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import streamlit as st
import torch
import logging
import time
from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration
# Configure page
st.set_page_config(page_title="🌐 Translator", page_icon="🌐")
# Device detection
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if device.type == "cpu":
logging.warning("⚠️ GPU not found — using CPU (translation may be slow).")
# Language mapping
lang_id = {
"Afrikaans": "af", "Amharic": "am", "Arabic": "ar", "Asturian": "ast",
"Azerbaijani": "az", "Bashkir": "ba", "Belarusian": "be", "Bulgarian": "bg",
"Bengali": "bn", "Breton": "br", "Bosnian": "bs", "Catalan": "ca",
"Cebuano": "ceb", "Czech": "cs", "Welsh": "cy", "Danish": "da",
"German": "de", "Greeek": "el", "English": "en", "Spanish": "es",
"Estonian": "et", "Persian": "fa", "Fulah": "ff", "Finnish": "fi",
"French": "fr", "Western Frisian": "fy", "Irish": "ga", "Gaelic": "gd",
"Galician": "gl", "Gujarati": "gu", "Hausa": "ha", "Hebrew": "he",
"Hindi": "hi", "Croatian": "hr", "Haitian": "ht", "Hungarian": "hu",
"Armenian": "hy", "Indonesian": "id", "Igbo": "ig", "Iloko": "ilo",
"Icelandic": "is", "Italian": "it", "Japanese": "ja", "Javanese": "jv",
"Georgian": "ka", "Kazakh": "kk", "Central Khmer": "km", "Kannada": "kn",
"Korean": "ko", "Luxembourgish": "lb", "Ganda": "lg", "Lingala": "ln",
"Lao": "lo", "Lithuanian": "lt", "Latvian": "lv", "Malagasy": "mg",
"Macedonian": "mk", "Malayalam": "ml", "Mongolian": "mn", "Marathi": "mr",
"Malay": "ms", "Burmese": "my", "Nepali": "ne", "Dutch": "nl",
"Norwegian": "no", "Northern Sotho": "ns", "Occitan": "oc", "Oriya": "or",
"Panjabi": "pa", "Polish": "pl", "Pushto": "ps", "Portuguese": "pt",
"Romanian": "ro", "Russian": "ru", "Sindhi": "sd", "Sinhala": "si",
"Slovak": "sk", "Slovenian": "sl", "Somali": "so", "Albanian": "sq",
"Serbian": "sr", "Swati": "ss", "Sundanese": "su", "Swedish": "sv",
"Swahili": "sw", "Tamil": "ta", "Thai": "th", "Tagalog": "tl",
"Tswana": "tn", "Turkish": "tr", "Ukrainian": "uk", "Urdu": "ur",
"Uzbek": "uz", "Vietnamese": "vi", "Wolof": "wo", "Xhosa": "xh",
"Yiddish": "yi", "Yoruba": "yo", "Chinese": "zh", "Zulu": "zu",
}
# Cache model/tokenizer loading
@st.cache_resource
def load_model():
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_1.2B")
model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_1.2B").to(device)
model.eval()
return tokenizer, model
# Title
st.title("🌍 M2M100 Language Translator")
st.markdown("🔁 Translate text between **100+ languages** using Facebook's `M2M100` multilingual model.")
# Text input
user_input = st.text_area(
"✏️ Enter your text below:",
height=200,
max_chars=5120,
placeholder="E.g. Hello, how are you?"
)
# Language selections (default: English → Hindi)
col1, col2 = st.columns(2)
with col1:
source_lang = st.selectbox("🌐 Source Language", sorted(lang_id.keys()), index=list(lang_id.keys()).index("English"))
with col2:
target_lang = st.selectbox("🔁 Target Language", sorted(lang_id.keys()), index=list(lang_id.keys()).index("Hindi"))
# Translate Button
if st.button("🚀 Translate", disabled=(not user_input.strip())):
with st.spinner("Translating... Please wait"):
start = time.time()
tokenizer, model = load_model()
src = lang_id[source_lang]
tgt = lang_id[target_lang]
tokenizer.src_lang = src
with torch.no_grad():
encoded = tokenizer(user_input, return_tensors="pt").to(device)
output = model.generate(
**encoded,
forced_bos_token_id=tokenizer.get_lang_id(tgt)
)
result = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
end = time.time()
st.success("✅ Translation complete!")
st.markdown("### 📝 Translated Text")
st.text_area("Output", value=result, height=150, disabled=True)
st.caption(f"⏱️ Time taken: {round(end - start, 2)} seconds")
# Optional reset
st.markdown("---")
if st.button("🔄 Reset"):
st.experimental_rerun()
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