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Dockerfile

@@ -1,21 +1,16 @@
+# 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
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# 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
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# 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"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -0,0 +1,47 @@
+
+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("rental_price_prediction_model_v1_0.joblib")
+
+model = load_model()
+
+# Streamlit UI for Price Prediction
+st.title("Airbnb Rental Price Prediction App")
+st.write("This tool predicts the price of an Airbnb listing based on the property details.")
+
+st.subheader("Enter the listing details:")
+
+# Collect user input
+room_type = st.selectbox("Room Type", ["Entire home/apt", "Private room", "Shared room"])
+accommodates = st.number_input("Accommodates (Number of guests)", min_value=1, value=2)
+bathrooms = st.number_input("Bathrooms", min_value=1, step=1, value=2)
+cancellation_policy = st.selectbox("Cancellation Policy (kind of cancellation policy)", ["strict", "flexible", "moderate"])
+cleaning_fee = st.selectbox("Cleaning Fee Charged?", ["True", "False"])
+instant_bookable = st.selectbox("Instantly Bookable?", ["False", "True"])
+review_scores_rating = st.number_input("Review Score Rating", min_value=0.0, max_value=100.0, step=1.0, value=90.0)
+bedrooms = st.number_input("Bedrooms", min_value=0, step=1, value=1)
+beds = st.number_input("Beds", min_value=0, step=1, value=1)
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'room_type': room_type,
+    'accommodates': accommodates,
+    'bathrooms': bathrooms,
+    'cancellation_policy': cancellation_policy,
+    'cleaning_fee': cleaning_fee,
+    'instant_bookable': 'f' if instant_bookable=="False" else "t",
+    'review_scores_rating': review_scores_rating,
+    'bedrooms': bedrooms,
+    'beds': beds
+}])
+
+# Predict button
+if st.button("Predict"):
+    prediction = model.predict(input_data)
+    st.write(f"The predicted price of the rental property is ${np.exp(prediction)[0]:.2f}.")

rental_price_prediction_model_v1_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a373f720cb8147ba06379bff712827966fbd4e5b14074c35f79f6290a44b88f3
+size 90868

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.7.1
+xgboost==3.0.2
+joblib==1.5.1
+streamlit==1.43.2