Update app.py

app.py

@@ -6,21 +6,11 @@ import gradio as gr
 # Load the trained model
 model = joblib.load('hackathonrf.joblib')
 
-# Define AQI category labels
-aqi_labels = {
-    0: 'good',
-    1: 'moderate',
-    2: 'unhealthy_sensitive',
-    3: 'unhealthy',
-    4: 'very_unhealthy',
-    5: 'hazardous'
-}
-
 # Function to get latitude and longitude from location name
 def get_coordinates(location):
     geolocator = geopy.geocoders.Nominatim(user_agent="air_quality_app")
     location = geolocator.geocode(location)
-    return location.latitude, location.longitude
+    return location.latitude, location.longitude, location.address
 
 # Function to get AQI value from OpenWeatherMap API
 def get_aqi(latitude, longitude):
@@ -33,11 +23,19 @@ def get_aqi(latitude, longitude):
 
 # Function to make prediction
 def predict_air_quality(location):
-    latitude, longitude = get_coordinates(location)
+    latitude, longitude, city = get_coordinates(location)
     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}'"
+    label_mapping = {
+        0: 'good',
+        1: 'moderate',
+        2: 'unhealthy_sensitive',
+        3: 'unhealthy',
+        4: 'very_unhealthy',
+        5: 'hazardous'
+    }
+    air_quality_label = label_mapping[prediction[0]]
+    return f"{city} air quality is currently '{air_quality_label}', with AQI {aqi_value}"
 
 # Create Gradio interface
 iface = gr.Interface(fn=predict_air_quality,