app.py

# Install dependencies (run this once in Colab or your terminal)
!pip install transformers gradio torch

# ---- English → Urdu Translator App ----
from transformers import MBart50TokenizerFast, MBartForConditionalGeneration
import gradio as gr

# Load model and tokenizer
model_name = "abdulwaheed1/english-to-urdu-translation-mbart"
tokenizer = MBart50TokenizerFast.from_pretrained(model_name, src_lang="en_XX", tgt_lang="ur_PK")
model = MBartForConditionalGeneration.from_pretrained(model_name)

# Translation function
def translate_to_urdu(text):
    if not text.strip():
        return "Please enter some English text."
    inputs = tokenizer(text, return_tensors="pt", padding=True)
    translated_tokens = model.generate(**inputs)
    urdu_output = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
    return urdu_output

# Create a simple dashboard with Gradio
app = gr.Interface(
    fn=translate_to_urdu,
    inputs=gr.Textbox(label="Enter English Text", placeholder="Type your English sentence here..."),
    outputs=gr.Textbox(label="Urdu Translation"),
    title="🕌 English → Urdu Translator",
    description="This is my First fine-tuned mBART model to translate English sentences into Urdu.",
    theme="soft"
)

# Launch app
app.launch(share=True)   # use share=True to get a public link accessible in Chrome