from classifier import classify
from PIL  import Image
import streamlit as st

st.title("Twitter Sentiment Analysis using BERT model")

st.subheader("Motivation")
st.markdown("""
Cyberbullying is a serious problem in today's world. It is a form of bullying that takes place using electronic technology. This model will act as an tool for the detection of the abusive content 
in the tweets. This model can be used by the social media platforms to detect the abusive content in the tweets and take necessary action.

Huggingface provides an easy interfce to test the models before the use.
""")


text = st.text_input("Enter a tweet to classify it as either Normal or Abusive. (Press enter to submit)", 
              value="I love DCNM course", max_chars=512, key=None, type="default", 
              help=None, autocomplete=None) 
st.markdown(f"The tweet is classified as: **{classify(text)}**")

st.markdown("Try out for abusive _Giving and taking dowry is crappy thing_")

st.subheader("About the model")
st.markdown("""
Model was trained on twitter dataset ENCASEH2020 from Founta, A.M et. al. (2018) [3]. BERT Tiny model [1][2][5] was chosen for this project because, empirically, 
giving better result with least number of parameters. The model was trained for 10 epochs with batch size of 32 and AdamW optimizer with learning rate of 1e-2 and loss as cross entropy.
""")

st.image("./images/train_val_accuracy.png [4]", caption="Train and Validation Accuracy", use_column_width=True)
st.image("./images/train_test_scores.png [4]", caption="Classification Report", use_column_width=True)
st.image("./images/confusion_matrix.png [4]", caption="Confusion Matrix", use_column_width=True)

st.subheader("References")
st.markdown("1. [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)")
st.markdown("2. [BERT-Tiny: A Tiny BERT for Natural Language Understanding](https://arxiv.org/abs/1909.10351)")
st.markdown("3. [Founta, A.M., Djouvas, C., Chatzakou, D., Leontiadis, I., Blackburn, J., Stringhini, G., Vakali, A., Sirivianos, M., & Kourtellis, N. (2018).Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior. In 11th International Conference on Web and Social Media, ICWSM 2018.](https://arxiv.org/abs/1802.00393)")
st.markdown("4. [Ajay S, Ram, Kowsik N D, Navaneeth D, Amarnath C N, Cyberbullying Detection using Bidirectional Encoder Representation from Transformers 2022](https://github.com/Cubemet/bert-models)")
st.markdown("5. [Base Model from nreimers](https://huggingface.co/nreimers/BERT-Tiny_L-2_H-128_A-2")