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()