app.py

import streamlit as st
from transformers import pipeline

option = st.selectbox(
    'Choisissez votre langue / 言語を選択',
    ('Français', '日本語'))

if option == "Français":
    pipeline = pipeline(task="text-generation", model="Aruno/Bloom-FR-160m")
    title_str = "Génération de texte en français"
    min_length_str = "Longueur minimum"
    max_length_str = "Longueur maximum"
    num_beams_str = "Nombre de beam"
    num_output_str = "Nombre de réponse"
    submitted_str = "Génération"
    input_value_str1 = 'Entrée'
    input_value_str2 = 'Je vais dans'
else:
    pipeline = pipeline(task="text-generation", model="Aruno/Bloom-JP-160m")
    title_str = "日本語のテキスト生成"
    min_length_str = "生成最小数"
    max_length_str = "生成最大数"
    num_beams_str = "Beam数"
    num_output_str = "応答数"
    submitted_str = "生成"
    input_value_str1 = '入力'
    input_value_str2 = '宇宙に行って、'

st.title(title_str)

with st.form("my_form"):
   input_value = st.text_input(input_value_str1, input_value_str2)
   min_length = st.slider(min_length_str, min_value=1, value=16)
   max_length = st.slider(max_length_str, min_value=1, value=32)
   num_beams = st.slider(num_beams_str, min_value=1, max_value=10, value=3)
   num_output = st.slider(num_output_str, min_value=1, max_value=10, value=3)

   submitted = st.form_submit_button(submitted_str)
   if submitted:
       predictions = pipeline(input_value, min_length=min_length, max_length=max_length, num_beams=num_beams, num_return_sequences=num_output)
       st.write(predictions)