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| import streamlit as st | |
| import pandas as pd | |
| import requests # Import requests for API calls | |
| # --- Configuration for Backend API --- # | |
| # This URL should point to your deployed backend Hugging Face Space | |
| BACKEND_API_URL = "https://lokiiparihar-superkart-api-t.hf.space" # Replace with your actual backend space URL | |
| PREDICT_ENDPOINT = f"{BACKEND_API_URL}/v1/sales" | |
| # Streamlit UI for Sales Prediction | |
| st.title("SuperKart Sales Prediction App") | |
| st.write("This tool predicts the sales revenue for a specific product in a SuperKart store.") | |
| st.subheader("Enter the product and store details:") | |
| # Collect user input for SuperKart sales prediction | |
| product_id = st.selectbox("Product ID Prefix", ['FD', 'NC', 'DR']) | |
| product_weight = st.number_input("Product Weight", min_value=0.0, value=12.0) | |
| 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=0.05) | |
| product_type = st.selectbox("Product Type", ['Perishables', 'Non Perishables']) | |
| product_mrp = st.number_input("Product MRP", min_value=0.0, value=150.0) | |
| store_id = st.selectbox("Store ID", ['OUT004', 'OUT003', 'OUT001', 'OUT002']) | |
| store_type = st.selectbox("Store Type", ["Grocery Store","Supermarket Type1","Supermarket Type2","Supermarket Type3"]) | |
| store_size = st.selectbox("Store Size", ['Medium', 'High', 'Small']) | |
| store_location_city_type = st.selectbox("Store Location City Type", ['Tier 2', 'Tier 1', 'Tier 3']) | |
| store_current_age = st.number_input("Store Current Age (Years)", min_value=0, value=15) | |
| # Convert user input into a dictionary for JSON payload | |
| input_data = { | |
| 'Product_Id': product_id, | |
| '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_Type': store_type, | |
| 'Store_Size': store_size, | |
| 'Store_Location_City_Type': store_location_city_type, | |
| 'Store_Current_Age': store_current_age | |
| } | |
| # Predict button | |
| if st.button("Predict Sales"): | |
| try: | |
| # Make a POST request to the backend API | |
| response = requests.post(PREDICT_ENDPOINT, json=input_data) | |
| response.raise_for_status() # Raise an exception for HTTP errors (4xx or 5xx) | |
| predicted_sales = response.json().get('Predicted Sales') | |
| if predicted_sales is not None: | |
| st.write(f"The predicted sales revenue is ${predicted_sales:.2f}.") | |
| else: | |
| st.error("Prediction failed: Unexpected response from backend.") | |
| st.json(response.json()) # Display full response for debugging | |
| except requests.exceptions.ConnectionError: | |
| st.error("Could not connect to the backend API. Please ensure the backend Space is running and accessible.") | |
| except requests.exceptions.RequestException as e: | |
| st.error(f"An error occurred during the API request: {e}") | |
| st.text(response.text) # Display raw response text for debugging | |
| except Exception as e: | |
| st.error(f"An unexpected error occurred: {e}") | |