app.py

import gradio as gr
from joblib import dump, load

#inputs
input_text = gr.inputs.Textbox(label="Review Comment")
input_dropdown = gr.inputs.Dropdown(choices=['Logistic', 'LDA', 'QDA', 'SVC'], label='Method')

#outputs
output_text = gr.outputs.Textbox(label='Predicted sentiment class')
output_label = gr.outputs.Label(label='Predicted probability')

def predict(input_text, model):
  labels = ['Negative Comment', 'Positive Comment']
  input_text = [input_text]

  vectorizer = load('Vectorizer.joblib')
  input_text = vectorizer.transform(input_text).toarray()

  if model == 'Logistic':
    log_model = load('NLP_log.joblib')
    pred = log_model.predict_proba(input_text)
    print(pred)

  if model == 'LDA':
    lda_model = load('NLP_lda.joblib')
    pred = lda_model.predict_proba(input_text)
    print(pred)

  if model == 'QDA':
    qda_model = load('NLP_qda.joblib')
    pred = qda_model.predict_proba(input_text)
    print(pred)

  if model == 'SVC':
    svc_model = load('NLP_svc.joblib')
    pred = svc_model.predict_proba(input_text)
    print(pred)

  return 'model', {label: float(pred) for label, pred in zip(labels, pred[0])}


gr.Interface(fn = predict,
 inputs = [input_text, input_dropdown],
 outputs = [output_text, output_label]
 ).launch(debug=True)