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from transformers import T5Tokenizer, T5Model, T5ForConditionalGeneration, pipeline
import nltk.data
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 = "w11wo/indonesian-roberta-base-sentiment-classifier"
pretrained_ner = "cahya/bert-base-indonesian-NER"
sentence_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
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
)
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):
text_list = tokenizer.tokenize(text)
result = [sentiment_analysis(text) for text in text_list]
sentence = []
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['Label'] = labels
df['Score'] = scores
return df
def run(text):
summ_ = summ_t5(text)
sent_ = sentiment_analysis(summ_)
ner_ = ner(summ_)
df_ = sentiment_df(text)
ner_all = ner(text)
fig = plt.figure()
df.groupby(["Label"])["Text"].count().plot.pie(autopct="%.1f%%", figsize=(6,6))
return summ_, sent_, ner_, fig, ner_all
if __name__ == "__main__":
with gr.Blocks() as demo:
gr.Markdown("""<h1 style="text-align:center">News Analyzer - Indonesia</h1>""")
gr.Markdown(
"""
Creator: wiraindrak
"""
)
with gr.Row():
with gr.Column():
input_text = gr.Textbox(label="Input Text")
analyze_button = gr.Button(label="Analyze")
summ_output = gr.Textbox(label="Article Summary")
ner_output = gr.HighlightedText(label="NER Summary")
sent_output = gr.Textbox(label="Sentiment Summary")
with gr.Column():
plot_component = gr.Plot(label="Pie Chart of Sentiments")
ner_all_output = gr.HighlightedText(label="NER Article")
analyze_button.click(run, inputs=input_text, outputs=[summ_output, sent_output, ner_output, plot_component, ner_all_output])
demo.launch() |