app.py

import streamlit as st


from annotated_text import annotated_text
from nltk.tokenize import word_tokenize

import warnings
import pandas as pd
from pandas import DataFrame


warnings.filterwarnings('ignore')
import re, flair, random, time
from bnlp import BasicTokenizer
from flair.data import Corpus, Sentence
from flair.datasets import ColumnCorpus

from flair.models import SequenceTagger
from flair.trainers import ModelTrainer

from huggingface_hub import hf_hub_download


st.set_page_config(
   page_title="Indic POS Tagger",
   page_icon="✔️",
   layout="wide",
)

model_path = hf_hub_download(
    repo_id="atanu0491/IndicPOSModel",
    filename="best-model-002.pt"
)



@st.cache_resource()
def load_model(model_name):
    model = SequenceTagger.load(model_name)
    return (model)


st.markdown('''<h1><center><b><u>BIS POS Tagset</u></b><center></h1>''', unsafe_allow_html=True)

activity = ['Select your Choice', 'File Upload', 'Text Input']
#choice = st.sidebar.selectbox('আপনি কিভাবে এটি প্রক্রিয়া করতে চান?',activity)
choice = st.selectbox('How you want to proceed?', activity)

# st.sidebar.markdown('''<h1><center><b><u>BIS POS Tagset</u></b><center></h1>''', unsafe_allow_html=True)

tag_activity = ['TAG', '<unk>', 'CC_CCD', 'CC_CCS', 'CC_CCS_UT', 'DM_DMD', 'DM_DMR', 'DM_DMQ', 'JJ', 'N_NN', 'N_NNP', 'N_NNV', 'N_NST', 'PR_PRC', 'PR_PRF', 'PR_PRL', 'PR_PRP', 'PR_PRQ', 'PSP', 'QT_QTC', 'QT_QTF', 'RB', 'RD_ECH', 'RD_PUNC', 'RD_RDF', 'RD_SYM', 'RD_UNK', 'RP_CL', 'RP_INJ', 'RP_INTF', 'RP_NEG', 'RP_RPD', 'V_VAUX', 'V_VM', 'V_VM_VF', 'V_VM_VINF', 'V_VM_VNF', 'V_VM_VNG', 'QT_QTO']
tag_choice = st.sidebar.selectbox('Search the Tag you want to know', tag_activity)

