selectbox("City Type", ["Urban", "Semi-Urban", "Rural"]) | |
| store_size = st.selectbox("Store Size", ["Small", "Medium", "High"]) | |
| est_year = st.number_input("Store Establishment Year", min_value=1980, max_value=2025, value=2005) | |
| weight = st.number_input("Product Weight", min_value=0.0, value=12.0) | |
| area = st.number_input("Product Allocated Area", min_value=0.0, value=125.0) | |
| mrp = st.number_input("Product MRP", min_value=0.0, value=120.0) | |
| input_data = pd.DataFrame([{ | |
| 'Store_Id': store_id, | |
| 'Product_Type': product_type, | |
| 'Product_Sugar_Content': sugar_content, | |
| 'Store_Type': store_type, | |
| 'Store_Location_City_Type': city_type, | |
| 'Store_Size': store_size, | |
| 'Store_Establishment_Year': est_year, | |
| 'Product_Weight': weight, | |
| 'Product_Allocated_Area': area, | |
| 'Product_MRP': mrp | |
| }]) | |
| if st.button("Predict Sales"): | |
| api_url = "https://abcabcabc999--superkart.hf.space/v1/storesales" | |
| response = requests.post(api_url, json=input_data.to_dict(orient='records')) | |
| if response.status_code == 200: | |
| result = response.json() | |
| st.success(f"π¦ Predicted Total Sales: βΉ{result['Total_Store_Sales']:,.2f}") | |
| else: | |
| st.error(f"β API Error: {response.text}") | |
| st.subheader("π Batch Prediction via CSV") | |
| file = st.file_uploader("Upload CSV", type=["csv"]) | |
| if file and st.button("Predict Batch"): | |
| response = requests.post("https://abcabcabc999--superkart.hf.space/v1/storesalesbatch", files={"file": file}) | |
| if response.status_code == 200: | |
| st.write(pd.DataFrame(response.json())) | |
| else: | |
| st.error(f"β Batch API Error: {response.text}") | |