Update app.py

app.py

@@ -15,10 +15,6 @@ def download_file(url, output_path):
                 st.error(f"Failed to download {url}. HTTP status code: {response.status_code}")
                 st.stop()
 
-# Updated URLs to the model and label encoder files
-model_url = "https://huggingface.co/spaces/amornpan/weather-prediction-app/raw/main/random_forest_weather_model.pkl"
-encoder_url = "https://huggingface.co/spaces/amornpan/weather-prediction-app/raw/main/label_encoders.pkl"
-
 # Local paths for the downloaded files
 model_path = "random_forest_weather_model.pkl"
 encoder_path = "label_encoders.pkl"
@@ -43,12 +39,12 @@ except Exception as e:
     st.stop()
 
 # Debug: Check the structure of label_encoders
-if isinstance(label_encoders, dict):
-    st.write("Loaded Label Encoders:", label_encoders.keys())
-else:
+if not isinstance(label_encoders, dict):
     st.error("Label encoders file is not in the expected format.")
     st.stop()
 
+st.write("Loaded Label Encoders:", label_encoders.keys())
+
 # Define UI
 st.title("Weather Prediction App")
 st.write("กรอกข้อมูลเพื่อพยากรณ์ประเภทของสภาพอากาศ")
@@ -60,24 +56,26 @@ wind_speed = st.number_input("Wind Speed (km/h)", min_value=0.0, max_value=200.0
 precipitation = st.number_input("Precipitation (%)", min_value=0.0, max_value=100.0, value=10.0)
 
 # Handle 'Cloud Cover' selection
-if 'Cloud Cover' in label_encoders:
-    cloud_cover_options = label_encoders['Cloud Cover'].classes_ if hasattr(label_encoders['Cloud Cover'], 'classes_') else ["Clear", "Partly Cloudy", "Overcast"]
-else:
-    cloud_cover_options = ["Clear", "Partly Cloudy", "Overcast"]
-
+cloud_cover_options = label_encoders['Cloud Cover'].classes_ if 'Cloud Cover' in label_encoders else ["Clear", "Partly Cloudy", "Overcast"]
 cloud_cover = st.selectbox("Cloud Cover", cloud_cover_options)
 
 # Other inputs
 atmospheric_pressure = st.number_input("Atmospheric Pressure (hPa)", min_value=900.0, max_value=1100.0, value=1010.0)
 uv_index = st.number_input("UV Index", min_value=0, max_value=10, value=5)
-season = st.selectbox("Season", label_encoders['Season'].classes_ if 'Season' in label_encoders else ["Winter", "Spring", "Summer", "Autumn"])
+season_options = label_encoders['Season'].classes_ if 'Season' in label_encoders else ["Winter", "Spring", "Summer", "Autumn"]
+season = st.selectbox("Season", season_options)
 visibility = st.number_input("Visibility (km)", min_value=0.0, max_value=50.0, value=10.0)
-location = st.selectbox("Location", label_encoders['Location'].classes_ if 'Location' in label_encoders else ["Inland", "Coastal", "Mountain"])
+location_options = label_encoders['Location'].classes_ if 'Location' in label_encoders else ["Inland", "Coastal", "Mountain"]
+location = st.selectbox("Location", location_options)
 
 # Encode categorical inputs
-cloud_cover_encoded = label_encoders['Cloud Cover'].transform([cloud_cover])[0] if 'Cloud Cover' in label_encoders else 0
-season_encoded = label_encoders['Season'].transform([season])[0] if 'Season' in label_encoders else 0
-location_encoded = label_encoders['Location'].transform([location])[0] if 'Location' in label_encoders else 0
+try:
+    cloud_cover_encoded = label_encoders['Cloud Cover'].transform([cloud_cover])[0]
+    season_encoded = label_encoders['Season'].transform([season])[0]
+    location_encoded = label_encoders['Location'].transform([location])[0]
+except Exception as e:
+    st.error(f"Error encoding categorical inputs: {e}")
+    st.stop()
 
 # Prepare input data
 input_data = pd.DataFrame({