if tag_choice == 'TAG':
    st.sidebar.info('Select the TAG', icon="ℹ️")
if tag_choice == '<unk>':
    st.sidebar.info('Unknown', icon="ℹ️") 
if tag_choice == 'CC_CCD':
    st.sidebar.info('Co-ordinator', icon="ℹ️") 
if tag_choice == 'CC_CCS':
    st.sidebar.info('Subordinator', icon="ℹ️") 
if tag_choice == 'CC_CCS_UT':
    st.sidebar.info('Quotative', icon="ℹ️") 
if tag_choice == 'DM_DMD':
    st.sidebar.info('Deictic demonstrative', icon="ℹ️") 
if tag_choice == 'DM_DMR':
    st.sidebar.info('Relative demonstrative', icon="ℹ️") 
if tag_choice == 'DM_DMQ':
    st.sidebar.info('Wh-word', icon="ℹ️") 
if tag_choice == 'JJ':
    st.sidebar.info('Adjective', icon="ℹ️") 
if tag_choice == 'N_NN':
    st.sidebar.info('Common noun', icon="ℹ️") 
if tag_choice == 'N_NNP':
    st.sidebar.info('Proper noun', icon="ℹ️") 
if tag_choice == 'N_NNV':
    st.sidebar.info('Verbal noun', icon="ℹ️") 
if tag_choice == 'N_NST':
    st.sidebar.info('Locative noun', icon="ℹ️") 
if tag_choice == 'PR_PRC':
    st.sidebar.info('Reciprocal pronoun', icon="ℹ️") 
if tag_choice == 'PR_PRF':
    st.sidebar.info('Reflexive pronoun', icon="ℹ️") 
if tag_choice == 'PR_PRL':
    st.sidebar.info('Relative pronoun', icon="ℹ️") 
if tag_choice == 'PR_PRP':
    st.sidebar.info('Personal pronoun', icon="ℹ️") 
if tag_choice == 'PR_PRQ':
    st.sidebar.info('Wh-word', icon="ℹ️") 
if tag_choice == 'PSP':
    st.sidebar.info('Postposition', icon="ℹ️") 
if tag_choice == 'QT_QTC':
    st.sidebar.info('Cardinals', icon="ℹ️") 
if tag_choice == 'QT_QTF':
    st.sidebar.info('General quantifier', icon="ℹ️") 
if tag_choice == 'RB':
    st.sidebar.info('Adverb', icon="ℹ️") 
if tag_choice == 'RD_ECH':
    st.sidebar.info('Echo words', icon="ℹ️") 
if tag_choice == 'RD_PUNC':
    st.sidebar.info('Punctuation', icon="ℹ️") 
if tag_choice == 'RD_RDF':
    st.sidebar.info('Foreign words', icon="ℹ️") 
if tag_choice == 'RD_SYM':
    st.sidebar.info('Symbol', icon="ℹ️") 
if tag_choice == 'RD_UNK':
    st.sidebar.info('Unknown', icon="ℹ️") 
if tag_choice == 'RP_CL':
    st.sidebar.info('Classifier particle', icon="ℹ️") 
if tag_choice == 'RP_INJ':
    st.sidebar.info('Interjection particle', icon="ℹ️") 
if tag_choice == 'RP_INTF':
    st.sidebar.info('Intensifier particle', icon="ℹ️") 
if tag_choice == 'RP_NEG':
    st.sidebar.info('Negation particle', icon="ℹ️") 
if tag_choice == 'RP_RPD':
    st.sidebar.info('Default particle', icon="ℹ️") 
if tag_choice == 'V_VAUX':
    st.sidebar.info('Auxiliary verb', icon="ℹ️") 
if tag_choice == 'V_VM':
    st.sidebar.info('Main verb', icon="ℹ️") 
if tag_choice == 'V_VM_VF':
    st.sidebar.info('Finite verb', icon="ℹ️") 
if tag_choice == 'V_VM_VINF':
    st.sidebar.info('Infinite verb', icon="ℹ️") 
if tag_choice == 'V_VM_VNF':
    st.sidebar.info('Non-finite verb', icon="ℹ️") 
if tag_choice == 'V_VM_VNG':
    st.sidebar.info('Gerund verb', icon="ℹ️") 
if tag_choice == 'QT_QTO':
    st.sidebar.info('Ordinals', icon="ℹ️") 
# st.sidebar.info('''
# <unk> --> _Unknown_  
# CC_CCD --> _Co-ordinator_  
# CC_CCS --> _Subordinator_  
# CC_CCS_UT --> _Quotative_  
# DM_DMD --> _Deictic demonstrative_  
# DM_DMR --> _Relative demonstrative_  
# DM_DMQ --> _Wh-word_  
# JJ --> _Adjective_  
# N_NN --> _Common noun_  
# N_NNP --> _Proper noun_  
# N_NNV --> _Verbal noun_  
# N_NST --> _Locative noun_  
# PR_PRC --> _Reciprocal pronoun_  
# PR_PRF --> _Reflexive pronoun_  
# PR_PRL --> _Relative pronoun_  
# PR_PRP --> _Personal pronoun_  
# PR_PRQ --> _Wh-word_  
# PSP --> _Postposition_  
# QT_QTC --> _Cardinals_  
# QT_QTF --> _General quantifier_  
# RB --> _Adverb_  
# RD_ECH --> _Echo words_  
# RD_PUNC --> _Punctuation_  
# RD_RDF --> _Foreign words_  
# RD_SYM --> _Symbol_  
# RD_UNK --> _Unknown_  
# RP_CL --> _Classifier particle_  
# RP_INJ --> _Interjection particle_  
# RP_INTF --> _Intensifier particle_  
# RP_NEG --> _Negation particle_  
# RP_RPD --> _Default particle_  
# V_VAUX --> _Auxiliary verb_  
# V_VM --> _Main verb_  
# V_VM_VF --> _Finite verb_  
# V_VM_VINF --> _Infinite verb_  
# V_VM_VNF --> _Non-finite verb_  
# V_VM_VNG --> _Gerund verb_  
# QT_QTO --> _Ordinals_  
# ''')
st.sidebar.info('Last updated on: 23 April 2025', icon="✅")



