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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
+
+# Set the title of the Streamlit app
+st.title("Sales Revenue Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+Product_Id = st.text_input("Product Id")
+Product_Weight = st.number_input("Product Weight", min_value=0.0)
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0)
+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","Others",
+                                             "Starchy Foods","Breakfast","Seafood"])
+Product_MRP = st.number_input("Product MRP", min_value=0.0)
+Store_Id = st.text_input("Store Id")
+Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=0)
+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", ["Supermarket Type2", "Supermarket Type1", "Departmental Store","Food Mart"])
+
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{'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_Establishment_Year': Store_Establishment_Year,
+        'Store_Location_City_Type': Store_Location_City_Type,
+        'Store_Type': Store_Type}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://pragmat-SalesRevenuePredictionBackend.hf.space/v1/revenue", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Sales Revenue (in dollars)']
+        st.success(f"Predicted Sales Revenue (in dollars): {prediction}")
+    else:
+        st.error("Error making prediction.")
+
+# 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"])
+
+# Make batch prediction when the "Predict Batch" button is clicked
+if uploaded_file is not None:
+    if st.button("Predict Batch"):
+        response = requests.post("https://pragmat-SalesRevenuePredictionBackend.hf.space/v1/revenuebatch", 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.")

requirements.txt

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