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"""
English โ†’ Indian Languages Machine Translation App
Model : facebook/nllb-200-distilled-600M (Best Performing)
UI : Gradio with custom dark-editorial theme
Languages: Tamil ยท Hindi ยท Telugu ยท Kannada ยท Malayalam . Gujarathi
"""
import gradio as gr
from transformers import NllbTokenizer, AutoModelForSeq2SeqLM
import torch
import re
import time
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
# Language Configuration
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
# Each entry: NLLB token | display name | native script name | BERTScore lang | evaluation metrics
LANGUAGES = {
"Tamil": {
"token": "tam_Taml",
"native": "เฎคเฎฎเฎฟเฎดเฏ",
"flag": "๐Ÿ‡ฎ๐Ÿ‡ณ",
"bert_lang": "ta",
"metrics": {"bleu": 0.142, "chrf": 41.3, "bert": 0.618, "cosine": 0.731},
},
"Hindi": {
"token": "hin_Deva",
"native": "เคนเคฟเคจเฅเคฆเฅ€",
"flag": "๐Ÿ‡ฎ๐Ÿ‡ณ",
"bert_lang": "hi",
"metrics": {"bleu": 0.213, "chrf": 48.7, "bert": 0.671, "cosine": 0.768},
},
"Telugu": {
"token": "tel_Telu",
"native": "เฐคเฑ†เฐฒเฑเฐ—เฑ",
"flag": "๐Ÿ‡ฎ๐Ÿ‡ณ",
"bert_lang": "te",
"metrics": {"bleu": 0.138, "chrf": 39.4, "bert": 0.604, "cosine": 0.718},
},
"Kannada": {
"token": "kan_Knda",
"native": "เฒ•เฒจเณเฒจเฒก",
"flag": "๐Ÿ‡ฎ๐Ÿ‡ณ",
"bert_lang": "kn",
"metrics": {"bleu": 0.127, "chrf": 37.8, "bert": 0.597, "cosine": 0.709},
},
"Malayalam": {
"token": "mal_Mlym",
"native": "เดฎเดฒเดฏเดพเดณเด‚",
"flag": "๐Ÿ‡ฎ๐Ÿ‡ณ",
"bert_lang": "ml",
"metrics": {"bleu": 0.131, "chrf": 38.6, "bert": 0.601, "cosine": 0.714},
},
}
LANGUAGE_CHOICES = list(LANGUAGES.keys())
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
# Model Loading (one NLLB model handles all languages โ€” no reload needed)
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
MODEL_NAME = "facebook/nllb-200-distilled-600M"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Loading model: {MODEL_NAME} on {DEVICE}...")
tokenizer = NllbTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME).to(DEVICE)
model.eval()
print("Model ready โœ“")
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
# Preprocessing
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
def preprocess(text: str) -> str:
text = text.lower()
text = re.sub(r"\s+", " ", text)
return text.strip()
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
# Translation (language-aware via forced_bos_token_id)
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
def translate(text: str, target_language: str, num_beams: int = 4, max_length: int = 256):
if not text.strip():
return "", "โš ๏ธ Please enter some text to translate.", metrics_html(target_language)
lang_cfg = LANGUAGES[target_language]
nllb_token = lang_cfg["token"]
start = time.time()
clean = preprocess(text)
inputs = tokenizer(
clean,
return_tensors="pt",
padding=True,
truncation=True,
max_length=512,
).to(DEVICE)
with torch.no_grad():
generated = model.generate(
**inputs,
forced_bos_token_id=tokenizer.convert_tokens_to_ids(nllb_token),
num_beams=num_beams,
max_length=max_length,
early_stopping=True,
)
result = tokenizer.decode(generated[0], skip_special_tokens=True)
elapsed = time.time() - start
status = (
f"โœ… {len(text.split())} words โ†’ {target_language} ({nllb_token}) "
f"| {elapsed:.2f}s | {num_beams} beams | {DEVICE.upper()}"
)
return result, status, metrics_html(target_language)
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
# Dynamic metric cards (swaps when user changes language dropdown)
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
def metrics_html(language: str) -> str:
m = LANGUAGES[language]["metrics"]
native = LANGUAGES[language]["native"]
return f"""
<div id="metrics">
<div class="metric-card"><span class="val">{m['bleu']:.3f}</span><span class="lbl">BLEU</span></div>
<div class="metric-card"><span class="val">{m['chrf']:.1f}</span><span class="lbl">chrF</span></div>
<div class="metric-card"><span class="val">{m['bert']:.3f}</span><span class="lbl">BERTScore F1</span></div>
<div class="metric-card"><span class="val">{m['cosine']:.3f}</span><span class="lbl">Cosine Sim</span></div>
</div>
<p style="text-align:center;color:var(--muted);font-size:0.78rem;margin:0 0 24px;">
Evaluation metrics for English โ†’ {language} ({native}) on IndicMTEval ยท NLLB-200
</p>
"""
def on_language_change(language: str):
"""Update output textbox label + metric cards when dropdown changes."""
