Track the streamlit application file

app.py

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+# Hint: this cheatsheet is magic! https://cheat-sheet.streamlit.app/
+
+import constants
+import pandas as pd
+import streamlit as st
+from transformers import BertForSequenceClassification, AutoTokenizer
+
+
+@st.cache_data
+def convert_df(df):
+    # IMPORTANT: Cache the conversion to prevent computation on every rerun
+    return df.to_csv(index=None).encode("utf-8")
+
+
+def compute_ALDi(inputs):
+    return 0.5
+
+
+input_type = st.sidebar.radio(
+    "Select the input type:", [constants.CHOICE_FILE, constants.CHOICE_TEXT]
+)
+
+st.title(constants.TITLE)
+
+if input_type == constants.CHOICE_TEXT:
+    sent = st.text_input("Arabic Sentence:", placeholder="Enter an Arabic sentence.")
+
+    # TODO: Check if this is needed!
+    st.button("Submit")
+
+    if sent:
+        ALDi_score = compute_ALDi(sent)
+        st.write(ALDi_score)
+
+else:
+    file = st.file_uploader("Upload a file", type=["txt"])
+    if file is not None:
+        df = pd.read_csv(file, sep="\t", header=None)
+        df.columns = ["Sentence"]
+
+        # TODO: Run the model
+        df["ALDi"] = df["Sentence"].apply(lambda s: compute_ALDi(s))
+
+        # A horizontal rule
+        st.markdown("""---""")
+
+        col1, col2 = st.columns([2, 3])
+
+        with col1:
+            # Add a download button
+            csv = convert_df(df)
+
+            st.download_button(
+                label=":file_folder: Download predictions as CSV",
+                data=csv,
+                file_name="ALDi_scores.csv",
+                mime="text/csv",
+            )
+
+            # Display the output
+            st.dataframe(
+                df,
+                hide_index=True,
+            )
+
+        with col2:
+            # TODO: Add the visualization
+            st.image("https://static.streamlit.io/examples/dog.jpg")

constants.py

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+CHOICE_TEXT = "Input Text"
+CHOICE_FILE = "Upload File"
+TITLE = "ALDi: Arabic Level of Dialectness"