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### 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 | |
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 | |
def load_data(): | |
# load dev bench | |
devBench = rg.DevBench.load("./assets/data/rg/sst_db.devbench") | |
return devBench | |
# load model | |
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 | |
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 | |
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) | |