Create app.py

app.py

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+import gradio as gr
+from transformers import MarianMTModel, MarianTokenizer
+
+# Load the pre-trained model and tokenizer for English-to-Urdu translation
+model_name = 'Helsinki-NLP/opus-mt-en-ur'
+model = MarianMTModel.from_pretrained(model_name)
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+
+# Function to translate English text to Urdu
+def translate_to_urdu(text, chunk_size=500):
+    # Split the text into chunks to handle long sentences
+    words = text.split()
+    chunks = [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
+    
+    translated_text = []
+    
+    for chunk in chunks:
+        # Tokenize the chunk and translate it
+        translated = tokenizer.encode(chunk, return_tensors="pt", padding=True, truncation=True, max_length=512)
+        chunk_translation = model.generate(translated, max_length=512, num_beams=4, early_stopping=True)
+        
+        # Decode and add the translated chunk to the result
+        translated_chunk = tokenizer.decode(chunk_translation[0], skip_special_tokens=True)
+        translated_text.append(translated_chunk)
+    
+    # Join all translated chunks into one complete translation
+    return ' '.join(translated_text)
+
+# Gradio interface
+def gradio_interface(english_text):
+    return translate_to_urdu(english_text)
+
+# Create the Gradio interface with input and output textboxes
+iface = gr.Interface(
+    fn=gradio_interface,  # Function that handles translation
+    inputs=gr.Textbox(label="English Text", placeholder="Type English text here..."),
+    outputs=gr.Textbox(label="Urdu Translation", placeholder="Translated Urdu text will appear here...", interactive=False),
+    title="English to Urdu Translation",
+    description="Enter English text and get the translation in Urdu. Long texts will be split into chunks for translation.",
+)
+
+# Launch the app
+iface.launch()