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import streamlit as st
from transformers import (
pipeline
)
# albert
# albert_base_chinese_cluecorpussmall=pipeline(task="fill-mask", model="uer/albert-base-chinese-cluecorpussmall")
# roberta
# xlm_roberta_base=pipeline(task="fill-mask", model="xlm-roberta-base")
# xlm_roberta_large=pipeline(task="fill-mask", model="xlm-roberta-large")
# bert
pipe=pipeline(
# model="rjx/chinese-new-text-classification-10200-albert-base-chinese-cluecorpussmall",
model="rjx/rjxai-xlm-roberta-longformer-1024-and-dataset-en-0523",
use_auth_token=st.secrets["read_key"]
)
if 'type' not in st.session_state:
st.session_state.type=""
if 'article' not in st.session_state:
st.session_state.article=""
if 'result' not in st.session_state:
st.session_state.result=""
# def charlength():
# if len(st.session_state.article)>=512:
# st.warning("article length is 512")
def form_article_click():
# st.warning(st.session_state.model_key)
# st.warning(st.session_state.article_key)
if st.session_state.article_key == "":
st.warning("Need to enter content")
else:
article=st.session_state.article_key
# 截取大于510字符的内容
if len(article)>=1024:
article=article[0: 1024]
# st.info(article)
print(article)
result = pipe(article)
print(result)
# st.session_state.type=result[0]["label"]
if result:
if result[0]["label"]=="人类写的":
st.session_state.type="Human writing"
elif result[0]["label"]=="ai写的":
st.session_state.type="AI writing"
st.session_state.result=result
st.title("AI write or Human write v2")
col1, col2 = st.columns(2)
with col1:
st.header("input")
with st.form(key="article_form"):
# 模型选择
# option_input = st.selectbox(
# 'select rjx model:（Other model making）',
# ('chinese-new-text-classification-10200-albert-base-chinese-cluecorpussmall', 'other'),
# disabled=True,
# key='model_key',
# # on_change=charlength
# )
# 文本输入
article_input=st.text_area(
'content',
height=270,
key='article_key'
)
# 提交按钮
submit_button=st.form_submit_button(label='submit', on_click=form_article_click)
with col2:
st.header("output")
st.text_input('classification', st.session_state.type, disabled=True)
# st.text_area(
# 'result',
# st.session_state.result,
# height=270,
# disabled=True
# )
# st.write('selected classification model:', option)
# file_name = st.file_uploader("Upload a hot dog candidate image")
# if file_name is not None:
# col1, col2 = st.columns(2)
# image = Image.open(file_name)
# col1.image(image, use_column_width=True)
# predictions = pipeline(image)
# col2.header("Probabilities")
# for p in predictions:
# col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
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