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import streamlit as st
@st.cache_resource
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")
if 'topic_model' not in st.session_state:
with st.spinner("Loading Model....."):
topic_classifier = load_topic_transfomers()
st.success("Model_loaded")
st.session_state.topic_model = True
whole_text = st.text_input("Enter the text Here: ")
try:
predicted_topic = suggest_topic(whole_text)
st.write('Suggested Topics')
for i in predicted_topic[:10]:
st.write(i)
except Exception as e:
print("Error", e)
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