MaMaL-Com / app.py
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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
@st.cache_resource
def load_model(model_name):
# load model
model = T5ForConditionalGeneration.from_pretrained("hanbin/MaMaL-Com")
# load tokenizer
tokenizer = RobertaTokenizer.from_pretrained("hanbin/MaMaL-Com")
return model,tokenizer
def main(model,tokenizer):
# `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(
"images/panda.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("images/panda.png",width=270)
st.sidebar.write("")
# 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.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.
"""
)
if __name__ == '__main__':
model, tokenizer = load_model("hanbin/MaMaL-Gen")
main(model, tokenizer)