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

def start_server():   
    os.system("python3 -m spacy download en_core_web_sm")
    os.system("uvicorn GrammarTokenize:app --port 8080 --host 0.0.0.0 --workers 2")

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):
    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):
    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
    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__":
    
        st.title('Grammar Styler')
        st.subheader('Grammar and sentence structure restyler')
        examples = [
                     "I looked at the med cabinet and meds are out.  Can you order me more?",
                    "Been spendin my whole life jus to her dat song",
                    "whatdjya think about dat?",
                    "Lets git sum holesome waves and go surfin"
                    ]

        if not st.session_state['models_loaded']:
            load_models()                     

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

        st.markdown(f'##### Try it now:')
        input_text = st.selectbox(
        label="Choose an example",
        options=examples
        )
        st.write("(or)")
        input_text = st.text_input(
            label="Bring your own sentence",
            value=input_text
        )

        if input_text.strip(): 
            get_correction(input_text)