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