streamlit app

app.py

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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
+@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.")
+
+    # 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)