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import streamlit as st | |
from transformers import pipeline | |
import pandas as pd | |
dialects = {"Palestinian/Jordanian": "P", "Syrian": "S", "Lebanese": "L", "Egyptian": "E"} | |
pipeline = pipeline(task="translation", model="guymorlan/English2Dialect") | |
st.title("English to Levantine Arabic") | |
num_translations = st.sidebar.selectbox("Number of Translations Per Dialect:", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], index=0) | |
input_text = st.text_input("Enter English text:") | |
if input_text: | |
inputs = [f"{val} {input_text}" for val in dialects.values()] | |
result = pipeline(inputs, max_length=1024, num_return_sequences=num_translations, num_beams=max(num_translations, 5)) | |
#df = pd.DataFrame({"Dialect": [x for x in dialects.keys()], | |
# "Translation": [x["translation_text"] for x in result]}) | |
for i in range(len(result)): | |
st.markdown(f"<div style='font-size:24px'><b>{list(dialects.keys())[i]}:</b></div>", unsafe_allow_html=True) | |
if num_translations > 1: | |
for j in range(num_translations): | |
st.markdown(f"<div style='font-size:24px; text-align:right; direction:rtl;'>{result[i][j]['translation_text']}</div>", unsafe_allow_html=True) | |
else: | |
st.markdown(f"<div style='font-size:24px; text-align:right; direction:rtl;'>{result[i]['translation_text']}</div>", unsafe_allow_html=True) | |
st.markdown("<br>", unsafe_allow_html=True) |