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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.") | |
| # 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) | |