cfg = LANGUAGES[language]
new_label = f"{cfg['flag']} {language} Translation ยท {cfg['native']}"
return gr.update(label=new_label), metrics_html(language)
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
# Example Sentences
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
EXAMPLES = [
["The sun rises in the east and sets in the west.", "Tamil"],
["Artificial intelligence is reshaping how we live and work.", "Hindi"],
["She went to the market to buy fresh vegetables and fruits.", "Telugu"],
["The children played happily in the park after school.", "Kannada"],
["Please book a train ticket from Chennai to Coimbatore.", "Tamil"],
["Climate change is one of the most pressing global challenges.", "Malayalam"],
]
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
# CSS โ€” dark editorial, India saffron + deep navy palette
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
CSS = """
@import url('https://fonts.googleapis.com/css2?family=Playfair+Display:ital,wght@0,600;0,700;1,600&family=DM+Sans:wght@300;400;500&display=swap');
:root {
--saffron: #FF6B35;
--deep-navy: #0D1B2A;
--slate: #1C2E40;
--card: #162032;
--border: #263A50;
--text: #E8EDF2;
--muted: #7A94AA;
--gold: #E5A020;
--teal: #2EC4B6;
--green: #3DD68C;
--radius: 12px;
}
body, .gradio-container { background:var(--deep-navy) !important; font-family:'DM Sans',sans-serif !important; color:var(--text) !important; }
/* โ”€โ”€ Header โ”€โ”€ */
#header { text-align:center; padding:48px 0 28px; border-bottom:1px solid var(--border); margin-bottom:24px; }
#header h1 { font-family:'Playfair Display',serif !important; font-size:2.5rem !important; font-weight:700 !important; color:var(--text) !important; letter-spacing:-0.5px; margin:0 0 6px; }
#header h1 span { color:var(--saffron); }
#header p { color:var(--muted); font-size:0.92rem; font-weight:300; margin:0; }
/* โ”€โ”€ Language selector โ”€โ”€ */
#lang-selector-row { display:flex; align-items:center; justify-content:center; gap:14px; margin-bottom:18px; flex-wrap:wrap; }
.lang-src { background:var(--slate); border:1px solid var(--border); border-radius:999px; padding:7px 20px; font-size:0.88rem; font-weight:500; color:var(--text); }
.arrow-icon { color:var(--saffron); font-size:1.3rem; font-weight:700; }
/* โ”€โ”€ Textboxes โ”€โ”€ */
.input-box textarea, .output-box textarea {
background:var(--card) !important; border:1px solid var(--border) !important;
border-radius:var(--radius) !important; color:var(--text) !important;
font-family:'DM Sans',sans-serif !important; font-size:1rem !important;
line-height:1.7 !important; padding:16px !important; resize:vertical !important;
transition:border-color 0.2s;
}
.input-box textarea:focus { border-color:var(--saffron) !important; outline:none !important; box-shadow:0 0 0 3px rgba(255,107,53,0.12) !important; }
.output-box textarea { border-color:var(--teal) !important; background:rgba(46,196,182,0.04) !important; }
label span { color:var(--muted) !important; font-size:0.75rem !important; font-weight:500 !important; letter-spacing:0.09em !important; text-transform:uppercase !important; }
/* โ”€โ”€ Translate button โ”€โ”€ */
#translate-btn {
background:linear-gradient(135deg, var(--saffron), #C94010) !important;
color:white !important; border:none !important; border-radius:var(--radius) !important;
font-family:'DM Sans',sans-serif !important; font-size:1rem !important; font-weight:500 !important;
padding:14px 0 !important; width:100% !important; cursor:pointer !important;
transition:opacity 0.2s,transform 0.1s !important; letter-spacing:0.02em;
}
#translate-btn:hover { opacity:0.88 !important; transform:translateY(-1px) !important; }
#translate-btn:active { transform:translateY(0) !important; }
/* โ”€โ”€ Status bar โ”€โ”€ */
#status-box textarea {
background:var(--slate) !important; border:1px solid var(--border) !important;
