Spaces:
Running
Running
File size: 3,964 Bytes
8defc61 206ed66 8defc61 206ed66 604feee 206ed66 8defc61 206ed66 8defc61 206ed66 8defc61 604feee 206ed66 8defc61 206ed66 8defc61 206ed66 8defc61 604feee 8defc61 604feee 8defc61 206ed66 8defc61 604feee 206ed66 604feee 206ed66 604feee 8defc61 604feee 8defc61 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
# 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",
)
|