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import streamlit as st | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
MODEL_NAME = "reshinthadith" | |
def load_model_and_tokenizer(model_name): | |
"""Adding load_model_and_tokenizer function to keep the model in the memory""" | |
model = AutoModelForCausalLM.from_pretrained(model_name) | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
return tokenizer,model | |
tokenizer,model = load_model_and_tokenizer(MODEL_NAME) | |
st.set_page_config( | |
page_title= "Code Representation Learning", | |
initial_sidebar_state= "expanded" | |
) | |
st.sidebar.title("Code Representation Learning") | |
workflow = st.sidebar.selectbox('select a task', ['Bash Synthesis']) | |
if workflow == "Bash Synthesis": | |
st.title("Program Synthesis for Bash") | |
arxiv_id = st.text_input("Natural Language prompt ","list all the files in the directory 'data\' ") | |
output_diction = {} | |
button = st.button("synthesize") | |
if button: | |
link_gen = r"https://arxiv.org/abs/" | |
webbrowser.open_new_tab(link_gen+str(arxiv_id)) | |
# Abstract | |
with st.beta_expander("Abstract"): | |
st.write(output_diction["abstract"]) | |
with st.beta_expander("Influencing Citations"): | |
st.write(output_diction["influenital_citations"]) | |
with st.beta_expander("Citation Graph"): | |
print("") | |
elif workflow == "Rebuttal Analysis": | |
st.title("Rebuttal Analysis") |