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Merge branch 'main' of https://github.com/geemi725/XpertAI
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@geemi725
geemi725 committed Jun 5, 2024
2 parents 81df28b + 1722e7b commit 8b9305f
Showing 1 changed file with 57 additions and 59 deletions.
116 changes: 57 additions & 59 deletions app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,13 +6,12 @@
import os
import streamlit as st
import sys

__import__("pysqlite3")
sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")
load_dotenv()
ss = st.session_state

# Set width of sidebar

st.markdown(
"""
<style>
Expand All @@ -24,15 +23,11 @@
unsafe_allow_html=True,
)


def on_api_key_change():
api_key = ss.get("api_key") or os.getenv("OPENAI_API_KEY")
# api_key = os.getenv('OPENAI_API_KEY')
os.environ["OPENAI_API_KEY"] = api_key
openai.api_key = api_key



def save_uploadfile(uploadedfile):
dirpath = os.path.join("data", "lit_dir")
if os.path.exists(dirpath):
Expand All @@ -41,10 +36,6 @@ def save_uploadfile(uploadedfile):
with open(os.path.join(dirpath, uploadedfile.name), "wb") as f:
f.write(uploadedfile.getbuffer())


# Header section
# logo = Image.open('assets/logo.png')
# st.image(logo)
st.write(
"## Xpert AI: Extract human interpretable structure-property relationships from raw data"
)
Expand All @@ -53,20 +44,16 @@ def save_uploadfile(uploadedfile):
Currently, GPT-4 model is used to generate natural language explanations."""
)



def run_autofill():
st.session_state.auto_target = "toxicity of small molecules"
st.session_state.auto_df = "tests/toxicity_sample_data.csv"
st.experimental_rerun()


auto_target = st.session_state.get("auto_target", None)
auto_arxiv = st.session_state.get("auto_arxiv", None)


with st.sidebar:
logo = Image.open("assets/logo.png")
logo = Image.open("assets/logo_2.png")
st.image(logo)
# st.markdown('# Setup your inputs!')
# Input OpenAI api key
Expand All @@ -90,12 +77,17 @@ def run_autofill():
st.markdown("### Set up XAI workflow")
mode_type = st.radio(
"1. Select the model type",
["Classifier", "Regressor",],
captions=["For predicting discreet labels","For predicting continuous values"],
[
"Classifier",
"Regressor",
],
captions=["For predicting discreet labels", "For predicting continuous values"],
)

XAI_tool = st.radio("2. What's your favorite XAI method?", ["SHAP", "LIME", "Both"])
top_k = st.slider("3. Select the max number of features to be explained!", 0, 10,value=2)
top_k = st.slider(
"3. Select the max number of features to be explained!", 0, 10, value=2
)

st.markdown(
"### Provide literature to generate scientific explanations! \nIf you don't provide literature, you will receive an explanation based on XAI tools."
Expand All @@ -106,35 +98,31 @@ def run_autofill():
arxiv_keywords = st.text_input(
"If you want to scrape arxiv, provide keywords for arxiv scraping:",
help="organic molecules, solubility of small molecules",

value = auto_arxiv
value=auto_arxiv,
)
max_papers = st.number_input(
"Maximum number of papers to download from arxiv.org", value=15
)

button = st.button("Generate Explanation", type="primary")

st.markdown("## Not sure what to do? Run a test case - explaining toxicity of small molecules!")
st.markdown("""**Make sure to add your OpenAPI key**.
st.markdown(
"## Not sure what to do? Run a test case - explaining toxicity of small molecules!"
)
st.markdown(
"""**Make sure to add your OpenAPI key**.
You can download the input dataset after the explanation is generated.
Literature parsing is not used here.""")

auto_button = st.button("Test Run", on_click=run_autofill)
Literature parsing is not used here."""
)

auto_button = st.button("Test Run", on_click=run_autofill)

