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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
MODEL_NAME = "Helsinki-NLP/opus-mt-ur-en"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
EXAMPLES = [
["پاکستان ایک خوبصورت ملک ہے۔ اس کے پہاڑ اور دریا بہت مشہور ہیں۔"],
["اسلام آباد پاکستان کا دارالحکومت ہے۔ یہ ایک جدید اور صاف شہر ہے۔"],
["کرکٹ پاکستان کا سب سے مقبول کھیل ہے۔ پاکستانی عوام کرکٹ سے بہت محبت کرتے ہیں۔"],
["تعلیم ہر انسان کا بنیادی حق ہے۔ علم کی روشنی سے دنیا کو بہتر بنایا جا سکتا ہے۔"],
]
def translate(text, num_beams):
if not text.strip():
return "", 0
inputs = tokenizer(
text.strip(),
return_tensors="pt",
truncation=True,
max_length=512
)
with torch.no_grad():
outputs = model.generate(
inputs["input_ids"],
max_length=256,
num_beams=int(num_beams),
early_stopping=True
)
translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
word_count = len(translation.split())
return translation, word_count
css = """
@import url('https://fonts.googleapis.com/css2?family=Noto+Nastaliq+Urdu&family=Inter:wght@400;500;600&display=swap');
body, .gradio-container { background: #0a0a0f !important; font-family: 'Inter', sans-serif; }
#title { text-align: center; padding: 2rem 1rem 1rem; }
#title h1 { font-size: 2rem; font-weight: 600; color: #e4e4f0; margin-bottom: 0.4rem; }
#title p { color: #6b6b8a; font-size: 0.9rem; }
#stats {
display: flex; justify-content: center; gap: 2rem;
padding: 0.5rem; margin-bottom: 1rem;
}
.stat-card {
background: #12121e; border: 1px solid #2a2a3e;
border-radius: 10px; padding: 0.6rem 1.2rem; text-align: center;
}
.stat-num { font-size: 1.2rem; font-weight: 600; color: #3b82f6; }
.stat-label { font-size: 0.7rem; color: #6b6b8a; margin-top: 2px; }
.urdu-box textarea {
font-family: 'Noto Nastaliq Urdu', serif !important;
font-size: 1.2rem !important; direction: rtl !important;
text-align: right !important; line-height: 2.2 !important;
background: #12121e !important; border: 1px solid #2a2a3e !important;
color: #e4e4f0 !important; border-radius: 10px !important;
}
.urdu-box textarea:focus {
border-color: #3b82f6 !important;
box-shadow: 0 0 0 2px rgba(59, 130, 246, 0.15) !important;
}
.english-box textarea {
font-family: 'Inter', sans-serif !important;
font-size: 1.1rem !important;
background: #12121e !important; border: 1px solid #2a2a3e !important;
color: #60a5fa !important; border-radius: 10px !important;
line-height: 1.8 !important;
}
button.primary {
background: #3b82f6 !important; border: none !important;
color: white !important; font-weight: 500 !important;
border-radius: 8px !important;
}
button.primary:hover { background: #2563eb !important; }
footer { display: none !important; }
"""
with gr.Blocks(css=css, theme=gr.themes.Base()) as demo:
gr.HTML("""
<div id="title">
<h1>🌐 Urdu → English Translator</h1>
<p>اردو سے انگریزی ترجمہ — Helsinki-NLP opus-mt-ur-en trained on 2.7M sentence pairs</p>
</div>
<div id="stats">
<div class="stat-card">
<div class="stat-num">2.7M</div>
<div class="stat-label">Training sentence pairs</div>
</div>
<div class="stat-card">
<div class="stat-num">opus-mt</div>
<div class="stat-label">Model architecture</div>
</div>
<div class="stat-card">
<div class="stat-num">Marian</div>
<div class="stat-label">Framework</div>
</div>
</div>
""")
with gr.Row():
with gr.Column():
input_text = gr.Textbox(
label="اردو متن — Urdu Text",
placeholder="یہاں اردو میں لکھیں یا پیسٹ کریں...",
lines=7,
elem_classes="urdu-box"
)
num_beams = gr.Slider(
label="Beam search width (higher = better quality, slower)",
minimum=1, maximum=8, value=4, step=1
)
translate_btn = gr.Button("ترجمہ کریں — Translate", variant="primary")
with gr.Column():
output_text = gr.Textbox(
label="English Translation",
lines=7,
interactive=False,
elem_classes="english-box"
)
word_count = gr.Number(label="Translation word count", interactive=False)
gr.Examples(
examples=EXAMPLES,
inputs=input_text,
label="مثالیں — Try these examples"
)
gr.HTML("""
<div style="text-align:center; padding:1.5rem; color:#3a3a5a; font-size:0.8rem;">
Built by <a href="https://huggingface.co/H-Layba" style="color:#3b82f6">H-Layba</a> ·
Model: Helsinki-NLP/opus-mt-ur-en · OPUS parallel corpus
</div>
""")
translate_btn.click(
fn=translate,
inputs=[input_text, num_beams],
outputs=[output_text, word_count]
)
input_text.submit(
fn=translate,
inputs=[input_text, num_beams],
outputs=[output_text, word_count]
)
demo.launch()
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