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| import streamlit as st | |
| import pandas as pd | |
| import requests | |
| API_ENDPOINT="https://TokenTutor-SuperKartSalesPrectionBackend.hf.space/v1/forecast" | |
| #product type | |
| product_types = [ | |
| "Fruits and Vegetables", | |
| "Snack Foods", | |
| "Frozen Foods", | |
| "Dairy", | |
| "Household", | |
| "Baking Goods", | |
| "Canned", | |
| "Health and Hygiene", | |
| "Meat", | |
| "Soft Drinks", | |
| "Breads", | |
| "Hard Drinks", | |
| "Others", | |
| "Starchy Foods", | |
| "Breakfast", | |
| "Seafood" | |
| ] | |
| #store types | |
| store_types = [ | |
| "Food Mart", | |
| "Supermarket Type1", | |
| "Supermarket Type2", | |
| "Departmental Store" | |
| ] | |
| #Store Id | |
| store_ids = [ | |
| "OUT001", | |
| "OUT002", | |
| "OUT003", | |
| "OUT004" | |
| ] | |
| store_Location_City_Types=[ | |
| "Tier 1", | |
| "Tier 2", | |
| "Tier 3" | |
| ] | |
| store_sizes=[ | |
| "Small", | |
| "Medium", | |
| "Large" | |
| ] | |
| #Set title of the Streamlit app | |
| st.title("Product Revenue prediction") | |
| #Section for online prediction | |
| st.subheader("Online Prediction") | |
| #Collect user input for features | |
| Product_Weight = st.number_input("Product Weight", min_value=4.0, max_value=25.0, step=0.5) | |
| Product_Sugar_Content = st.selectbox("Product Sugar Content", ["No Sugar", "Low Sugar", "Regular"]) | |
| Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.001, max_value=0.3) | |
| Product_Type = st.selectbox("Product Type", product_types) | |
| Product_MRP = st.number_input("Product MRP", min_value=30.0, max_value=300.0) | |
| Store_Id = st.selectbox("Store Id", store_ids) | |
| Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1988, max_value=2010, step=1) | |
| Store_Size = st.selectbox("Store Size", store_sizes) | |
| Store_Location_City_Type = st.selectbox("Store Location City Type", store_Location_City_Types) | |
| Store_Type = st.selectbox("Store Type", store_types) | |
| payload = { | |
| '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 | |
| } | |
| if st.button("Predict"): | |
| response = requests.post(API_ENDPOINT, json=payload) | |
| if response.status_code == 200: | |
| json_data= response.json() | |
| st.write('Predicted Sales revenue ', json_data.get('Prediction')) | |
| else: | |
| st.write(f"Error making prediction: {response.status_code}") | |
| # Section for batch prediction | |
| st.subheader("Batch Prediction") | |
| # Allow users to upload a CSV file for batch prediction | |
| uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"]) | |
| BATCH_ENDPOINT="https://TokenTutor-SuperKartSalesPrectionBackend.hf.space/v1/forecastbatch" | |
| # Make batch prediction when the "Predict Batch" button is clicked | |
| if uploaded_file is not None: | |
| if st.button("Predict Batch"): | |
| response = requests.post(BATCH_ENDPOINT, files={"file": uploaded_file}) # Send file to Flask API | |
| if response.status_code == 200: | |
| predictions = response.json() | |
| st.success("Batch predictions completed!") | |
| st.write(predictions) # Display the predictions | |
| else: | |
| st.error("Error making batch prediction.") | |