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# This file is .....
# Author: Hanbin Wang
# Date: 2023/4/18
import transformers
import streamlit as st
from PIL import Image
from transformers import RobertaTokenizer, T5ForConditionalGeneration
from transformers import pipeline
@st.cache_resource
def get_model(model_path):
tokenizer = RobertaTokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
model.eval()
return tokenizer, model
def main():
# `st.set_page_config` is used to display the default layout width, the title of the app, and the emoticon in the browser tab.
st.set_page_config(
layout="centered", page_title="MaMaL-Sum Demo(代码摘要)", page_icon="❄️"
)
c1, c2 ,c3 = st.columns([0.32, 2,0.5])
# The snowflake logo will be displayed in the first column, on the left.
with c1:
st.image(
"./panda27.png",
width=100,
)
# The heading will be on the right.
with c2:
st.caption("")
st.title("MaMaL-Sum(代码摘要)")
############ SIDEBAR CONTENT ############
st.sidebar.image("./panda27.png",width=270)
st.sidebar.markdown("---")
st.sidebar.write(
"""
## 使用方法：
在【输入】文本框输入想要解释的代码，点击【摘要】按钮,即会显示代码的自然语言描述。
"""
)
st.sidebar.write(
"""
## 注意事项：
1）APP托管在外网上，请确保您可以全局科学上网。
2）您可以下载[MaMaL-Sum](https://huggingface.co/hanbin/MaMaL-Sum)模型，本地测试。（无需科学上网）
"""
)
# For elements to be displayed in the sidebar, we need to add the sidebar element in the widget.
# We create a text input field for users to enter their API key.
# API_KEY = st.sidebar.text_input(
# "Enter your HuggingFace API key",
# help="Once you created you HuggingFace account, you can get your free API token in your settings page: https://huggingface.co/settings/tokens",
# type="password",
# )
#
# # Adding the HuggingFace API inference URL.
# API_URL = "https://api-inference.huggingface.co/models/valhalla/distilbart-mnli-12-3"
#
# # Now, let's create a Python dictionary to store the API headers.
# headers = {"Authorization": f"Bearer {API_KEY}"}
st.sidebar.markdown("---")
# Let's add some info about the app to the sidebar.
st.write(
"> **Tip：** 首次运行需要加载模型，可能需要一定的时间！"
)
st.write(
"> **Tip：** 左侧栏给出了一些good case 和 bad case，you can try it！"
)
st.sidebar.write(
"> **Good case：**"
)
code_good = """def svg_to_image(string, size=None):
if isinstance(string, unicode):
string = string.encode('utf-8')
renderer = QtSvg.QSvgRenderer(QtCore.QByteArray(string))
if not renderer.isValid():
raise ValueError('Invalid SVG data.')
if size is None:
size = renderer.defaultSize()
image = QtGui.QImage(size, QtGui.QImage.Format_ARGB32)
painter = QtGui.QPainter(image)
renderer.render(painter)
return image"""
st.sidebar.code(code_good, language='python')
st.sidebar.write(
"> **Bad cases：**"
)
code_bad = """from transformers import RobertaTokenizer, T5ForConditionalGeneration
from transformers import pipeline"""
st.sidebar.code(code_bad, language='python')
st.sidebar.write(
"""
App 由 东北大学NLP课小组成员创建， 使用 [Streamlit](https://streamlit.io/)🎈 和 [HuggingFace](https://huggingface.co/inference-api)'s [MaMaL-Sum](https://huggingface.co/hanbin/MaMaL-Sum) 模型.
"""
)
# model, tokenizer = load_model("hanbin/MaMaL-Gen")
st.write("### 输入：")
input = st.text_area("", height=200)
button = st.button('摘要')
tokenizer,model = get_model("hanbin/MaMaL-Sum")
input_ids = tokenizer(input, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=100)
output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
# generator = pipeline('text-generation', model="E:\DenseRetrievalGroup\CodeT5-base")
# output = generator(input)
# code = '''def hello():
# print("Hello, Streamlit!")'''
if button:
st.write("### 输出：")
st.code(output, language='python')
else:
st.write('')
if __name__ == '__main__':
main()
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