app.py

import gradio as gr
from transformers import T5ForConditionalGeneration, T5Tokenizer

# Load model & tokenizer once
model_name = "utrobinmv/t5_summary_en_ru_zh_base_2048"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)

def summarize_email(email_text, mode):
    prefix_map = {
        "Short Summary": "summary to en: ",
        "Detailed Summary": "summary big to en: ",
        "Brief Summary": "summary brief to en: "
    }
    prefix = prefix_map.get(mode, "summary to en: ")
    input_text = prefix + email_text

    inputs = tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=True)
    outputs = model.generate(**inputs, max_new_tokens=200)

    return tokenizer.decode(outputs[0], skip_special_tokens=True)

demo = gr.Interface(
    fn=summarize_email,
    inputs=[
        gr.Textbox(lines=12, placeholder="Paste your email text here...", label="Email Text"),
        gr.Radio(["Short Summary", "Detailed Summary", "Brief Summary"], label="Mode", value="Short Summary")
    ],
    outputs="text",
    title="Email Summarizer (T5)",
    description="Paste your email, select summary mode, and get a concise version."
)

if __name__ == "__main__":
    demo.launch()