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
+    return location.latitude, location.longitude
 
 # Function to get AQI value from OpenWeatherMap API
 def get_aqi(latitude, longitude):
@@ -18,35 +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()
-    if 'list' in data and data['list']:
-        aqi_value = data['list'][0]['main']['aqi']
-        return aqi_value
-    else:
-        return None
-
-# Function to convert AQI value to corresponding label
-def convert_to_label(aqi_value):
-    if aqi_value is None:
-        return "unknown"
-    elif aqi_value <= 50:
-        return "good"
-    elif aqi_value <= 100:
-        return "moderate"
-    elif aqi_value <= 150:
-        return "unhealthy for sensitive groups"
-    elif aqi_value <= 200:
-        return "unhealthy"
-    elif aqi_value <= 300:
-        return "very unhealthy"
-    else:
-        return "hazardous"
+    aqi_value = data['list'][0]['main']['aqi']
+    return aqi_value
 
 # Function to make prediction
 def predict_air_quality(location):
-    latitude, longitude, city = get_coordinates(location)
+    latitude, longitude = get_coordinates(location)
     aqi_value = get_aqi(latitude, longitude)
-    air_quality_label = convert_to_label(aqi_value)
-    return f"{city} air quality is currently '{air_quality_label}', with AQI {aqi_value}"
+    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}'"
 
 # Create Gradio interface
 iface = gr.Interface(fn=predict_air_quality,