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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-Gen Demo(代码生成)", page_icon="❄️"
)
c1, c2 = st.columns([0.32, 2])
# The snowflake logo will be displayed in the first column, on the left.
with c1:
st.image(
"./panda23.png",
width=100,
)
# The heading will be on the right.
with c2:
st.caption("")
st.title("MaMaL-Gen(代码生成)")
############ SIDEBAR CONTENT ############
st.sidebar.image("./panda23.png",width=270)
st.sidebar.markdown("---")
st.sidebar.write(
"""
## 使用方法：
在【输入】文本框输入自然语言，点击【生成】按钮,即会生成想要的代码。
"""
)
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-Gen](https://huggingface.co/hanbin/MaMaL-Gen)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("---")
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.write(
"> **Tip：** 只支持英文输入，输入过长，效果会变差。"
)
st.sidebar.write(
"> **Good case：**"
)
code_good = """1)Convert a SVG string to a QImage
2)Try to seek to given offset"""
st.sidebar.code(code_good, language='python')
st.sidebar.write(
"> **Bad cases：**"
)
code_bad = """Read an OpenAPI binary file ."""
st.sidebar.code(code_bad, language='python')
# Let's add some info about the app to the sidebar.
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-Gen](https://huggingface.co/hanbin/MaMaL-Gen) 模型.
"""
)
# model, tokenizer = load_model("hanbin/MaMaL-Gen")
st.write("### 输入：")
input = st.text_area("", height=100)
button = st.button('生成')
tokenizer,model = get_model("hanbin/MaMaL-Gen")
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('#### 输出位置~~')
if __name__ == '__main__':
main()
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