app.py

import streamlit as st
from transformers import pipeline

import streamlit as st
from st_chat_message import message

message("Hello world!", is_user=True)
message("Hi")

st.markdown("# Auto-generating Question-Answering Datasets with Domain-Specific Knowledge for Language Models in Scientific Tasks", unsafe_allow_html=True)

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# Transformation Algorithm
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st.markdown('## QA Dataset Auto Generation', unsafe_allow_html=True)





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# Property Extraction
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st.markdown('## Property Extraction', unsafe_allow_html=True)

st.markdown('### Select a model: ', unsafe_allow_html=True)

# 定义模型配置选项
size_lst = ["-base", "-large"]
cased_lst = ["-cased", "-uncased"]
fpretrain_lst = ["None", "-scsmall", "-scmedium", "-sclarge"]
finetune_lst = ["-squad", "-scqa1", "-scqa2"]

# 为每个选项创建下拉菜单
size = st.selectbox("Choose a model size:", size_lst)
cased = st.selectbox("Whether distinguish upper and lowercase letters:", cased_lst)
fpretrain = st.selectbox("Further pretrained on a solar cell corpus:", fpretrain_lst)
finetune = st.selectbox("Finetuned on a QA dataset:", finetune_lst)

# 根据选择构建模型名称
if fpretrain == "None":
    model = "".join(["ZongqianLi/bert", size, cased, finetune])
else:
    model = "".join(["ZongqianLi/bert", size, cased, fpretrain, finetune])

# 显示用户选择的模型
st.write(f"Your selected model: {model}")

# 加载问答模型
pipe = pipeline("question-answering", model=model)

st.markdown('### Input a paper: ', unsafe_allow_html=True)

# 设置默认的问题和上下文
default_property = "FF"
default_context = "The referential DSSC with Pt CE was also measured under the same conditions, which yields η of 6.66% (Voc= 0.78 V, Jsc= 13.0 mA cm−2, FF = 65.9%)."

# 获取用户输入的问题和上下文
property = st.text_input("Enter your the name of the property: ", value=default_property)
context = st.text_area("Enter the context: ", value=default_context, height=100)

# 添加一个按钮，用户点击后执行问答
if st.button('Extract the property'):
    question_1 = f"What is the value of {property}?"
    if context and question_1:
        out = pipe({
            'question': question_1,
            'context': context
        })
        value = out["answer"]
        st.write(f"First-turn question: {question_1}")
        st.write(f"First-turn answer: {value}")
        question_2 = f"What material has {property} of {value}?"
        out = pipe({
            'question': question_2,
            'context': context
        })
        material = out["answer"]
        st.write(f"Second-turn question: {question_2}")
        st.write(f"First-turn answer: {material}")
    else:
        st.write("Please enter both a question and context.")