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| # ============================== | |
| # π’ Step 2: Import Required Packages | |
| # ============================== | |
| from transformers import MarianMTModel, MarianTokenizer | |
| import gradio as gr | |
| # ============================== | |
| # π’ Step 3: Load Translation Model | |
| # ============================== | |
| model_name = "Helsinki-NLP/opus-mt-en-ur" # English β Urdu model | |
| tokenizer = MarianTokenizer.from_pretrained(model_name) | |
| model = MarianMTModel.from_pretrained(model_name) | |
| # ============================== | |
| # π’ Step 4: Define 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 = model.generate(**inputs, max_length=100) | |
| urdu_text = tokenizer.decode(translated[0], skip_special_tokens=True) | |
| return urdu_text | |
| # ============================== | |
| # π’ Step 5: Create Gradio Interface | |
| # ============================== | |
| interface = gr.Interface( | |
| fn=translate_to_urdu, | |
| inputs=gr.Textbox(lines=3, placeholder="Enter English text here..."), | |
| outputs="text", | |
| title="π English to Urdu Translator", | |
| description="Translate English sentences into Urdu using a pretrained Hugging Face model." | |
| ) | |
| # ============================== | |
| # π’ Step 6: Launch App | |
| # ============================== | |
| interface.launch(share=True) | |