app.py

# ============================================================================
# ENGLISH → URDU AI TRANSLATOR — app.py (Hugging Face Spaces ready)
# Deploy: create a new Space (SDK: Gradio), upload this file + requirements.txt
# ============================================================================

# ----------------------------------------------------------------------------
# 1. IMPORTS
# ----------------------------------------------------------------------------
import re
import torch
import gradio as gr
from transformers import MarianMTModel, MarianTokenizer

# ----------------------------------------------------------------------------
# 2. LOAD THE FREE ENGLISH → URDU MODEL
# ----------------------------------------------------------------------------
# Helsinki-NLP/opus-mt-en-ur is a free, open-source MarianMT model trained
# specifically for English -> Urdu translation. It downloads automatically
# from the Hugging Face Hub on first run and is cached afterwards.
MODEL_NAME = "Helsinki-NLP/opus-mt-en-ur"

print("Loading translation model...")
device = "cuda" if torch.cuda.is_available() else "cpu"

try:
    tokenizer = MarianTokenizer.from_pretrained(MODEL_NAME)
    model = MarianMTModel.from_pretrained(MODEL_NAME).to(device)
    model.eval()
    print(f"Model loaded successfully on device: {device}")
except Exception as e:
    raise RuntimeError(f"Failed to load the translation model: {e}")


# ----------------------------------------------------------------------------
# 3. TRANSLATION LOGIC
# ----------------------------------------------------------------------------
def split_into_sentences(text: str):
    """
    Split a paragraph into sentences so long, multi-sentence text is
    translated more accurately (MarianMT works best on shorter chunks).
    Keeps punctuation attached to each sentence.
    """
    sentences = re.split(r'(?<=[.!?])\s+', text.strip())
    return [s for s in sentences if s.strip()]


def translate_en_to_ur(text: str) -> str:
    """
    Translates English text (single sentence, multiple sentences, or a
    full paragraph) into Urdu using the loaded MarianMT model.
    Punctuation and paragraph breaks are preserved.
    """
    if not text or not text.strip():
        return ""

    # Preserve paragraph breaks by translating each paragraph separately
    paragraphs = text.split("\n")
    translated_paragraphs = []

    for paragraph in paragraphs:
        if not paragraph.strip():
            translated_paragraphs.append("")
            continue

        sentences = split_into_sentences(paragraph)
        translated_sentences = []

        for sentence in sentences:
            inputs = tokenizer(sentence, return_tensors="pt", padding=True, truncation=True).to(device)
            with torch.no_grad():
                translated_tokens = model.generate(**inputs, max_length=512, num_beams=4)
            translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
            translated_sentences.append(translated_text)

        translated_paragraphs.append(" ".join(translated_sentences))

    return "\n".join(translated_paragraphs)


def handle_translate(english_text: str, progress=gr.Progress()):
    """
    Wrapper function called by the Gradio 'Translate' button.
    Handles empty input, shows progress, and catches errors gracefully.
    """
    if not english_text or not english_text.strip():
        return "⚠️ Please enter some English text to translate."

    try:
        progress(0.2, desc="Analyzing text...")
        progress(0.5, desc="Translating to Urdu...")
        result = translate_en_to_ur(english_text)
        progress(1.0, desc="Done!")
        if not result.strip():
            return "⚠️ Could not generate a translation. Please try different text."
        return result
    except Exception as e:
        return f"❌ An error occurred during translation: {str(e)}"


def count_characters(text: str) -> str:
    """Returns a live character/word count string for the input box."""
    if not text:
        return "0 characters | 0 words"
    char_count = len(text)
    word_count = len(text.split())
    return f"{char_count} characters | {word_count} words"


def clear_fields():
    """Resets the input box, output box, and character counter."""
    return "", "", "0 characters | 0 words"


