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| import streamlit as st | |
| import pandas as pd | |
| import requests | |
| # Title | |
| st.title("SuperKart Sales Price Predictor") | |
| # ------------- Online Prediction ---------------- | |
| st.subheader("Online Prediction") | |
| # Required store outlet columns after encoding | |
| required_store_columns = ['Store_OUT001', 'Store_OUT002', 'Store_OUT003', 'Store_OUT004'] | |
| product_weight = st.number_input("Product Weight (kg)", min_value=0.0) | |
| product_allocated_area = st.number_input("Product Allocated Area", min_value=0.0) | |
| product_mrp = st.number_input("Product MRP", min_value=0.0) | |
| store_year = st.number_input("Store Establishment Year", min_value=1990, max_value=2025) | |
| product_sugar_content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar"]) | |
| store_size = st.selectbox("Store Size", ["High", "Medium", "Low"]) | |
| store_location = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"]) | |
| store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"]) | |
| product_type = st.selectbox("Product Type", [ | |
| "Meat", "Snack Foods", "Hard Drinks", "Dairy", "Canned", "Soft Drinks", | |
| "Health and Hygiene", "Baking Goods", "Bread", "Breakfast", "Frozen Foods", | |
| "Fruits and Vegetables", "Household", "Seafood", "Starchy Foods", "Others" | |
| ]) | |
| store_id = st.selectbox("Store ID", ["Store_OUT001", "Store_OUT002", "Store_OUT003", "Store_OUT004"]) | |
| input_data = pd.DataFrame([{ | |
| "Product_Weight": product_weight, | |
| "Product_Allocated_Area": product_allocated_area, | |
| "Product_MRP": product_mrp, | |
| "Store_Establishment_Year": store_year, | |
| "Product_Sugar_Content": product_sugar_content, | |
| "Store_Size": store_size, | |
| "Store_Location_City_Type": store_location, | |
| "Store_Type": store_type, | |
| "Product_Type": product_type, | |
| "Store_ID": store_id | |
| }]) | |
| # One-hot encode store id | |
| for col in required_store_columns: | |
| input_data[col] = 1 if col == f'Store_{store_id}' else 0 | |
| # Convert to DataFrame | |
| input_df = input_data | |
| if st.button("Predict"): | |
| try: | |
| response = requests.post( | |
| "https://zezkcy-SalePricePredictionBackendDemo.hf.space/v1/sales", # 🔁 UPDATE THIS | |
| json=input_df.to_dict(orient='records')[0] | |
| ) | |
| if response.status_code == 200: | |
| prediction = response.json().get("Predicted Sales", "No prediction returned") | |
| st.success(f"Predicted Sales: ${prediction}") | |
| else: | |
| st.error("Error in prediction.") | |
| st.text(response.text) | |
| except Exception as e: | |
| st.error(f"Exception: {e}") | |
| # ------------- Batch Prediction ---------------- | |
| st.subheader("Batch Prediction") | |
| uploaded_file = st.file_uploader("Upload CSV file", type=["csv"]) | |
| if uploaded_file is not None: | |
| if st.button("Predict Batch"): | |
| try: | |
| files = {"file": (uploaded_file.name, uploaded_file, "text/csv")} | |
| response = requests.post( | |
| "https://zezkcy-SalePricePredictionBackendDemo.hf.space/v1/salesbatch", # 🔁 UPDATE THIS | |
| files=files | |
| ) | |
| if response.status_code == 200: | |
| predictions = response.json() | |
| st.success("Batch prediction complete.") | |
| st.dataframe(pd.DataFrame(predictions)) | |
| else: | |
| st.error("Batch prediction failed.") | |
| st.text(response.text) | |
| except Exception as e: | |
| st.error(f"Exception: {e}") | |