Nazneen Rajani
Sidebar collapse css class
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history blame
6.09 kB
### LIBRARIES ###
# # Data
import numpy as np
import pandas as pd
import json
from math import floor
# Robustness Gym and Analysis
import robustnessgym as rg
from gensim.models.doc2vec import Doc2Vec
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
import nltk
nltk.download('punkt') #make sure that punkt is downloaded
# App & Visualization
import streamlit as st
import altair as alt
# utils
from interactive_model_cards import utils as ut
from interactive_model_cards import app_layout as al
from random import sample
from PIL import Image
### LOADING DATA ###
# model card data
@st.experimental_memo
def load_model_card():
with open("./assets/data/text_explainer/model_card.json") as f:
mc_text = json.load(f)
return mc_text
# pre-computed robusntess gym dev bench
# @st.experimental_singleton
@st.cache(allow_output_mutation=True)
def load_data():
# load dev bench
devBench = rg.DevBench.load("./assets/data/rg/sst_db.devbench")
return devBench
# load model
@st.experimental_singleton
def load_model():
model = rg.HuggingfaceModel(
"distilbert-base-uncased-finetuned-sst-2-english", is_classifier=True
)
return model
#load pre-computed embedding
def load_embedding():
embedding = pd.read_pickle("./assets/models/sst_vectors.pkl")
return embedding
#load doc2vec model
@st.experimental_singleton
def load_doc2vec():
doc2vec = Doc2Vec.load("./assets/models/sst_train.doc2vec")
return(doc2vec)
# @st.experimental_memo
def load_examples():
with open("./assets/data/user_data/example_sentence.json") as f:
examples = json.load(f)
return examples
# loading the dataset
def load_basic():
# load data
devBench = load_data()
# load model
model = load_model()
#protected_classes
protected_classes = json.load(open("./assets/data/protected_terms.json"))
return devBench, model, protected_classes
@st.experimental_singleton
def load_title():
img = Image.open("./assets/img/title.png")
return(img)
if __name__ == "__main__":
### STREAMLIT APP CONGFIG ###
st.set_page_config(layout="wide", page_title="Interactive Model Card")
# import custom styling
ut.init_style()
### LOAD DATA AND SESSION VARIABLES ###
# ******* loading the mode and the data
with st.spinner():
sst_db, model,protected_classes = load_basic()
embedding = load_embedding()
doc2vec = load_doc2vec()
# load example sentences
sentence_examples = load_examples()
# ******* session state variables
if "user_data" not in st.session_state:
st.session_state["user_data"] = pd.DataFrame()
if "example_sent" not in st.session_state:
st.session_state["example_sent"] = "I like you. I love you"
if "quant_ex" not in st.session_state:
st.session_state["quant_ex"] = {"Overall Performance": sst_db.metrics["model"]}
if "selected_slice" not in st.session_state:
st.session_state["selected_slice"] = None
if "slice_terms" not in st.session_state:
st.session_state["slice_terms"] = {}
if "embedding" not in st.session_state:
st.session_state["embedding"] = embedding
if "protected_class" not in st.session_state:
st.session_state["protected_class"] = protected_classes
### STREAMLIT APP LAYOUT###
# ******* MODEL CARD PANEL *******
#st.sidebar.title("Interactive Model Card")
img = load_title()
st.sidebar.image(img,width=400)
st.sidebar.warning("Data is not permanently collected or stored from your interactions, but is temporarily cached during usage.")
st.markdown('''
<a href="javascript:document.getElementsByClassName('css-1rs6os edgvbvh3')[1].click();">
<img src="./assets/img/info.png" style="width:30px;height:30px;"/>
</a>
''', unsafe_allow_html=True)
# load model card data
errors = st.sidebar.checkbox("Show Warnings", value=True)
model_card = load_model_card()
al.model_card_panel(model_card,errors)
lcol, rcol = st.columns([4, 8])
# ******* USER EXAMPLE DATA PANEL *******
st.markdown("---")
with lcol:
# Choose waht to show for the qunatiative analysis.
st.write("""<h1 style="font-size:20px;padding-top:0px;"> Quantitative Analysis</h1>""",
unsafe_allow_html=True)
st.markdown("View the model's performance or visually explore the model's training and testing dataset")
data_view = st.selectbox("Show:",
["Model Performance Metrics","Data Subpopulation Comparison Visualization"])
st.markdown("Any groups you define via the *analysis actions* will be automatically added to the view")
st.markdown("---")
# Additional Analysis Actions
st.write(
"""<h1 style="font-size:18px;padding-top:5px;"> Analysis Actions</h1>""",
unsafe_allow_html=True,
)
al.example_panel(sentence_examples, model, sst_db,doc2vec)
# ****** GUIDANCE PANEL *****
with st.expander("Guidance"):
st.markdown(
"Need help understanding what you're seeing in this model card?"
)
st.markdown(
" * **[Understanding Metrics](https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks)**: A cheatsheet of model metrics"
)
st.markdown(
" * **[Understanding Sentiment Models](https://www.semanticscholar.org/topic/Sentiment-analysis/6011)**: An overview of sentiment analysis"
)
st.markdown(
"* **[Next Steps](https://docs.google.com/document/d/1r9J1NQ7eTibpXkCpcucDEPhASGbOQAMhRTBvosGu4Pk/edit?usp=sharin)**: Suggestions for follow-on actions"
)
st.markdown("Feel free to submit feedback via our [online form](https://sfdc.co/imc_feedback)")
# ******* QUANTITATIVE DATA PANEL *******
al.quant_panel(sst_db, st.session_state["embedding"], rcol,data_view)