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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the text summarization model
model_name = "Aiyan99/Theus_eleuther_1.3B_concepttagging"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Define the Gradio interface
def summarize_text(input_text):
# Tokenize and generate summary
input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=1024, truncation=True)
summary_ids = model.generate(input_ids, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return summary
iface = gr.Interface(
fn=summarize_text,
inputs="text",
outputs="text",
title="Concept Tagger",
description="Concept tag your text using Aiyan99's Theus model (1.3B Concept Tagging).",
)
if __name__ == "__main__":
iface.launch()
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