Update app.py

app.py

@@ -6,11 +6,21 @@ 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, location.address.split(", ")[-2]
+    return location.latitude, location.longitude
 
 # Function to get AQI value from OpenWeatherMap API
 def get_aqi(latitude, longitude):
@@ -18,16 +28,16 @@ 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()
-    aqi_value = sum([pollutant['value'] for pollutant in data['list'][0]['components'].values()])
+    aqi_value = data['list'][0]['main']['aqi']
     return aqi_value
 
 # Function to make prediction
 def predict_air_quality(location):
-    latitude, longitude, city_state = get_coordinates(location)
+    latitude, longitude = get_coordinates(location)
     aqi_value = get_aqi(latitude, longitude)
     prediction = model.predict([[aqi_value, aqi_value, aqi_value, aqi_value]])
-    label_string = "good" if prediction[0] == 0 else "moderate" if prediction[0] == 1 else "unhealthy" if prediction[0] == 2 else "very unhealthy" if prediction[0] == 3 else "hazardous"
-    return f"{city_state} air quality is currently {label_string}, with AQI {aqi_value:.2f}"
+    label_string = aqi_labels[prediction[0]]
+    return f"{location} air quality is currently '{label_string}'"
 
 # Create Gradio interface
 iface = gr.Interface(fn=predict_air_quality,