model = load_model(model_path)
#model = load_model('best-model-002.pt')
#model = SequenceTagger.load('best-model-002.pt')



if choice == 'Text Input':
    input_data = st.text_area("Write your sentence below", value="", height=68)
    if st.button('Click to execute'):
       
        data = BasicTokenizer().tokenize(input_data)
        # data = word_tokenize(input_data)
        
       
        sentence = Sentence(data)
        # model = load_model('best-model-002.pt')
        model.predict(sentence)
        my_list = []
      
        for token in sentence:
            word = []
            word.append(token.text)
            word.append(token.tag)
            my_list.append(tuple(word))
        annotated_text(my_list)


# if choice == 'File Upload':
#     uploaded_file = st.file_uploader("Upload your File in .txt format", type='.txt')
#     if uploaded_file is not None:
#         lines = uploaded_file.read().decode('utf-8').splitlines()

#         # Define output file name
#         output_file_name = uploaded_file.name.split('.')[0] + '_tagged.tsv'

#         with st.spinner("Wait for processing the file...", show_time=True):
#             with open(output_file_name, 'w', encoding='utf-8') as out_file:
#                 for line in lines:
#                     data = BasicTokenizer().tokenize(line)
#                     sentence = Sentence(data)
#                     model.predict(sentence)
#                     my_list = []
                    
#                     for token in sentence:
#                         word = (token.text, token.tag)
#                         my_list.append(word)
                        
#                     # Write line to output file
#                     raw_line = ' '.join([f"{word}" for word, tag in my_list])
#                     tagged_line = ' '.join([f"{word}/{tag}" for word, tag in my_list])
#                     out_file.write(raw_line + '\t' + tagged_line + '\n')
                    
#         with open(output_file_name, "rb") as f:
#             btn = st.download_button(
#                 label="Download the tagged data in tsv (tab separated value) format",
#                 data=f,
#                 file_name=output_file_name,
#                 mime="text/plain"
#             )
if choice == 'File Upload':
    uploaded_file = st.file_uploader("Upload your File in .txt format", type='.txt')
    if uploaded_file is not None:
        lines = uploaded_file.read().decode('utf-8').splitlines()

        # Define output Excel file name
        output_file_name = uploaded_file.name.split('.')[0] + '_tagged.xlsx'

        raw_sentences = []
        tagged_sentences = []

        with st.spinner("Wait for processing the file..."):
            for line in lines:
                data = BasicTokenizer().tokenize(line)
                sentence = Sentence(data)
                model.predict(sentence)
                my_list = []

                for token in sentence:
                    word = (token.text, token.tag)
                    my_list.append(word)

                raw_line = ' '.join([f"{word}" for word, tag in my_list])
                tagged_line = ' '.join([f"{word}/{tag}" for word, tag in my_list])

                raw_sentences.append(raw_line)
                tagged_sentences.append(tagged_line)

            # Save to Excel
            df = pd.DataFrame({
                "Raw Sentence": raw_sentences,
                "Tagged Sentence": tagged_sentences
            })

            df.to_excel(output_file_name, index=False)

        # Provide download button
        with open(output_file_name, "rb") as f:
            btn = st.download_button(
                label="Download the tagged data in Excel (.xlsx) format",
                data=f,
                file_name=output_file_name,
                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
            )
        


st.info('An initiative of Natural Language Processing Lab, Jadavpur University', icon="📚")  

# st.success('For any issues/query you can mail us at atanumandal0491@gmail.com', icon="✅")