# -*- coding: utf-8 -*-
"""
Created on Mon Nov 21 16:21:25 2022

@author: luol2
"""

import streamlit as st
from src.nn_model import bioTag_CNN,bioTag_Bioformer
from src.dic_ner import dic_ont
from src.tagging_text import bioTag
import os
import json
from pandas import DataFrame
import nltk 
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')

st.set_page_config(
    page_title="PhenoTagger",
    page_icon="🎈",
    layout="wide",
    menu_items={
        'Get Help': 'https://www.ncbi.nlm.nih.gov/research/bionlp/',
        'About': "PhenoTagger v1.1"
    }
)


# def _max_width_():
#     max_width_str = f"max-width: 2400px;"
#     st.markdown(
#         f"""
#     <style>
#     .reportview-container .main .block-container{{
#         {max_width_str}
#     }}
#     </style>    
#     """,
#         unsafe_allow_html=True,
#     )


# _max_width_()

# c30, c31, c32 = st.columns([2.5, 1, 3])

# with c30:
#     # st.image("logo.png", width=400)
st.title("👨‍⚕️ PhenoTagger Demo")

with st.expander("ℹ️ - About this app", expanded=True):

    st.write(
        """     
-   This app is an easy-to-use interface built in Streamlit for [PhenoTagger](https://github.com/ncbi-nlp/PhenoTagger) library!
-   PhenoTagger is a hybrid method that combines dictionary and deep learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text. Please refer to [our paper](https://doi.org/10.1093/bioinformatics/btab019) for more details.
-   Contact: [NLM/NCBI BioNLP Research Group](https://www.ncbi.nlm.nih.gov/research/bionlp/)
	    """
    )

    st.markdown("")

st.markdown("")
st.markdown("## 📌 Paste document ")
with st.form(key="my_form"):


    ce, c1, ce, c2, c3 = st.columns([0.07, 1, 0.07, 4, 0.07])
    with c1:
        ModelType = st.radio(
            "Choose your model",
            ["Bioformer(Default)", "CNN"],
            help="Bioformer is more precise, CNN is more efficient",
        )

        if ModelType == "Bioformer(Default)":
            # kw_model = KeyBERT(model=roberta)

            @st.cache(allow_output_mutation=True)
            def load_model():
                ontfiles={'dic_file':'./dict_new/noabb_lemma.dic',
                  'word_hpo_file':'./dict_new/word_id_map.json',
                  'hpo_word_file':'./dict_new/id_word_map.json'}
        

                vocabfiles={'labelfile':'./dict_new/lable.vocab',
                            'config_path':'./vocab/bioformer-cased-v1.0/bert_config.json',
                            'checkpoint_path':'./vocab/bioformer-cased-v1.0/bioformer-cased-v1.0-model.ckpt-2000000',
                            'vocab_path':'./vocab/bioformer-cased-v1.0/vocab.txt'}
                modelfile='./vocab/bioformer_p5n5_b64_1e-5_95_hponew3.h5'
       
    
                biotag_dic=dic_ont(ontfiles)    
    
                nn_model=bioTag_Bioformer(vocabfiles)
                nn_model.load_model(modelfile)
                return nn_model,biotag_dic

            nn_model,biotag_dic = load_model()

        else:
            @st.cache(allow_output_mutation=True)
            def load_model():
                ontfiles={'dic_file':'./dict_new/noabb_lemma.dic',
                  'word_hpo_file':'./dict_new/word_id_map.json',
                  'hpo_word_file':'./dict_new/id_word_map.json'}
        

                vocabfiles={'w2vfile':'./vocab/bio_embedding_intrinsic.d200',   
                            'charfile':'./vocab/char.vocab',
                            'labelfile':'./dict_new/lable.vocab',
                            'posfile':'./vocab/pos.vocab'}
                modelfile='./vocab/cnn_p5n5_b128_95_hponew1.h5'
        
                biotag_dic=dic_ont(ontfiles)    
            
                nn_model=bioTag_CNN(vocabfiles)
                nn_model.load_model(modelfile)
            
                return nn_model,biotag_dic

            nn_model,biotag_dic = load_model()
        
        para_overlap = st.checkbox(
            "Overlap concept",
            value=True,
            help="Tick this box to identify overlapping concepts",
        )
        para_abbr = st.checkbox(
            "Abbreviaitons",
            value=True,
            help="Tick this box to identify abbreviations",
        )        
        
        para_threshold = st.slider(
            "Threshold",
            min_value=0.5,
            max_value=1.0,
            value=0.95,
            step=0.05,
            help="Retrun the preditions which socre over the threshold.",
        )
      



    with c2:
        ex_but=st.button('example')
            
