Create app.py

app.py

@@ -0,0 +1,34 @@
+# Install dependencies (run this once in Colab or your terminal)
+!pip install transformers gradio torch
+
+# ---- English → Urdu Translator App ----
+from transformers import MBart50TokenizerFast, MBartForConditionalGeneration
+import gradio as gr
+
+# Load model and tokenizer
+model_name = "abdulwaheed1/english-to-urdu-translation-mbart"
+tokenizer = MBart50TokenizerFast.from_pretrained(model_name, src_lang="en_XX", tgt_lang="ur_PK")
+model = MBartForConditionalGeneration.from_pretrained(model_name)
+
+# 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_tokens = model.generate(**inputs)
+    urdu_output = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+    return urdu_output
+
+# Create a simple dashboard with Gradio
+app = gr.Interface(
+    fn=translate_to_urdu,
+    inputs=gr.Textbox(label="Enter English Text", placeholder="Type your English sentence here..."),
+    outputs=gr.Textbox(label="Urdu Translation"),
+    title="🕌 English → Urdu Translator",
+    description="This is my First fine-tuned mBART model to translate English sentences into Urdu.",
+    theme="soft"
+)
+
+# Launch app
+app.launch(share=True)   # use share=True to get a public link accessible in Chrome
+