# ----------------------------------------------------------------------------
# 4. CUSTOM CSS — MODERN, ROUNDED, SOFT-COLORED, RESPONSIVE UI
# ----------------------------------------------------------------------------
custom_css = """
.gradio-container {
    font-family: 'Segoe UI', 'Poppins', sans-serif !important;
    background: linear-gradient(135deg, #f5f7fa 0%, #e8eef7 100%) !important;
    max-width: 900px !important;
    margin: auto !important;
}

#title_md h1 {
    text-align: center;
    font-weight: 700;
    background: linear-gradient(90deg, #2b6cb0, #38b2ac);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    margin-bottom: 0px;
}

#subtitle_md {
    text-align: center;
    color: #555;
    margin-top: 4px;
    margin-bottom: 20px;
}

.gr-box, .block {
    border-radius: 16px !important;
    box-shadow: 0 4px 12px rgba(0,0,0,0.06) !important;
}

textarea, input {
    border-radius: 14px !important;
    border: 1px solid #d6e0ea !important;
}

#translate_btn {
    background: linear-gradient(90deg, #2b6cb0, #38b2ac) !important;
    color: white !important;
    border-radius: 14px !important;
    font-weight: 600 !important;
    border: none !important;
}

#clear_btn {
    border-radius: 14px !important;
    font-weight: 600 !important;
    background: #f1f3f6 !important;
    color: #333 !important;
    border: 1px solid #ddd !important;
}

#char_counter {
    text-align: right;
    color: #888;
    font-size: 0.85em;
}

footer {
    visibility: hidden;
}
"""

# ----------------------------------------------------------------------------
# 5. EXAMPLE SENTENCES
# ----------------------------------------------------------------------------
example_sentences = [
    "Hello, how are you today?",
    "I love reading books in my free time.",
    "The weather is beautiful this morning.",
    "Can you please help me with my homework?",
    "Pakistan is a country with a rich cultural heritage.",
    "Artificial intelligence is changing the world rapidly.",
    "Thank you very much for your kindness.",
    "Education is the key to a better future.",
]

# ----------------------------------------------------------------------------
# 6. BUILD THE GRADIO BLOCKS INTERFACE
# ----------------------------------------------------------------------------
with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="blue", secondary_hue="teal")) as demo:

    gr.Markdown("# 🌐 English → Urdu AI Translator", elem_id="title_md")
    gr.Markdown(
        "Translate English text into fluent Urdu instantly, powered by a free open-source "
        "Hugging Face model. Supports single sentences, multiple sentences, and full paragraphs.",
        elem_id="subtitle_md"
    )

    with gr.Row():
        with gr.Column(scale=1):
            english_input = gr.Textbox(
                label="✍️ English Text",
                placeholder="Type or paste English text here...",
                lines=8,
            )
            char_counter = gr.Markdown("0 characters | 0 words", elem_id="char_counter")

            with gr.Row():
                translate_btn = gr.Button("🔁 Translate", elem_id="translate_btn", variant="primary")
                clear_btn = gr.Button("🗑️ Clear", elem_id="clear_btn")

        with gr.Column(scale=1):
            urdu_output = gr.Textbox(
                label="🇵🇰 Urdu Translation",
                placeholder="اردو ترجمہ یہاں ظاہر ہوگا...",
                lines=8,
                rtl=True,
                interactive=False,
            )

    gr.Markdown("### 💡 Try an example:")
    gr.Examples(
        examples=example_sentences,
        inputs=english_input,
        label="Example Sentences",
    )

    # Event wiring
    english_input.change(fn=count_characters, inputs=english_input, outputs=char_counter)
    translate_btn.click(fn=handle_translate, inputs=english_input, outputs=urdu_output)
    english_input.submit(fn=handle_translate, inputs=english_input, outputs=urdu_output)
    clear_btn.click(fn=clear_fields, inputs=None, outputs=[english_input, urdu_output, char_counter])

# ----------------------------------------------------------------------------
# 7. LAUNCH — works both locally and on Hugging Face Spaces
# ----------------------------------------------------------------------------
if __name__ == "__main__":
    demo.launch()