        doc = st.text_area(
              "Paste your text below",
              height=400,
            )
        if ex_but:
            doc = st.text_area(
                  "Paste your text below",
                  value='He denies synophrys. Individual II-1 is a 10 year old boy. He was born at term with normal birth parameters and good APGAR scores (9/10/10). The neonatal period was uneventful, and he had normal motor development during early childhood: he began to look up at 3 months, sit by himself at 5 months, stand up at 11 months, walk at 13 months, and speak at 17 months. He attended a regular kindergarten, without any signs of difference in intelligence, compared to his peers. Starting at age 6, the parents observed ever increasing behavioral disturbance for the boy, manifesting in multiple aspects of life. For example, he can no longer wear clothes by himself, cannot obey instruction from parents/teachers, can no longer hold subjects tightly in hand, which were all things that he could do before 6 years of age. In addition, he no longer liked to play with others; instead, he just preferred to stay by himself, and he sometimes fell down when he walked on the stairs, which had rarely happened at age 5. The proband continued to deteriorate: at age 9, he could not say a single word and had no action or response to any instruction given in clinical exams. Additionally, rough facial features were noted with a flat nasal bridge, a synophrys (unibrow), a long and smooth philtrum, thick lips and an enlarged mouth. He also had rib edge eversion, and it was also discovered that he was profoundly deaf and had completely lost the ability to speak. He also had loss of bladder control. The diagnosis of severe intellectual disability was made, based on Wechsler Intelligence Scale examination. Brain MRI demonstrated cortical atrophy with enlargement of the subarachnoid spaces and ventricular dilatation (Figure 2). Brainstem evoked potentials showed moderate abnormalities. Electroencephalography (EEG) showed abnormal sleep EEG.',
                  height=400,
            )
        

        

        # MAX_WORDS = 500
        # import re
        # res = len(re.findall(r"\w+", doc))
        # if res > MAX_WORDS:
        #     st.warning(
        #         "⚠️ Your text contains "
        #         + str(res)
        #         + " words."
        #         + " Only the first 500 words will be reviewed. Stay tuned as increased allowance is coming! 😊"
        #     )

        #     doc = doc[:MAX_WORDS]

        submit_button = st.form_submit_button(label="✨ Submit!")


if not submit_button:
    st.stop()


para_set={
          #model_type':para_model, # cnn or bioformer
          'onlyLongest':para_overlap, # False: return overlap concepts, True only longgest
          'abbrRecog':para_abbr,# False: don't identify abbr, True: identify abbr
          'ML_Threshold':para_threshold,# the Threshold of deep learning model
          }
st.markdown("")
st.markdown("## 💡 Tagging results:")
with st.spinner('Wait for tagging...'):
    tag_result=bioTag(doc,biotag_dic,nn_model,onlyLongest=para_set['onlyLongest'], abbrRecog=para_set['abbrRecog'],Threshold=para_set['ML_Threshold'])

st.markdown('<font style="color: rgb(128, 128, 128);">Move the mouse🖱️ over the entity to display the HPO id.</font>', unsafe_allow_html=True)
# print('dic...........:',biotag_dic.keys())
# st.write('parameters:', para_overlap,para_abbr,para_threshold)

html_results=''
text_results=doc+'\n'
entity_end=0
hpoid_count={}
if len(tag_result)>=0:
    for ele in tag_result:
        entity_start=int(ele[0])
        html_results+=doc[entity_end:entity_start]
        entity_end=int(ele[1])
        entity_id=ele[2]
        entity_score=ele[3]
        text_results+=ele[0]+'\t'+ele[1]+'\t'+doc[entity_start:entity_end]+'\t'+ele[2]+'\t'+format(float(ele[3]),'.2f')+'\n'
        if entity_id not in hpoid_count.keys():
            hpoid_count[entity_id]=1
        else:
            hpoid_count[entity_id]+=1
        
        html_results+='<font style="background-color: rgb(255, 204, 0)'+';" title="'+entity_id+'">'+doc[entity_start:entity_end]+'</font>'
    html_results+=doc[entity_end:]
                
else:
    html_results=doc
    
st.markdown('<table border="1"><tr><td>'+html_results+'</td></tr></table>', unsafe_allow_html=True)


#table
data_entity=[]
for ele in hpoid_count.keys():
    temp=[ele,biotag_dic.hpo_word[ele][0],hpoid_count[ele]] #hpoid, term name, count
    data_entity.append(temp)


st.markdown("")
st.markdown("")
# st.markdown("## Table output:")

# cs, c1, c2, c3, cLast = st.columns([2, 1.5, 1.5, 1.5, 2])

# with c1:
#     CSVButton2 = download_button(keywords, "Data.csv", "📥 Download (.csv)")
# with c2:
#     CSVButton2 = download_button(keywords, "Data.txt", "📥 Download (.txt)")
# with c3:
#     CSVButton2 = download_button(keywords, "Data.json", "📥 Download (.json)")

# st.header("")

df = (
    DataFrame(data_entity, columns=["HPO_id", "Term name","Frequency"])
    .sort_values(by="Frequency", ascending=False)
    .reset_index(drop=True)
)

df.index += 1

c1, c2, c3 = st.columns([1, 4, 1])

# format_dictionary = {
#     "Relevancy": "{:.1%}",
# }

# df = df.format(format_dictionary)

with c2:
    st.table(df)
    
c1, c2, c3 = st.columns([1, 1, 1])
with c2:
    st.download_button('Download annotations', text_results)