Create app.py

app.py

@@ -0,0 +1,44 @@
+# app.py
+from transformers import MarianMTModel, MarianTokenizer
+import gradio as gr
+
+# Load the pre-trained model and tokenizer
+model_name = "Helsinki-NLP/opus-mt-en-ur"
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
+
+# Define the translation function
+def translate_english_to_urdu(text):
+    # Tokenize the input text
+    tokenized_text = tokenizer.prepare_seq2seq_batch([text], return_tensors="pt")
+    
+    # Perform the translation
+    translated_tokens = model.generate(**tokenized_text)
+    
+    # Decode the translated tokens to a string
+    translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+    
+    return translated_text
+
+# Create a Gradio interface
+def gradio_translator(input_text):
+    # Translate the input text
+    translated_text = translate_english_to_urdu(input_text)
+    return translated_text
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=gradio_translator,  # Function to call
+    inputs=gr.Textbox(lines=2, placeholder="Enter English text here..."),  # Input component
+    outputs=gr.Textbox(lines=2, placeholder="Urdu translation will appear here..."),  # Output component
+    title="English to Urdu Translator",  # Title of the interface
+    description="Translate English text to Urdu using the Helsinki-NLP/opus-mt-en-ur model.",
+    examples=[
+        ["Hello, how are you?"],
+        ["What is your name?"],
+        ["I love programming."]
+    ]
+)
+
+# Launch the Gradio interface
+interface.launch()