Add streamlit app

app.py

@@ -0,0 +1,55 @@
+import io   
+
+import pandas as pd
+import streamlit as st
+
+from transformers import AutoTokenizer
+
+from tapas_visualizer import TapasVisualizer
+
+st.set_page_config(page_title="Tapas Tokenizer", page_icon='‍🍽️', layout="wide")
+
+def set_file_input():
+    st.session_state.input_stream = "file"
+
+def set_text_input():
+    st.session_state.input_stream = "text"
+
+
+def main():
+
+    models = [
+        "google/tapas-base",
+        "deepset/tapas-large-nq-hn-reader"
+    ]
+
+    @st.cache()
+    def load_tokenizer():
+        tokenizer = AutoTokenizer.from_pretrained(selected_model)
+        return tokenizer
+
+    col1, col2 = st.columns([1, 2])
+    with col1:
+        selected_model = st.selectbox("Select a tokenizer", models, key=1)
+        text = st.text_area(label="", placeholder="Table to tokenize; csv", on_change=set_text_input)
+        uploaded_file = st.file_uploader("(Or) Choose a file", on_change=set_file_input)
+        button_clicked = st.button("Tokenize")
+
+    tokenizer = load_tokenizer()
+    visualizer = TapasVisualizer(tokenizer)
+
+    with col2:
+        if text or uploaded_file or button_clicked:
+            df: pd.DataFrame
+
+            if 'input_stream' not in st.session_state or st.session_state.input_stream == "text":
+                df = pd.read_csv(io.StringIO(text), sep=",")
+            elif st.session_state.input_stream == "file":
+                df = pd.read_csv(uploaded_file)
+
+            if df is not None:
+                st.components.v1.html(visualizer(df.astype(str)), height=1500)
+
+
+if __name__ == '__main__':
+    main()