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Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
+
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

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 Sales Predictor App")
+st.write("This tool predicts store sales revenue based on store and product details. Enter the required information below.")
+
+#Collect user info based on dataset columns
+ProductWeight = st.number_input("Product_Weight", min_value= 0.5, max_value= 100.0),
+ProductSugarContent = st.selectbox("Product_Sugar_Content",["No Sugar", "Low Sugar", "Regular"])	
+ProductAllocatedArea = st.number_input("Product_Allocated_Area", min_value=0.001, max_value=0.5),
+ProductType = st.selectbox("Product_Type",["Baking Goods", "Breads", "Breakfast", "Canned", "Dairy", "Frozen Foods", "Fruits and Vegetables", "Hard Drinks", "Health and Hygiene", "Household", "Meat", "Seafood", "Snack Foods", "Soft Drinks", "Starchy Foods"]),
+ProductMRP = st.number_input("Product_MRP", min_value=5, max_value=500),
+StoreID = st.selectbox("Store_Id",["OUT001", "OUT002", "OUT003","OUT004"]),
+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", "Grocery Store"]),
+StoreEstablishmentYear = st.number_input("Store_Age", min_value=2023, max_value=2027)
+
+#Convert categorical inputs to match model training
+store_data = {
+      'Product_Weight' :	ProductWeight,
+      'Product_Sugar_Content' : ProductSugarContent,	
+      'Product_Allocated_Area'	: ProductAllocatedArea,
+      'Product_Type'	: ProductType,
+      'Product_MRP'	: ProductMRP,
+      'Store_Id'	: StoreID,
+      'Store_Size' : StoreSize,	
+      'Store_Location_City_Type' : StoreLocationCityType,	
+      'Store_Type' : 	StoreType,
+      'Store_Age' : StoreEstablishmentYear
+}
+
+if st.button("Predict", type='primary'):
+  response = requests.post("https://rojasnath/Backend.hf.space/predict", json=store_data)
+  if response.status_code == 200:
+    result = response.json()
+    sales_prediction = result['prediction']
+    st.write(f"Based on the information provided, the forecasted sales revenue for the store is ${sales_prediction:.2f}.")
+  else:
+    st.error("Error in API Request")
+
+
+#Batch Prediction
+st.subheader("Batch Prediction")
+
+file = st.file_uploader("Upload a CSV file", type=["csv"])
+if file is not None:
+  if st.button("Predict"):
+    response = requests.post("https://rojasnath/Backend.hf.space/predict_batch", files={"file": file})
+    if response.status_code == 200:
+    result = response.json()
+    st.header("Bacth Prediction Results")
+    st.write(result)
+  else:
+    st.error("Error in API Request")

requirements.txt

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+
+pandas==2.2.2
+requests==2.32.3
+streamlit==1.43.2