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| import streamlit as st | |
| import pandas as pd | |
| import requests | |
| # Set the title of the Streamlit app | |
| st.title("SuperKart Sales Forecast") | |
| # Section for online prediction | |
| st.subheader("Online Forecast") | |
| # Collect user input for property features | |
| Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.6) | |
| Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, value=0.06) | |
| Product_MRP = st.number_input("Product MRP", min_value=0.0, value=147.0) | |
| #store_age = st.number_input("Store Age", min_value=0.0, value=10.0) | |
| Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"]) | |
| Product_Type = st.selectbox("Product Type", ["Fruits and Vegetables", "Snack Foods", "Frozen Foods", | |
| "Dairy","Household","Baking Goods","Canned","Health and Hygiene" | |
| "Meat","Soft Drinks","Breads","Hard Drinks","Starchy Foods", | |
| "Breakfast","Seafood","Others" ]) | |
| Store_Id = st.selectbox("Store Id", ["OUT001", "OUT002", "OUT003", "OUT004"]) | |
| Store_Size = st.selectbox("Store Size", ["Small","Medium","High"]) | |
| Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1","Tier 2","Tier 3"]) | |
| Store_Type = st.selectbox("Store Type", ["Departmental Store","Supermarket Type1", | |
| "Supermarket Type2", "Food Mart"]) | |
| # Convert user input into a DataFrame | |
| input_data = pd.DataFrame([{ | |
| 'Product_Weight': Product_Weight, | |
| 'Product_Allocated_Area': Product_Allocated_Area, | |
| 'Product_MRP': Product_MRP, | |
| 'Product_Sugar_Content': Product_Sugar_Content, | |
| 'Product_Type': Product_Type, | |
| 'Store_Id': Store_Id, | |
| 'Store_Size': Store_Size, | |
| 'Store_Location_City_Type': Store_Location_City_Type, | |
| 'Store_Type' : Store_Type | |
| }]) | |
| # Make prediction when the "Predict" button is clicked | |
| if st.button("Forecast"): | |
| response = requests.post("https://DrishVij-SuperKartBackend2.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API | |
| if response.status_code == 200: | |
| forecast = response.json() | |
| st.success(f"Forcasted Sales for the product (in dollars) is {forecast}") | |
| else: | |
| st.error("Error making forecast.") | |
| # Section for batch prediction | |
| st.subheader("Batch Forecast") | |
| # Allow users to upload a CSV file for batch prediction | |
| uploaded_file = st.file_uploader("Upload CSV file for batch sales forecast", type=["csv"]) | |
| # Make batch prediction when the "Predict Batch" button is clicked | |
| if uploaded_file is not None: | |
| if st.button("Forecast Batch"): | |
| response = requests.post("https://DrishVij-SuperKartBackend2.hf.space/v1/salesbatch", files={"file": uploaded_file}) # Send file to Flask API | |
| if response.status_code == 200: | |
| forecast = response.json() | |
| st.success("Batch forecast completed!") | |
| st.write(forecast) # Display the predictions | |
| else: | |
| st.error("Error making sales prediction.") | |