import joblib
import pandas as pd
import gradio as gr

vec = joblib.load('vectorizer.joblib')
feature_names = vec.get_feature_names_out()

knn = joblib.load('best_knn.joblib')
lreg = joblib.load('best_lreg.joblib')
svm = joblib.load('best_svm.joblib')
forest = joblib.load('best_forest.joblib')

knn_label = "KNN Classifier"
lreg_label = "Logistic Regression"
svm_label = "Support Vector Machine"
forest_label = "Random Forest"

str_to_model = {
    knn_label: knn,
    lreg_label: lreg,
    svm_label: svm,
    forest_label: forest
}

review_input = gr.Textbox(label = 'Review Comment')
dropdown_input = gr.Dropdown([knn_label, lreg_label, svm_label, forest_label], label='Method')

def multi_inputs(review, model_label):
  model = str_to_model[model_label]
  mat = vec.transform([review])
  bow = pd.DataFrame(mat.toarray(), columns=feature_names)

  a = model.predict(bow)[0]
  b = model.predict_proba(bow)[0]

  out1 = 'Negative comment' if a == 0 else 'Positive comment'
  out2 = f'{b[0]*100:.0f}%'
  out3 = f'{b[1]*100:.0f}%'

  return out1, out2, out3