Remove extra code

app.py

@@ -13,29 +13,6 @@ encoder = {
         'positive':'assets/positive.jpeg'
     }
 
-def predict(model, text):
-    
-    selected_model = None
-    with open('vectorizer.pkl', 'rb') as file:
-        vectorizer = pickle.load(file)
-
-    if 'Random Forest' == model:
-        selected_model = "models/rf_twitter.pkl"
-    elif 'Logistic Regression' == model:
-        selected_model = "models/lg_twitter.pkl"
-    elif 'Naive Bayes' == model:
-        selected_model = "models/nb_twitter.pkl"
-    elif 'Decision Tree' == model:
-        selected_model = "models/dt_twitter.pkl"
-    elif 'KNN' == model:
-        selected_model = "models/knn_twitter.pkl"
-    else:
-        selected_model = "models/lg_twitter.pkl"
-    loaded_model = load_model(selected_model)
-    text_vector = vectorizer.transform([text])
-    prediction = loaded_model.predict(text_vector)
-    return encoder[prediction[0]]
-    
 classifier = pipeline(task="zero-shot-classification", model="facebook/bart-large-mnli")
 def analyze_sentiment(text):
   results = classifier(text,["positive","negative",'neutral'],multi_label=True)
@@ -44,7 +21,5 @@ def analyze_sentiment(text):
   result = results['labels'][ind]
   return encoder[result]
 
-# models = gr.Radio(['Random Forest', 'Logistic Regression','Naive Bayes','Decision Tree','KNN'], label="Choose model")
-# demo = gr.Interface(fn=predict, inputs=[models,"text"], outputs="image", title="Sentiment Analysis") 
 demo = gr.Interface(fn=analyze_sentiment, inputs="text", outputs="image", title="Sentiment Analysis") 
 demo.launch(share=True)