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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 joblib
+import numpy as np
+
+# Load the trained model
+@st.cache_resource
+def load_model():
+    return joblib.load("deployment_files/backend_files/product_sales_prediction_model_rf_tuned_v2_0.joblib")
+
+model = load_model()
+
+# Streamlit UI for Price Prediction
+st.title("Product Sales Prediction App")
+st.write("This tool predicts the price of an products listing based on the given details.")
+
+st.subheader("Enter the listing details:")
+
+# Collect user input
+Product_Sugar_Content = st.selectbox("Sugar Content", ["Regular", "Low Sugar", "No Sugar"])
+Product_Weight = st.number_input("Product Weights (grams)", min_value=0, max_value=25, format="%.2f", value=15.0, step=0.01)
+Product_Allocated_Area = st.number_input("Product Allocated Area (Sq.Ft)", min_value=0,max_value=0.3, step=0.01, value=0.20, format="%.2f")
+Product_MRP = st.number_input("Product Price ($) ", min_value=0, max_value=300, step=1, value=100, format="%.2f")
+Store_Size = st.selectbox("Store Size", ["High", "Medium", "Small"])
+Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Food Mart", "Departmental Store"])
+Product_Id_Code = st.selectbox("Product ID Code ", ["FD", "DR", "NC"])
+Product_Type_Category = st.selectbox("Product Type Category ", ["Non Perishables", "Perishables"])
+Store_Age_Years = st.number_input("Age of the store ? ", min_value=0, max_value=50, value=15, step=1)
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'Product_Sugar_Content': Product_Sugar_Content,
+    'Product_Weight': Product_Weight,
+    'Product_Allocated_Area': Product_Allocated_Area,
+    'Product_MRP': Product_MRP,
+    'Store_Size': Store_Size,
+    'Store_Location_City_Type': Store_Location_City_Type,
+    'Store_Type': Store_Type,
+    'Product_Id_Code': Product_Id_Code,
+    'Product_Type_Category': Product_Type_Category,
+    'Store_Age_Years': Store_Age_Years
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://R-autowired-ProductSalesPredictionBackend.hf.space/v1/salespredict", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Price (in dollars)']
+        st.success(f"Predicted Rental Price (in dollars): {prediction}")
+    else:
+        st.error("Error making prediction.")

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2