from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("kriton/greek-text-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("kriton/greek-text-summarization")

from transformers import pipeline

summarizer = pipeline("summarization", model="kriton/greek-text-summarization")

def genarate_summary(article):
    inputs = tokenizer(
        'summarize: ' + article, 
        return_tensors="pt", 
        max_length=1024, 
        truncation=True,
        padding="max_length",
    )

    outputs = model.generate(
        inputs["input_ids"], 
        max_length=512, 
        min_length=130, 
        length_penalty=3.0, 
        num_beams=8, 
        early_stopping=True,
        repetition_penalty=3.0,
    )
    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
    summary = summary[:summary.rfind('.')]
    return summary
    
    
import gradio as gr

def calculate_summary(article):
    return genarate_summary(article)

iface = gr.Interface(
    fn=calculate_summary,
    inputs=gr.Textbox(lines=20, placeholder="Article Here", label='Article'),
    outputs=gr.Textbox(lines=5, placeholder="Summary", label='Summary'),
    btn = gr.Button("Generate")
)
iface.launch()