app.py

# Hint: this cheatsheet is magic! https://cheat-sheet.streamlit.app/
import constants
import pandas as pd
import streamlit as st
import matplotlib.pyplot as plt
from transformers import BertForSequenceClassification, AutoTokenizer

import altair as alt
from altair import X, Y, Scale
import base64


@st.cache_data
def render_svg(svg):
    """Renders the given svg string."""
    b64 = base64.b64encode(svg.encode("utf-8")).decode("utf-8")
    html = rf'<p align="center"> <img src="data:image/svg+xml;base64,{b64}"/> </p>'
    c = st.container()
    c.write(html, unsafe_allow_html=True)


@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")


@st.cache_resource
def load_model(model_name):
    model = BertForSequenceClassification.from_pretrained(model_name)
    return model


tokenizer = AutoTokenizer.from_pretrained(constants.MODEL_NAME)
model = load_model(constants.MODEL_NAME)


def compute_ALDi(sentences):
    # TODO: Perform inference in batches
    progress_text = "Computing ALDi..."
    my_bar = st.progress(0, text=progress_text)

    BATCH_SIZE = 4
    output_logits = []
    for first_index in range(0, len(sentences), BATCH_SIZE):
        inputs = tokenizer(
            sentences[first_index : first_index + BATCH_SIZE],
            return_tensors="pt",
            padding=True,
        )
        outputs = model(**inputs).logits.reshape(-1).tolist()
        output_logits = output_logits + [max(min(o, 1), 0) for o in outputs]
        my_bar.progress(
            min((first_index + BATCH_SIZE) / len(sentences), 1), text=progress_text
        )
    my_bar.empty()
    return output_logits


render_svg(open("assets/ALDi_logo.svg").read())

tab1, tab2 = st.tabs(["Input a Sentence", "Upload a File"])

with tab1:
    sent = st.text_input(
        "Arabic Sentence:", placeholder="Enter an Arabic sentence.", on_change=None
    )

    # TODO: Check if this is needed!
    clicked = st.button("Submit")

    if sent:
        ALDi_score = compute_ALDi([sent])[0]

        ORANGE_COLOR = "#FF8000"
        fig, ax = plt.subplots(figsize=(8, 1))
        fig.patch.set_facecolor("none")
        ax.set_facecolor("none")

        ax.spines["left"].set_color(ORANGE_COLOR)
        ax.spines["bottom"].set_color(ORANGE_COLOR)
        ax.tick_params(axis="x", colors=ORANGE_COLOR)

        ax.spines[["right", "top"]].set_visible(False)

        ax.barh(y=[0], width=[ALDi_score], color=ORANGE_COLOR)
        ax.set_xlim(0, 1)
        ax.set_ylim(-1, 1)
        ax.set_title(f"ALDi score is: {round(ALDi_score, 3)}", color=ORANGE_COLOR)
        ax.get_yaxis().set_visible(False)
        ax.set_xlabel("ALDi score", color=ORANGE_COLOR)
        st.pyplot(fig)

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.reset_index(drop=True, inplace=True)

        # TODO: Run the model
        df["ALDi"] = compute_ALDi(df["Sentence"].tolist())

        # A horizontal rule
        st.markdown("""---""")

        chart = (
            alt.Chart(df.reset_index())
            .mark_area(color="darkorange", 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",
            )