# Main page
if api_key:
from xpertai.tools.explain_model import get_modelsummary
from xpertai.tools.scrape_arxiv import scrape_arxiv
from xpertai.tools.generate_nle import gen_nle
from xpertai.tools.utils import vector_db


##set up the inputs
if auto_button:
input_file = "./tests/toxicity_sample_data.csv"
df_init = pd.read_csv(input_file, header=0)

arg_dict_xai = {
"df_init": df_init,
"model_type": "Classifier",
Expand All @@ -150,31 +138,50 @@ def run_autofill():
"top_k": top_k,
"XAI_tool": XAI_tool,
}
else:
arg_dict_xai = None

if button or auto_button:
# validate api key
if api_key.startswith("sk-"):
from xpertai.tools.explain_model import get_modelsummary
from xpertai.tools.scrape_arxiv import scrape_arxiv
from xpertai.tools.generate_nle import gen_nle
from xpertai.tools.utils import vector_db

else:
st.warning("Please enter a valid OpenAI API key!")
st.stop()

if arg_dict_xai is None:
st.warning("Please upload a dataset!")
st.stop()

explanation = get_modelsummary(arg_dict_xai)

st.markdown("### XAI Analysis:")
xg_plot = Image.open(f"./data/figs/xgbmodel_error.png")
st.image(xg_plot)

if XAI_tool in ["SHAP","LIME"]:
if XAI_tool in ["SHAP", "LIME"]:
st.image(Image.open(f"./data/figs/{XAI_tool.lower()}_bar.png"))
else:
st.image(Image.open(f"./data/figs/shap_bar.png"))
st.image(Image.open(f"./data/figs/lime_bar.png"))

if auto_button:
shutil.copytree("./paper/datasets", "./data/figs", dirs_exist_ok=True)
shutil.copytree("./paper/datasets", "./data/figs", dirs_exist_ok=True)

with st.spinner("Please wait...:computer: :speech_balloon:"):
# read literature
if lit_files:
for file in lit_files:
save_uploadfile(file)
try:
vector_db(lit_file=os.path.join("./data/lit_dir", file.name),
try_meta_data=True)
vector_db(
lit_file=os.path.join("./data/lit_dir", file.name),
try_meta_data=True,
)
except BaseException:
st.write("vectordb failed!!")

Expand All @@ -185,32 +192,22 @@ def run_autofill():

scrape_arxiv(arg_dict_arxiv)

if not arxiv_keywords and not lit_files:

elif not arxiv_keywords and not lit_files:
st.markdown(
f"""### Literature is not provided to make an informed explanation. Based on XAI analysis, the following explanation can be given:
\n{explanation}"""
)

nle = explanation

#f = open(outname, "w+")
#f.write(f"Understanding {observation}\n:")
#f.write(explanation)
#f.write("Explanation generated with XpertAI. https://github.com/geemi725/XpertAI")
#f.close()
#with open(outname, "rb") as f:
# st.download_button("Download the explanation!",
# f,
# file_name="explanation.txt")

else:
# Generate evidence-based explanation
nle = gen_nle({
"observation": observation,
"top_k": top_k,
"XAI_tool": XAI_tool,
})
nle = gen_nle(
{
"observation": observation,
"top_k": top_k,
"XAI_tool": XAI_tool,
}
)

st.write(
"### The structure function relationship based on XAI analysis and literature, the following explanation can be given:\n",
Expand All @@ -220,12 +217,13 @@ def run_autofill():
f = open("./data/figs/structure_function_relationship.txt", "w+")
f.write(f"Understanding {observation}\n:")
f.write(nle)
f.write("\n\nExplanation generated with XpertAI. https://github.com/geemi725/XpertAI")
f.write(
"\n\nExplanation generated with XpertAI. https://github.com/geemi725/XpertAI"
)
f.close()

shutil.make_archive("./data/figs", "zip", "./data/figs/")
with open("./data/figs.zip", "rb") as f:
st.download_button(
"Download the outputs!", f, file_name="XpertAI_output.zip"
)

)

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