Update app.py

app.py

@@ -8,15 +8,19 @@ from tensorflow.keras.models import load_model
 # HF_TOKEN = os.getenv("HF_TOKEN")
 # hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "weather-madrid-flags")
 
-def predict_energy(f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13):
+def predict_energy(f0,f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13):
     # input_feature = [f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13]
+    # if f0 is not None: 
+    #     if f= == "Custom LSTM":      
+    
     weather_input_array = np.array([f1,f2,f3,f4,f5,f6]).reshape(1,-1)
 
     scaler = load("scaler.joblib")
     scaled_weather_input_array = scaler.transform(weather_input_array)
     history_input_array = np.array([f7,f8,f9,f10,f11,f12,f13])
     input_feature = np.concatenate((scaled_weather_input_array[0],history_input_array[0]))
-    lstm_model_7_in_1_out = load_model('history_7_future_1.h5')
+    
+    model = load_model('history_7_future_1.h5')
     # dtc = load('dtc_model.sav')
     # xgb = load('xgb_model.sav')