Create app.py

app.py

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+import gradio as gr
+from transformers import MarianMTModel, MarianTokenizer
+
+# Load the MarianMT model and tokenizer for English to Urdu translation
+def load_model():
+    model_name = 'Helsinki-NLP/opus-mt-en-ur'  # English to Urdu pre-trained model
+    model = MarianMTModel.from_pretrained(model_name)
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+# Define the translation function
+def translate(text, model, tokenizer):
+    # Prepare the text for translation
+    translated = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    # Generate translation
+    translated = model.generate(**translated)
+    # Decode the translated text
+    translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+    return translated_text
+
+# Load model and tokenizer
+model, tokenizer = load_model()
+
+# Define Gradio interface function
+def gradio_interface(text):
+    return translate(text, model, tokenizer)
+
+# Set up Gradio interface for the translation web app
+interface = gr.Interface(fn=gradio_interface,
+                         inputs="text",  # User input type (text box)
+                         outputs="text", # Output type (translated text)
+                         live=True,  # Updates live as user types
+                         title="English to Urdu Translation Web App", 
+                         description="Translate English text to Urdu using the MarianMT model.")
+
+# Launch the Gradio interface
+interface.launch()