import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "csebuetnlp/mT5_multilingual_XLSum"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)


def summarize_text(text, src_lang):
    inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
    summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

examples = [
    ["الذكاء الاصطناعي هو فرع من علوم الكمبيوتر يهدف إلى إنشاء آلات ذكية تعمل وتتفاعل مثل البشر. بعض الأنشطة التي صممت أجهزة الكمبيوتر الذكية للقيام بها تشمل: التعرف على الصوت، التعلم، التخطيط، وحل المشاكل.", "ar"],
    ["Artificial intelligence is a branch of computer science that aims to create intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition, learning, planning, and problem-solving.", "en"]
]

iface = gr.Interface(
    fn=summarize_text,
    inputs=[gr.Textbox(lines=10, placeholder="Enter the text here....."), gr.Dropdown(["ar", "en"], label="Language")],
    outputs="text",
    examples=examples,
    title=" AI Study Summary ",
    description="Enter a text in Arabic or English and the model will summarize it, you can also choose one of the available examples."
)

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