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Dockerfile

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+FROM python:3.10-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy all files from the current directory to the container's working directory
+COPY . .
+
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Define the command to start the application
+CMD ["streamlit","run", "app.py"]

app.py

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+import streamlit as st
+import pandas as pd
+import requests
+
+# Streamlit UI for Customer Churn Prediction
+st.title("SuperKart Store Sales Prediction App")
+st.write("This tool predicts SuperKart Store sales based on their details. Enter the required information below.")
+
+# Collect user input based on dataset columns
+# ProductID = st.number_input("Product ID") # Product Id may not be useful in our prediction.
+ProductWeight = st.number_input("Product Weight",min_value=0.0, max_value=500.0, value=10.0)
+ProductAllocatedArea = st.number_input("Product Allocated Area",min_value=0.0001, max_value=1.0, value=0.02)
+StoreEstablishmentYear = st.number_input("Store Establishment Year",min_value=1990, max_value=2025, value=2020)
+ProductMRP = st.number_input("Product MRP",min_value=0.0, max_value=1000.0, value=100.0)
+ProductSugarContent = st.selectbox("Product Sugar Content",["Low Sugar","Regular","No Sugar"])
+StoreId = st.selectbox("Store ID",["OUT001","OUT002","OUT003","OUT004","OTHERS"])
+StoreSize = st.selectbox("Store Size",["Small","Medium","High"])
+StoreLocationCityType = st.selectbox("Store Location City Type",["Tier 1","Tier 2","Tier 3"])
+StoreType = st.selectbox("Store Type",["Supermarket Type1","Supermarket Type2","Departmental Store", "Food Mart"])
+ProductType = st.selectbox("Product Type",['Frozen Foods', 'Dairy', 'Canned', 'Baking Goods', 'Health and Hygiene', 'Snack Foods', 'Meat', 'Household', 'Hard Drinks', 'Fruits and Vegetables', 'Breads', 'Soft Drinks', 'Breakfast', 'Others', 'Starchy Foods', 'Seafood'])
+
+# Convert categorical inputs to match model training
+sales_data = {
+ 'Product_Weight': ProductWeight,
+ 'Product_Allocated_Area': ProductAllocatedArea,
+ 'Store_Establishment_Year': StoreEstablishmentYear,
+ 'Product_MRP': ProductMRP,
+ 'Product_Sugar_Content': ProductSugarContent,
+ 'Store_Id': StoreId,
+ 'Store_Size': StoreSize,
+ 'Store_Location_City_Type': StoreLocationCityType,
+ 'Store_Type': StoreType,
+ 'Product_Type': ProductType
+ }
+
+if st.button("Predict", type='primary'):
+    response = requests.post("https://papsofts-SuperKart_Sales_Forecast_Backend.hf.space/v1/storeSales", json=sales_data)    # enter user name and space name before running the cell
+    if response.status_code == 200:
+        result = response.json()
+        sales_prediction = result["Prediction"]  # Extract only the value
+        st.write(f"Based on the information provided, the sales for the product mentioned is {sales_prediction}.")
+    else:
+        st.error("Error in API request")

requirements.txt

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+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2