import os, sys
import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
from sklearn.metrics.pairwise import paired_cosine_distances
from sklearn.preprocessing import normalize
from rolaser import RoLaserEncoder

laser_checkpoint = f"{os.environ['LASER']}/models/laser2.pt"
laser_vocab = f"{os.environ['LASER']}/models/laser2.cvocab"
laser_tokenizer = 'spm'
laser_model = RoLaserEncoder(model_path=laser_checkpoint, vocab=laser_vocab, tokenizer=laser_tokenizer)

rolaser_checkpoint = f"{os.environ['ROLASER']}/models/rolaser.pt"
rolaser_vocab = f"{os.environ['ROLASER']}/models/rolaser.cvocab"
rolaser_tokenizer = 'roberta'
rolaser_model = RoLaserEncoder(model_path=rolaser_checkpoint, vocab=rolaser_vocab, tokenizer=rolaser_tokenizer)

c_rolaser_checkpoint = f"{os.environ['ROLASER']}/models/c-rolaser.pt"
c_rolaser_vocab = f"{os.environ['ROLASER']}/models/c-rolaser.cvocab"
c_rolaser_tokenizer = 'char'
c_rolaser_model = RoLaserEncoder(model_path=c_rolaser_checkpoint, vocab=c_rolaser_vocab, tokenizer=c_rolaser_tokenizer)


STD_SENTENCES = ['See you tomorrow.'] * 10
UGC_SENTENCES = [
    'See you t03orro3.',
    'C. U. tomorrow.',
    'sea you tomorrow.',
    'See yo utomorrow.',
    'See you tmrw.',
    'See you tkmoerow.',
    'Cu 2moro.',
    'See yow tomorrow.',
    'C. Yew tomorrow.',
    'c ya 2morrow.'
]

def add_text_inputs(i):
    col1, col2 = st.columns(2)
    with col1:
        text_input1 = st.text_input('Enter standard text here:', key=f'std{i}', value=STD_SENTENCES[i])
    with col2:
        text_input2 = st.text_input('Enter non-standard text here:', key=f'ugc{i}', value=UGC_SENTENCES[i])
    return text_input1, text_input2

def main():
    st.title('Pairwise Cosine Distance Calculator')

    info = '''
    :bookmark: **Paper:** [Making Sentence Embeddings Robust to User-Generated Content (Nishimwe et al., 2024)](https://arxiv.org/abs/2403.17220)  
    :link: **Github:** [https://github.com/lydianish/RoLASER](https://github.com/lydianish/RoLASER)

    This demo app computes text embeddings of sentence pairs using the LASER encoder and its robust students RoLASER and c-RoLASER. 
    The pairwise cosine distances between the sentences are then computed and displayed. 
    '''
    st.markdown(info)
            
    st.header('Standard and Non-standard Text Input Pairs:')

    num_pairs = st.sidebar.number_input('Number of Text Input Pairs', min_value=1, max_value=10, value=5)

    std_text_inputs = []
    ugc_text_inputs = []
    for i in range(num_pairs):
        pair = add_text_inputs(i)
        std_text_inputs.append(pair[0])
        ugc_text_inputs.append(pair[1])

    if st.button('Submit'):
        X_std_laser = normalize(laser_model.encode(std_text_inputs))
        X_ugc_laser = normalize(laser_model.encode(ugc_text_inputs))
        X_cos_laser = paired_cosine_distances(X_std_laser, X_ugc_laser)

        X_std_rolaser = normalize(rolaser_model.encode(std_text_inputs))
        X_ugc_rolaser = normalize(rolaser_model.encode(ugc_text_inputs))
        X_cos_rolaser = paired_cosine_distances(X_std_rolaser, X_ugc_rolaser)

        X_std_c_rolaser = normalize(c_rolaser_model.encode(std_text_inputs))
        X_ugc_c_rolaser = normalize(c_rolaser_model.encode(ugc_text_inputs))
        X_cos_c_rolaser = paired_cosine_distances(X_std_c_rolaser, X_ugc_c_rolaser)

        outputs = pd.DataFrame(columns=[ 'model', 'pair', 'ugc', 'std', 'cos'])
        outputs['model'] = np.repeat(['LASER', 'RoLASER', 'c-RoLASER'], num_pairs)   
        outputs['pair'] = np.tile(np.arange(1,num_pairs+1), 3)
        outputs['std'] = np.tile(std_text_inputs, 3)
        outputs['ugc'] = np.tile(ugc_text_inputs, 3)
        outputs['cos'] = np.concatenate([X_cos_laser, X_cos_rolaser, X_cos_c_rolaser])

        st.header('Cosine Distance Scores:')
        st.caption('*This bar plot is interactive: Hover on the bars to display values. Click on the legend to filter models.*')
        fig = px.bar(outputs, x='pair', y='cos', color='model', barmode='group', hover_data=['ugc', 'std'])
        fig.update_xaxes(title_text='Text Input Pair')
        fig.update_yaxes(title_text='Cosine Distance')
        st.plotly_chart(fig, use_container_width=True)

        st.header('Average Cosine Distance Scores:')
        st.caption('*This data table is interactive: Click on a column header to sort values.*')
        st.write(outputs.groupby('model')['cos'].describe())

        
if __name__ == "__main__":
    main()