app.py

import streamlit as st
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer


# @st.cache(allow_output_mutation=True)
def get_model():
    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
    pulled_model = AutoModelForSequenceClassification.from_pretrained(
        "NazmusAshrafi/large_dataset_stock_twitter_topic_Bert")
    return tokenizer, pulled_model


tokenizer, model = get_model()


classifier = pipeline("sentiment-analysis",
                      model=model, tokenizer=tokenizer)


# print(classifier("AAP Up Theory = Will go up in future, buy now"))

st.title("Find the topic of a stock related tweets")
st.subheader(
    'This model can predict 5 topics - :blue[Tesla Investment News], :green[Technology stock watchlist], :orange[Esports News], :blue[Apple Market Updates], :green[Amazon Updates] - Entering a topic related tweet will yeild the best results')

# elif topic == 0:
#     return 'Tesla Investment News'
# elif topic == 1:
#     return 'Technology stock watchlist'
# elif topic == 2:
#     return 'Esports News'
# elif topic == 3:
#     return 'Apple Market Updates'
# elif topic == 4:
#     return 'Amazon Updates'

st.subheader(
    'Examples:')
st.markdown(
    ':blue[Tesla Investment News: "Electric cars stock going to go up in future, buy now"]')
st.markdown(
    ':green[Technology stock watchlist: "Keep a look out for that stock, its really good!"]')
st.markdown(
    ':orange[Esports News: "Ninja is going to play in the next tournament, the best is back playing Fortnite."]')
st.markdown(
    ':blue[Apple Market Updates: "$AAPL released a new phone but the looks never change much. Buy anyway!"]')
st.markdown(
    ':green[Amazon Updates: "Black Fridays at Amazon, hail Jeff Bezos"]')


st.subheader("", divider='rainbow')


user_input = st.text_area("Enter a tweet about a stock")
button = st.button("Analyze")

# print(classifier("AAP Down Theory = Will go down in future,do not buy now"))


if user_input and button:
    # output
    st.write("Topic Prediction: ", classifier(user_input)[0]['label'])
    st.write("Confidence Score: ", classifier(user_input)[0]['score'])


# st.markdown(
#     'Note: Model may be predicting "Investment decision" way too often, this is because of the data imbalance. The model was trained on 3 topics, but the data was not balanced. The model was trained on a dataset where a large portion of the data was on "Investment decision" topic. This is why the model is biased towards "Investment decision" topic. This is a flaw of this model, and it can be fixed by training the model on a balanced dataset.')