# -*- coding: utf-8 -*-
from transformers import pipeline
import gradio as gr
from gradio.components import Textbox
import random
import json

examples = []
with open('sample_data.jsonl', 'r', encoding='utf-8') as file:
        for line in file:
                data_dict = json.loads(line)
                examples.append(data_dict["text"])

def random_sample():
        random_number = random.randint(0, len(examples) - 1)
        return examples[random_number]

summarization = pipeline("summarization", model=f"xjlulu/ntu_adl_summarization_mt5_s", framework="pt")
def generate_answer(context):
        result = summarization(context)
        return result[0]['summary_text']

description="""
# Text Summarization
Enter a text paragraph to generate a title.
"""

with gr.Blocks(theme=gr.themes.Soft(), title="Text Summarization") as demo:
        gr.Markdown(description)

        S_output = Textbox(lines=3, label="title")
        with gr.Row():
                random_button = gr.Button("Random")
                generate_button = gr.Button("Generate")
        C_input = gr.Textbox(lines=6, label="Context paragraph", placeholder="Please enter text")

        random_button.click(random_sample, inputs=None, outputs=C_input)
        generate_button.click(generate_answer, inputs=C_input, outputs=S_output)

demo.launch()