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Dockerfile

@@ -1,16 +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
-
-# 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
+# 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

@@ -1,59 +1,59 @@
-import streamlit as st
-import pandas as pd
-import requests
-
-# Set the title of the Streamlit app
-st.title("Airbnb Rental Price Prediction")
-
-# Section for online prediction
-st.subheader("Online Prediction")
-
-# Collect user input for property features
-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",  # Convert to 't' or 'f'
-    'review_scores_rating': review_scores_rating,
-    'bedrooms': bedrooms,
-    'beds': beds
-}])
-
-# Make prediction when the "Predict" button is clicked
-if st.button("Predict"):
-    response = requests.post("https://RPeltier/RentalPricePredictionBackend.hf.space/v1/rental.hf.", 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.")
-
-# 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://RPeltier/RentalPricePredictionBackend.hf.space/v1/rentalbatch", 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.")
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("Airbnb Rental Price Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+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",  # Convert to 't' or 'f'
+    'review_scores_rating': review_scores_rating,
+    'bedrooms': bedrooms,
+    'beds': beds
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://RPeltier/RentalPricePredictionBackendV2.hf.space/v1/rental", 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.")
+
+# 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://RPeltier/RentalPricePredictionBackendV2.hf.space/v1/rentalbatch", 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

@@ -1,3 +1,3 @@
-pandas==2.2.2
-requests==2.28.1
-streamlit==1.43.2
+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2