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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) | |