add skeleton

app.py

@@ -0,0 +1,17 @@
+import streamlit as st
+import single
+import corpus
+from PIL import Image
+
+PAGES = {
+    "Single Explanation": single,
+    "Corpus Explanation": corpus,
+}
+
+st.sidebar.title("Explore ferret!")
+
+logo = Image.open("static/logo.png")
+st.sidebar.image(logo, caption="ferret")
+
+page = st.sidebar.radio("", list(PAGES.keys()))
+PAGES[page].body()

corpus.py

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+import streamlit as st
+
+
+def body():
+    st.text("TBD")

single.py

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+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+
+@st.cache()
+def get_model(model_name):
+    return AutoModelForSequenceClassification.from_pretrained(model_name)
+
+
+@st.cache()
+def get_tokenizer(tokenizer_name):
+    return AutoTokenizer.from_pretrained(tokenizer_name, use_fast=True)
+
+
+def body():
+
+    st.title("Evaluate using *ferret* !")
+
+    st.markdown(
+        """
+    
+        ### 👋 Hi!
+
+        Insert down below your text, choose a model and fire up ferret. We will use
+        *ferret* to:
+        1. produce explanations with all supported methods
+        2. evaluate explanations on state-of-the-art **faithfulness metrics**. 
+        """
+    )
+
+    col1, col2 = st.columns([1, 1])
+    with col1:
+        model_name = st.text_input("HF Model", "g8a9/bert-base-cased_ami18")
+    with col2:
+        tokenizer_name = st.text_input("HF Tokenizer", "bert-base-cased")
+
+    text = st.text_input("Text")
+
+    compute = st.button("Compute")
+
+    if text and compute and model_name and tokenizer_name:
+        st.text("hellp")
+
+    # model = get_model(model_name)
+    # tokenizer = get_tokenizer(tokenizer_name)