import gradio as gr
import pickle

from nltk.tokenize import word_tokenize
import nltk
nltk.download('all')

with open('LogisticRegression_classifier.pickle','rb') as fp:
  LogisticRegression_classifier = pickle.load(fp)

with open('word_features5k.pickle','rb') as fp:
  word_features = pickle.load(fp)


def find_features(news):
  words = word_tokenize(news)  
  features = {}
  for w in word_features:
    features[w] = (w in words)
  return features

def fn(news):
    return LogisticRegression_classifier.classify(find_features(news))

iface = gr.Interface(
        fn = fn,
        inputs = 'text',
        outputs = 'text'
)

url = iface.launch()
# return url[]