from bleu import Bleu
from rouge import Rouge
from datasets import load_metric
from pathlib import Path
import streamlit as st
import streamlit.components.v1 as components
#from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py

rouge = Rouge()
bleu = Bleu()

def read_markdown_file(markdown_file):
    return Path(markdown_file).read_text()


def compute(data):
    return metric.compute(predictions=data["predictions"], references=data["references"])["accuracy"]

with st.sidebar.expander("Metric", expanded=True):
    metrics= ['rouge','bleu']
    metric_name = st.selectbox(
        f"Choose metric to explore:",
        metrics)

metric = load_metric(metric_name)
st.markdown("# You chose " + metric_name.upper())

st.markdown("## You can test it out below:")

reference = st.text_input('Input a reference sentence here:')
prediction = st.text_input('Input a prediction sentence here:')

predictions = []
predictions.append(prediction.split())
#print(predictions)
references = []
references.append(reference.split())
#print(references)

if metric_name == "bleu":
    score = metric.compute(predictions=predictions, references=[references])
    col1, col2, col3 = st.columns(3)
    col1.metric("BLEU", score['bleu'])
    col2.metric("Brevity penalty", score['brevity_penalty'])
    col3.metric('Length Ratio', score['length_ratio'])


if metric_name == "rouge":
    score = metric.compute(predictions=predictions, references=references)
    #print(score)
    col1, col2, col3 = st.columns(3)
    col1.metric("Rouge 1 Precision", score['rouge1'].mid.precision)
    col2.metric("Rouge 1 Recall", score['rouge1'].mid.recall)
    col3.metric("Rouge 1 FMeasure", score['rouge1'].mid.fmeasure)

    col4, col5, col6 = st.columns(3)
    col4.metric("Rouge 2 Precision", score['rouge2'].mid.precision)
    col5.metric("Rouge 2 Recall", score['rouge2'].mid.recall)
    col6.metric("Rouge 2 FMeasure", score['rouge2'].mid.fmeasure)


#    col1.metric("BLEU", score['bleu'])
#    col2.metric("Brevity penalty", score['brevity_penalty'])
#    col3.metric('Length Ratio', score['length_ratio'])

st.markdown('===================================================================================')
#components.html("""<hr style="height:10px;border:none;color:#333;background-color:#333;" /> """)

st.markdown(read_markdown_file(metric_name+"_metric_card.md"))