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"""
🗣️ Translator - Translate text from one language to another.
Application file made with Streamlit.
Author:
- @ChainYo
"""
import re
import streamlit as st
from datetime import datetime
from transformers import pipeline
from available_models import MODELS
st.set_page_config(page_title="Translator", page_icon="🗣️")
st.title("🗣️ Translator")
st.subheader("Translation made fast and easy.")
st.markdown("""
[](https://github.com/ChainYo)
[](https://huggingface.co./ChainYo)
[](https://www.linkedin.com/in/thomas-chaigneau-dev/)
[](https://discord.gg/)
""")
st.write("To add a new model, hit me up! ⬆️")
with st.expander(label="❓ How does it work", expanded=True):
st.markdown("""
**Translator** is a **simple tool** that allows you to **translate text** from one language to another.
**Translator** is powered by the [Transformers library](https://huggingface.co./transformers) and uses the
[Helsinki-NLP](https://huggingface.co./Helsinki-NLP) models.
Choose the **source language**, the **target language** and add some **text to translate**.
**Translator** will translate the text and **save the output in a text file**. It cuts the sentences by following
the punctuation marks.
The output file content will also be displayed in the browser to help you understand the translation and choose
if you want to download it.
There is **no limit to the number of characters** that can be translated.
The only limit is the time you are ready to wait! 🤗
*P.S. I have built this tool to help me start writing blog posts in different languages. I am a French native speaker
and I will use it to translate my potential future blog posts in English.*
*P.P.S. I am a **Junior ML Engineer** passionate about **machine learning** and **data science**. Reach out to me by
clicking on the socials badges above.*
""")
lang1, lang2 = st.columns(2)
lang1.selectbox(
"Source Language", ["🇬🇧 English", "🇫🇷 French", "🇩🇪 German", "🇪🇸 Spanish", "🇷🇺 Russian"],
key="input_lang", index=1,
)
lang2.selectbox(
"Target Language", ["🇬🇧 English", "🇫🇷 French", "🇩🇪 German", "🇪🇸 Spanish", "🇷🇺 Russian"],
key="output_lang", index=0,
)
selected_model = MODELS[f"{st.session_state['input_lang']}->{st.session_state['output_lang']}"]
if selected_model[0] == None:
st.write("No model available for this pair.")
elif selected_model[0] == 0:
st.write("No translation necessary.")
else:
st.markdown(f"""
**Selected model:** [{selected_model[0]}]({selected_model[1]})
""")
input_text = st.text_area("Enter text to translate:", height=400, key="input")
translate_text = st.button("Translate")
if translate_text:
with st.spinner(text="⚙️ Model loading..."):
task = pipeline(
"translation",
model=selected_model[0],
tokenizer=selected_model[0],
)
progress_bar = st.progress(0)
with st.spinner(text="🔄 Translating..."):
text_to_translate = re.split('(?<=[.!?]) +', input_text)
total_progress = len(text_to_translate)
for i, text in enumerate(text_to_translate):
translation = task(text)
text_to_translate[i] = translation[0]["translation_text"]
progress_bar.progress((i + 1) / total_progress)
st.success("🗣️ Translated!")
st.write(f"**Translation:** {' '.join(text_to_translate)}")
st.download_button(
label="Download translated text",
data="\n".join(text_to_translate),
file_name=f"{st.session_state['input_lang']}-{st.session_state['output_lang']}-{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}.txt",
mime="text/plain"
)
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