import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "t5-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def generate_text(input_text):
    input_ids = tokenizer("summarize: " + input_text, return_tensors="pt", max_length=512, truncation=True).input_ids
    outputs = model.generate(input_ids, max_length=300, num_return_sequences=1, no_repeat_ngram_size=2)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

iface = gr.Interface(
    fn=generate_text,
    inputs=gr.Textbox(lines=5, label="Input Text"),
    outputs=gr.Textbox(label="Generated Text"),
    title="Text Generator",
    description="Enter text to generate a summary or continuation."
)

iface.launch()