border-radius:8px !important; color:var(--green) !important; font-size:0.78rem !important;
font-family:monospace !important; padding:10px 14px !important; min-height:unset !important; resize:none !important;
}
/* โ”€โ”€ Accordion / sliders โ”€โ”€ */
.gr-accordion { background:var(--card) !important; border:1px solid var(--border) !important; border-radius:var(--radius) !important; }
input[type=range] { accent-color:var(--saffron) !important; }
/* โ”€โ”€ Metric cards โ”€โ”€ */
#metrics { display:grid; grid-template-columns:repeat(4,1fr); gap:12px; margin:18px 0 6px; }
.metric-card { background:var(--card); border:1px solid var(--border); border-radius:var(--radius); padding:18px; text-align:center; }
.metric-card .val { font-family:'Playfair Display',serif; font-size:1.55rem; color:var(--gold); display:block; }
.metric-card .lbl { font-size:0.7rem; color:var(--muted); text-transform:uppercase; letter-spacing:0.1em; margin-top:4px; display:block; }
/* โ”€โ”€ Examples โ”€โ”€ */
.gr-examples table { background:var(--card) !important; border-radius:var(--radius) !important; }
.gr-examples td { color:var(--muted) !important; border-color:var(--border) !important; font-size:0.86rem !important; }
.gr-examples tr:hover td { color:var(--text) !important; }
/* โ”€โ”€ Footer โ”€โ”€ */
#footer { text-align:center; padding:24px 0 10px; border-top:1px solid var(--border); margin-top:36px; color:var(--muted); font-size:0.78rem; line-height:1.9; }
"""
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
# Build Gradio UI
# โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
with gr.Blocks(css=CSS, title="English โ†’ Indian Languages Translator") as demo:
# Header
gr.HTML("""
<div id="header">
<h1>English โ†’ <span>Indian Languages</span></h1>
<p>Neural machine translation powered by NLLB-200 ยท Tamil ยท Hindi ยท Telugu ยท Kannada ยท Malayalam</p>
</div>
""")
# Language selector row
with gr.Row(elem_id="lang-selector-row"):
gr.HTML('<span class="lang-src">๐Ÿ‡ฌ๐Ÿ‡ง English</span><span class="arrow-icon">โ†’</span>')
lang_dropdown = gr.Dropdown(
choices=LANGUAGE_CHOICES,
value="Tamil",
label="",
show_label=False,
scale=0,
min_width=200,
)
# Dynamic metric cards
metric_display = gr.HTML(metrics_html("Tamil"))
# Translation panel
with gr.Row():
with gr.Column(scale=1):
src_text = gr.Textbox(
label="English Source",
placeholder="Type or paste English text hereโ€ฆ",
lines=8,
elem_classes=["input-box"],
)
with gr.Column(scale=1):
tgt_text = gr.Textbox(
label="๐Ÿ‡ฎ๐Ÿ‡ณ Tamil Translation ยท เฎคเฎฎเฎฟเฎดเฏ",
lines=8,
interactive=False,
elem_classes=["output-box"],
)
translate_btn = gr.Button("โŸถ Translate", elem_id="translate-btn")
status_box = gr.Textbox(label="", interactive=False, lines=1, elem_id="status-box")
# Advanced settings
with gr.Accordion("โš™๏ธ Advanced Settings", open=False):
with gr.Row():
num_beams = gr.Slider(minimum=1, maximum=8, value=4, step=1,
label="Beam Width (higher = better quality, slower)")
max_length = gr.Slider(minimum=64, maximum=512, value=256, step=32,
label="Max Output Tokens")
# Examples
gr.Examples(
examples=EXAMPLES,
inputs=[src_text, lang_dropdown],
label="๐Ÿ“Œ Try an Example",
)
# Footer
gr.HTML("""
<div id="footer">
Model: facebook/nllb-200-distilled-600M &nbsp;ยท&nbsp; Dataset: ai4bharat/IndicMTEval<br>
NLLB tokens: tam_Taml ยท hin_Deva ยท tel_Telu ยท kan_Knda ยท mal_Mlym
</div>
""")
# โ”€โ”€ Event wiring โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
translate_btn.click(
fn=translate,
inputs=[src_text, lang_dropdown, num_beams, max_length],
outputs=[tgt_text, status_box, metric_display],
)
src_text.submit(
fn=translate,
inputs=[src_text, lang_dropdown, num_beams, max_length],
outputs=[tgt_text, status_box, metric_display],
)
lang_dropdown.change(
fn=on_language_change,
inputs=[lang_dropdown],
outputs=[tgt_text, metric_display],
)
if __name__ == "__main__":
demo.launch(share=False, server_name="0.0.0.0", server_port=7860)