Upload frontend_files/app.py with huggingface_hub

frontend_files/app.py

@@ -0,0 +1,70 @@
+import streamlit as st
+import pandas as pd
+import requests
+import os
+
+# Set the title of the Streamlit app
+st.title('Airbnb Rental Price Prediction')
+
+# Get the backend API URL from environment variables (set during Container Apps deployment)
+# Format: https://<app-name>.<region>.azurecontainerapps.io
+backend_api_url = os.getenv('BACKEND_API_URL', 'https://rental-price-prediction-api.azurecontainerapps.io')
+
+# 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'):
+    try:
+        response = requests.post(f'{backend_api_url}/v1/rental', json=input_data.to_dict(orient='records')[0])
+        if response.status_code == 200:
+            prediction = response.json()['Predicted Price (in dollars)']
+            st.success(f'Predicted Rental Price (in dollars): {prediction}')
+        else:
+            st.error(f'Error making prediction. Status code: {response.status_code}')
+    except Exception as e:
+        st.error(f'Error connecting to backend: {str(e)}')
+
+# 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'):
+        try:
+            response = requests.post(f'{backend_api_url}/v1/rentalbatch', files={'file': uploaded_file})
+            if response.status_code == 200:
+                predictions = response.json()
+                st.success('Batch predictions completed!')
+                st.write(predictions)  # Display the predictions
+            else:
+                st.error(f'Error making batch prediction. Status code: {response.status_code}')
+        except Exception as e:
+            st.error(f'Error connecting to backend: {str(e)}')