Spaces:
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add skeleton
Browse files- app.py +17 -0
- corpus.py +5 -0
- requirements.txt +0 -0
- single.py +45 -0
- static/logo.png +0 -0
app.py
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import streamlit as st
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import single
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import corpus
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from PIL import Image
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PAGES = {
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"Single Explanation": single,
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"Corpus Explanation": corpus,
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}
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st.sidebar.title("Explore ferret!")
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logo = Image.open("static/logo.png")
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st.sidebar.image(logo, caption="ferret")
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page = st.sidebar.radio("", list(PAGES.keys()))
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PAGES[page].body()
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corpus.py
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import streamlit as st
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def body():
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st.text("TBD")
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requirements.txt
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single.py
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import streamlit as st
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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@st.cache()
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def get_model(model_name):
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return AutoModelForSequenceClassification.from_pretrained(model_name)
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@st.cache()
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def get_tokenizer(tokenizer_name):
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return AutoTokenizer.from_pretrained(tokenizer_name, use_fast=True)
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def body():
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st.title("Evaluate using *ferret* !")
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st.markdown(
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"""
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### 👋 Hi!
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Insert down below your text, choose a model and fire up ferret. We will use
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*ferret* to:
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1. produce explanations with all supported methods
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2. evaluate explanations on state-of-the-art **faithfulness metrics**.
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"""
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)
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col1, col2 = st.columns([1, 1])
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with col1:
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model_name = st.text_input("HF Model", "g8a9/bert-base-cased_ami18")
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with col2:
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tokenizer_name = st.text_input("HF Tokenizer", "bert-base-cased")
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text = st.text_input("Text")
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compute = st.button("Compute")
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if text and compute and model_name and tokenizer_name:
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st.text("hellp")
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# model = get_model(model_name)
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# tokenizer = get_tokenizer(tokenizer_name)
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static/logo.png
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