app.py

from transformers import T5Tokenizer, T5Model, T5ForConditionalGeneration, MarianMTModel, MarianTokenizer, pipeline
import nltk.data
import pandas as pd
import matplotlib.pyplot as plt

nltk.download('punkt')

import gradio as gr
from gradio.mix import Parallel

tokenizer_t5 = T5Tokenizer.from_pretrained("panggi/t5-base-indonesian-summarization-cased")
model_t5 = T5ForConditionalGeneration.from_pretrained("panggi/t5-base-indonesian-summarization-cased")

pretrained_sentiment = "ProsusAI/finbert"
pretrained_ner = "51la5/roberta-large-NER"

sentence_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')

tokenizer_translate = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-id-en")
model_translate = MarianMTModel.from_pretrained(
    "Helsinki-NLP/opus-mt-id-en")
#finetuned_model = MarianMTModel.from_pretrained(
#    "wolfrage89/annual_report_translation_id_en")

sentiment_pipeline = pipeline(
    "sentiment-analysis",
    model=pretrained_sentiment,
    tokenizer=pretrained_sentiment,
    return_all_scores=True
)

ner_pipeline = pipeline(
    "ner",
    model=pretrained_ner,
    tokenizer=pretrained_ner,
    grouped_entities=True
)

examples = [
    "Perusahaan industri e-commerce Indonesia, Bukalapak telah memberhentikan puluhan karyawan dari beberapa function; Berlawanan dengan PHK sebelumnya, perusahaan mengontrak jajaran pekerja kantornya, harian Kompas melaporkan.",
    "Dengan pabrik produksi baru, perusahaan akan meningkatkan kapasitasnya untuk memenuhi peningkatan permintaan yang diharapkan dan akan meningkatkan penggunaan bahan baku dan oleh karena itu meningkatkan profitabilitas produksi.",
    "Lifetree didirikan pada tahun 2000, dan pendapatannya meningkat rata-rata 40% dengan margin di akhir 30-an."
]

def get_translation(text):
    translated_tokens = model_translate.generate(
        **tokenizer_translate([text], return_tensors='pt', max_length=104, truncation=True))[0]
    translated_sentence = tokenizer_translate.decode(
        translated_tokens, skip_special_tokens=True)
    return translated_sentence

def summ_t5(text):
    input_ids = tokenizer_t5.encode(text, return_tensors='pt')
    summary_ids = model_t5.generate(input_ids,
                max_length=100,
                num_beams=2,
                repetition_penalty=2.5,
                length_penalty=1.0,
                early_stopping=True,
                no_repeat_ngram_size=2,
                use_cache=True)
    summary_text = tokenizer_t5.decode(summary_ids[0], skip_special_tokens=True)
    return summary_text
    
def sentiment_analysis(text):
    output = sentiment_pipeline(text)
    return {elm["label"]: elm["score"] for elm in output[0]}

def ner(text):
    output = ner_pipeline(text)
    for elm in output:
        elm['entity'] = elm['entity_group']
    return {"text": text, "entities": output}
    
def sentiment_df(text):
    df = pd.DataFrame(columns=['Text', 'Eng', 'Label', 'Score'])
    text_list = sentence_tokenizer.tokenize(text)
    eng_text = [get_translation(text) for text in text_list]
    result = [sentiment_analysis(text) for text in eng_text]
    labels = []
    scores = []
    for pred in result:
        idx = list(pred.values()).index(max(list(pred.values())))
        labels.append(list(pred.keys())[idx])
        scores.append(round(list(pred.values())[idx], 3))
    df['Text'] = text_list
    df['Eng'] = eng_text 
    df['Label'] = labels
    df['Score'] = scores
    return df
    
def run(text):
    summ_ = summ_t5(text)
    summ_translated = get_translation(summ_)
    sent_ = sentiment_analysis(summ_translated )
    ner_ = ner(summ_)
    df_sentiment = sentiment_df(text)
    return summ_, sent_, ner_, df_sentiment

if __name__ == "__main__":
    with gr.Blocks() as demo:
        gr.Markdown("""<h1 style="text-align:center">Financial Statement Analysis - Indonesia</h1>""")

        gr.Markdown(
            """
            Creator: Wira Indra Kusuma
            """
            )
        with gr.Row():
            with gr.Column():
                input_text = gr.Textbox(label="Input Text")
                analyze_button = gr.Button(label="Analyze")
                examples_bar = gr.Examples(examples=examples, inputs=input_text)
                
            with gr.Column():
                summ_output = gr.Textbox(label="Article Summary")
                ner_output = gr.HighlightedText(label="NER of Summary")
                sent_output = gr.Label(label="Sentiment of Summary")
                
        dataframe_component = gr.DataFrame(type="pandas",
                                            label="Dataframe",
                                            max_rows=(20,'fixed'),
                                            overflow_row_behaviour='paginate',
                                            wrap=True)
                
                
        analyze_button.click(run, inputs=input_text, outputs=[summ_output, sent_output, ner_output, dataframe_component ])
    demo.launch()