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from utils import (
SentenceSimilarity,
pos_tagging,
text_analysis,
text_interface,
sentence_similarity,
)
from script import details
from transformers import pipeline
import gradio as gr
from functools import partial
pipes = {
"Sentiment Analysis": pipeline(
"text-classification",
model="StevenLimcorn/indonesian-roberta-base-emotion-classifier",
tokenizer="StevenLimcorn/indonesian-roberta-base-emotion-classifier",
),
"Emotion Classifier": pipeline(
"text-classification",
model="w11wo/indonesian-roberta-base-sentiment-classifier",
tokenizer="w11wo/indonesian-roberta-base-sentiment-classifier",
),
"summarization": pipeline(
"summarization",
model="LazarusNLP/IndoNanoT5-base-IndoSum",
tokenizer="LazarusNLP/IndoNanoT5-base-IndoSum",
),
"sentence-similarity": SentenceSimilarity(model="LazarusNLP/all-indobert-base-v2"),
"POS Tagging": pipeline(model="w11wo/indonesian-roberta-base-posp-tagger"),
}
if __name__ == "__main__":
# list of collections of all demos
classifiers = ["Sentiment Analysis", "Emotion Classifier"]
# Summary
summary_interface = gr.Interface.from_pipeline(
pipes["summarization"],
title="Summarization",
examples=details["summarization"]["examples"],
description=details["summarization"]["description"],
allow_flagging="never",
)
# Pos Tagging
pos_interface = gr.Interface(
fn=partial(pos_tagging, pipe=pipes["POS Tagging"]),
inputs=[
gr.Textbox(placeholder="Masukan kalimat di sini...", label="Input Text"),
],
outputs=[gr.HighlightedText()],
title="POS Tagging",
examples=details["POS Tagging"]["examples"],
description=details["POS Tagging"]["description"],
allow_flagging="never",
)
# Text Analysis
with gr.Blocks() as text_analysis_interface:
gr.Markdown("# Text Analysis")
gr.Markdown(details["Text Analysis"]["description"])
input_text = gr.Textbox(lines=5, label="Input Text")
with gr.Row():
smsa = gr.Label(label="Sentiment Analysis")
emot = gr.Label(label="Emotion Classification")
pos = gr.HighlightedText(label="POS Tagging")
btn = gr.Button("Analyze")
btn.click(
fn=partial(text_analysis, pipes=pipes),
inputs=[input_text],
outputs=[smsa, emot, pos],
)
gr.Examples(
details["Text Analysis"]["examples"],
inputs=input_text,
outputs=[smsa, emot, pos],
)
with gr.Blocks() as sentence_similarity_interface:
gr.Markdown("# Document Search 🔍")
gr.Markdown(details["sentence-similarity"]["description"])
with gr.Row():
with gr.Column():
input_text = gr.Textbox(lines=5, label="Query")
file_input = gr.File(
label="Documents", file_types=[".txt"], file_count="multiple"
)
button = gr.Button("Search...")
output = gr.Label()
button.click(
fn=partial(sentence_similarity, pipe=pipes["sentence-similarity"]),
inputs=[input_text, file_input],
outputs=[output],
)
demo_interface = {
"demo": [
text_interface(
pipes[name],
details[name]["examples"],
name,
name,
details[name]["description"],
)
for name in classifiers
]
+ [
sentence_similarity_interface,
summary_interface,
pos_interface,
text_analysis_interface,
],
"titles": classifiers
+ ["Document Search", "Summarization", "POS Tagging", "Text Analysis"],
}
# with gr.Blocks() as demo:
# with gr.Column():
# gr.Markdown("# Title")
# gr.TabbedInterface(
# demo_interface["demo"], demo_interface["titles"], theme="soft"
# )
demo = gr.TabbedInterface(
demo_interface["demo"], demo_interface["titles"], theme="soft"
)
demo.launch(debug=True)
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