Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import pandas as pd
 import joblib
-loaded_best_pipeline = joblib.load('my_first_pipeline.pkl')
+model = joblib.load('rf_model.pkl')
 
 
 
@@ -10,12 +10,15 @@ def predict_quality(fixed_acidity = None, volatile_acidity = None, citric_acid =
   if 0 in model_input:
     return_message = "Error: Invalid Input \n Please Input: \n"
     model_input_name = ['fixed_acidity', 'volatile_acidity', 'citric_acid', 'residual_sugar', 'chlorides', 'free_sulfur_dioxide', 'total_sulfur_dioxide', 'density', 'pH', 'sulphates', 'alcohol']
+
     for i in range(len(model_input)):
       if model_input[i] == 0:
         return_message = return_message + " " + model_input_name[i]
     return return_message
   else:
-    prediction = predict_model(loaded_best_pipeline, data = pd.DataFrame([model_input], columns = white_wine.columns[:-1]))
+    data = pd.DataFrame([[ fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol
+]], columns=model_input_name)
+    prediction = model.predict(data)[0]
     return_message = "Predicted Quality: " + str(prediction['prediction_label'][0]) + "\nConfidence: " + str(prediction['prediction_score'][0])
     return return_message