app.py

from annotated_text import annotated_text
from bs4 import BeautifulSoup
from multiprocessing import Process
import streamlit as st
import pandas as pd
import torch
import math
import re
import time
import json
import os
import requests
import spacy
import errant



def start_server():    
    os.system("cat custom_req.txt | xargs -n 1 -L 1 pip install -U")
    os.system("uvicorn InferenceServer:app --port 8080 --host 0.0.0.0 --workers 1")

def load_models():
    if not is_port_in_use(8080):
        with st.spinner(text="Loading models, please wait..."):
            proc = Process(target=start_server, args=(), daemon=True)
            proc.start()
            while not is_port_in_use(8080):
                time.sleep(1)
            st.success("Model server started.")
    else:
        st.success("Model server already running...")
    st.session_state['models_loaded'] = True

def is_port_in_use(port):
    import socket
    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        return s.connect_ex(('0.0.0.0', port)) == 0

if 'models_loaded' not in st.session_state:
    st.session_state['models_loaded'] = False


def show_highlights(input_text, corrected_sentence):
    """
        To show highlights
    """
    try:
        strikeout = lambda x: '\u0336'.join(x) + '\u0336'
        highlight_text = highlight(input_text, corrected_sentence)
        color_map = {'d':'#faa', 'a':'#afa', 'c':'#fea'}
        tokens = re.split(r'(<[dac]\s.*?<\/[dac]>)', highlight_text)
        annotations = []
        for token in tokens:
            soup = BeautifulSoup(token, 'html.parser')
            tags = soup.findAll()
            if tags:
                _tag = tags[0].name
                _type = tags[0]['type']
                _text = tags[0]['edit']
                _color = color_map[_tag]

                if _tag == 'd':
                    _text = strikeout(tags[0].text)

                annotations.append((_text, _type, _color))
            else:
                annotations.append(token)
        annotated_text(*annotations)
    except Exception as e:
        st.error('Some error occured!' + str(e))
        st.stop()

def show_edits(input_text, corrected_sentence):
    """
        To show edits
    """
    try:
        edits = get_edits(input_text, corrected_sentence)
        df = pd.DataFrame(edits, columns=['type','original word', 'original start', 'original end', 'correct word', 'correct start', 'correct end'])
        df = df.set_index('type')
        st.table(df)
    except Exception as e:
        st.error('Some error occured!')
        st.stop()

def highlight(orig, cor):
      edits = _get_edits(orig, cor)
      orig_tokens = orig.split()

      ignore_indexes = []

      for edit in edits:
          edit_type = edit[0]
          edit_str_start = edit[1]
          edit_spos = edit[2]
          edit_epos = edit[3]
          edit_str_end = edit[4]

          # if no_of_tokens(edit_str_start) > 1 ==> excluding the first token, mark all other tokens for deletion
          for i in range(edit_spos+1, edit_epos):
            ignore_indexes.append(i)

          if edit_str_start == "":
              if edit_spos - 1 >= 0:
                  new_edit_str = orig_tokens[edit_spos - 1]
                  edit_spos -= 1
              else:
                  new_edit_str = orig_tokens[edit_spos + 1]
                  edit_spos += 1
              if edit_type == "PUNCT":
                st = "<a type='" + edit_type + "' edit='" + \
                    edit_str_end + "'>" + new_edit_str + "</a>"
              else:
                st = "<a type='" + edit_type + "' edit='" + new_edit_str + \
                    " " + edit_str_end + "'>" + new_edit_str + "</a>"
              orig_tokens[edit_spos] = st
          elif edit_str_end == "":
            st = "<d type='" + edit_type + "' edit=''>" + edit_str_start + "</d>"
            orig_tokens[edit_spos] = st
          else:
            st = "<c type='" + edit_type + "' edit='" + \
                edit_str_end + "'>" + edit_str_start + "</c>"
            orig_tokens[edit_spos] = st

      for i in sorted(ignore_indexes, reverse=True):
        del(orig_tokens[i])

      return(" ".join(orig_tokens))


def _get_edits(orig, cor):
    orig = annotator.parse(orig)
    cor = annotator.parse(cor)
    alignment = annotator.align(orig, cor)
    edits = annotator.merge(alignment)

    if len(edits) == 0:  
        return []

    edit_annotations = []
    for e in edits:
        e = annotator.classify(e)
        edit_annotations.append((e.type[2:], e.o_str, e.o_start, e.o_end,  e.c_str, e.c_start, e.c_end))
            
    if len(edit_annotations) > 0:
        return edit_annotations
    else:    
        return []

def get_edits(orig, cor):
    return _get_edits(orig, cor)        

def get_correction(input_text):
    correct_request = "http://0.0.0.0:8080/correct?input_sentence="+input_text
    correct_response = requests.get(correct_request)
    correct_json = json.loads(correct_response.text)
    scored_corrected_sentence = correct_json["scored_corrected_sentence"]
    
    corrected_sentence, score = scored_corrected_sentence[0]
    st.markdown(f'##### Corrected text:')
    st.write('')
    st.success(corrected_sentence)
    exp1 = st.expander(label='Show highlights', expanded=True)
    with exp1:
        show_highlights(input_text, corrected_sentence)
    exp2 = st.expander(label='Show edits')
    with exp2:
        show_edits(input_text, corrected_sentence)
          
        
if __name__ == "__main__":
    if not st.session_state['models_loaded']:
        load_models() 


st.title('Gramformer')
st.subheader('A framework for correcting english grammatical errors')
st.markdown("Built for fun with 💙  by a quintessential foodie - Prithivi Da, The maker of [WhatTheFood](https://huggingface.co/spaces/prithivida/WhatTheFood), [Styleformer](https://github.com/PrithivirajDamodaran/Styleformer) and [Parrot paraphraser](https://github.com/PrithivirajDamodaran/Parrot_Paraphraser) | ✍️ [@prithivida](https://twitter.com/prithivida) |[[GitHub]](https://github.com/PrithivirajDamodaran)", unsafe_allow_html=True)

examples = [
            "what be the reason for everyone leave the comapny",
            "He are moving here.",
            "I am doing fine. How is you?",
            "How is they?",
            "Matt like fish",
            "the collection of letters was original used by the ancient Romans",
            "We enjoys horror movies",
            "Anna and Mike is going skiing",
            "I walk to the store and I bought milk",
            " We all eat the fish and then made dessert",
            "I will eat fish for dinner and drink milk",
            ]

nlp = spacy.load('en_core_web_sm')
annotator = errant.load('en', nlp)

input_text = st.selectbox(
label="Choose an example",
options=examples
)
st.write("(or)")
input_text = st.text_input(
    label="Enter your own text",
    value=input_text
)

if input_text.strip(): 
    get_correction(input_text)