Update app.py

app.py

@@ -18,23 +18,34 @@ 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 = data['list'][0]['main']['aqi']
-    return aqi_value
+    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"
 
 # Function to make prediction
 def predict_air_quality(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_mapping = {
-        0: 'good',
-        1: 'moderate',
-        2: 'unhealthy_sensitive',
-        3: 'unhealthy',
-        4: 'very_unhealthy',
-        5: 'hazardous'
-    }
-    air_quality_label = label_mapping[prediction[0]]
+    air_quality_label = convert_to_label(aqi_value)
     return f"{city} air quality is currently '{air_quality_label}', with AQI {aqi_value}"
 
 # Create Gradio interface