Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("abdulwaheed1/urdu_to_english_translation_mbart")
+model = AutoModelForSeq2SeqLM.from_pretrained("abdulwaheed1/urdu_to_english_translation_mbart")
+
+# Function to translate Urdu text to English
+def translate_urdu_to_english(urdu_text):
+    try:
+        # Tokenize the input Urdu text
+        inputs = tokenizer(urdu_text, return_tensors="pt", padding=True, truncation=True)
+
+        # Generate translation using the model
+        with torch.no_grad():
+            translated_tokens = model.generate(**inputs, max_length=512)
+
+        # Decode the generated tokens into English text
+        translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+
+        return translated_text
+
+    except Exception as e:
+        # Return an error message if something goes wrong
+        return f"Error in translation: {str(e)}"
+
+# Set up Gradio interface
+iface = gr.Interface(
+    fn=translate_urdu_to_english,  # Function to call
+    inputs=gr.Textbox(label="Enter Urdu Text"),  # Textbox for user input
+    outputs=gr.Textbox(label="Translated English Text"),  # Textbox for displaying output
+    live=True  # Optionally, enable live translation (i.e., as the user types)
+)
+
+# Launch the Gradio interface
+iface.launch()