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app.py

@@ -1,9 +1,13 @@
 # Hint: this cheatsheet is magic! https://cheat-sheet.streamlit.app/
 
 import constants
+import numpy as np
 import pandas as pd
 import streamlit as st
 from transformers import BertForSequenceClassification, AutoTokenizer
+import random
+import altair as alt
+from altair import X, Y, Scale
 
 
 @st.cache_data
@@ -13,16 +17,14 @@ def convert_df(df):
 
 
 def compute_ALDi(inputs):
-    return 0.5
+    return random.randint(0, 100) / 100
 
 
-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:
+tab1, tab2 = st.tabs(["Input a Sentence", "Upload a File"])
+
+with tab1:
     sent = st.text_input("Arabic Sentence:", placeholder="Enter an Arabic sentence.")
 
     # TODO: Check if this is needed!
@@ -32,37 +34,47 @@ if input_type == constants.CHOICE_TEXT:
         ALDi_score = compute_ALDi(sent)
         st.write(ALDi_score)
 
-else:
+with tab2:
     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"]
 
+        df = pd.concat([df, df, df])
+        df = pd.concat([df, df, df])
+        df = pd.concat([df, df, df])
+        df.reset_index(drop=True, inplace=True)
+
         # 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])
+        chart = (
+            alt.Chart(df.reset_index())
+            .mark_area(color="violet", opacity=0.5)
+            .encode(
+                x=X(field="index", title="Sentence Index"),
+                y=Y("ALDi", scale=Scale(domain=[0, 1]))
+            )
+        )
+        st.altair_chart(chart.interactive(), use_container_width=True)
+
+        col1, col2 = st.columns([4, 1])
 
         with col1:
+            # Display the output
+            st.table(
+                df,
+            )
+
+        with col2:
             # 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")