import streamlit as st

def load_topic_transfomers():
  from transformers import pipeline
  try:
      topic_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli",device="cuda", compute_type="float16")
  except Exception as e:
      topic_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
      print("Error: ", e)
  return topic_classifier

def suggest_topic(text):

    while len(text)> 1024:
        text = summarize(whole_text[:-10])

    possible_topics = ["Gadgets", 'Business','Finance', 'Health', 'Sports',  'Politics','Government','Science','Education', 'Travel', 'Tourism', 'Finance & Economics','Market','Technology','Scientific Discovery',
                      'Entertainment','Environment','News & Media' "Space,Universe & Cosmos", "Fashion", "Manufacturing and Constructions","Law & Crime","Motivation", "Development & Socialization",  "Archeology"]

    result = topic_classifier(text, possible_topics)

    return result['labels']

st.title("Topic Suggestion")
with st.spinner(Loading Model):
topic_classifier = load_topic_transfomers()
  st.success(Model_loaded)

whole_text = st.text_input("Enter the text Here: ")
predicted_topic = suggest_topic(whole_text)

st.write('Suggested Topics')
for i in predicted_topic:
  st.write(i)