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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
import torch
from langs import LANGS
import os
pipe1 = pipeline('text-generation', model='RamAnanth1/distilgpt2-sd-prompts')
TASK = "translation"
CKPT = "facebook/nllb-200-distilled-600M"
model = AutoModelForSeq2SeqLM.from_pretrained(CKPT)
tokenizer = AutoTokenizer.from_pretrained(CKPT)
device = 0 if torch.cuda.is_available() else -1
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
import torch
from langs import LANGS
TASK = "translation"
CKPT = "facebook/nllb-200-distilled-600M"
model = AutoModelForSeq2SeqLM.from_pretrained(CKPT)
tokenizer = AutoTokenizer.from_pretrained(CKPT)
device = 0 if torch.cuda.is_available() else -1
def translate(text):
"""
Translate the text from source lang to target lang
"""
src_lang = "zho-Hans"
tgt_lang = "eng_Latn"
max_length = 400
translation_pipeline = pipeline(TASK,
model=model,
tokenizer=tokenizer,
src_lang=src_lang,
tgt_lang=tgt_lang,
max_length=max_length,
device=device)
result = translation_pipeline(text)
return result[0]['translation_text']
with gr.Blocks() as demo:
gr.Markdown(
""" # <center>🥳💬💕 - Stable Diffusion，随时随地，想画就画！</center>
### <center>🤖 - 让有人文关怀的AI造福每一个人！AI向善，文明璀璨！山水AI - Enable the future！</center>
"""
)
with gr.Row().style(equal_height=True):
inp1 = gr.Textbox(label = "您要翻译的中文内容", lines=1)
button = gr.Button("翻译一下")
out1 = gr.Textbox(label = "翻译后的英文内容", lines=1)
button.click(translate, [inp1], [out1])
gr.Markdown(
""" ### <center>注意❗：请不要输入或生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及娱乐使用。用户输入或生成的内容与程序开发者无关，请自觉合法合规使用，违反者一切后果自负。</center>
### <center>Model by [ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b). Thanks to [THUDM](https://github.com/THUDM) and [CjangCjengh](https://github.com/CjangCjengh). Please follow me on [Bilibili](https://space.bilibili.com/501495851?spm_id_from=333.1007.0.0).</center>
"""
)
gr.HTML('''
<div class="footer">
<p>🎶🖼️🎡 - It’s the intersection of technology and liberal arts that makes our hearts sing. - Steve Jobs
</p>
</div>
''')
demo.queue().launch(show_error=True)
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