Update app.py

app.py

@@ -6,7 +6,7 @@ import gradio as gr
 # Load the trained model
 model = joblib.load('hackathonrf.joblib')
 
-# Define AQI category labels
+# Define AQI labels
 aqi_labels = {
     0: 'good',
     1: 'moderate',
@@ -28,16 +28,17 @@ def get_aqi(latitude, longitude):
     url = f"http://api.openweathermap.org/data/2.5/air_pollution?lat={latitude}&lon={longitude}&appid={api_key}"
     response = requests.get(url)
     data = response.json()
+    pm25_aqi_level = data['list'][0]['components']['pm2_5']
     aqi_value = data['list'][0]['main']['aqi']
-    return aqi_value
+    return pm25_aqi_level, aqi_value
 
 # Function to make prediction
 def predict_air_quality(location):
     latitude, longitude = get_coordinates(location)
-    aqi_value = get_aqi(latitude, longitude)
+    pm25_aqi_level, aqi_value = get_aqi(latitude, longitude)
     prediction = model.predict([[aqi_value, aqi_value, aqi_value, aqi_value]])
     label_string = aqi_labels[prediction[0]]
-    return f"{location} air quality is currently '{label_string}'"
+    return f"{location} air quality is currently {label_string}, with AQI {pm25_aqi_level}"
 
 # Create Gradio interface
 iface = gr.Interface(fn=predict_air_quality,