Create app.py

app.py

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+from transformers import MarianMTModel, MarianTokenizer
+import gradio as gr
+import torch
+
+# Model names
+en_ur_model_name = "Helsinki-NLP/opus-mt-en-ur"
+ur_en_model_name = "Helsinki-NLP/opus-mt-ur-en"
+
+# Load English to Urdu model
+en_ur_tokenizer = MarianTokenizer.from_pretrained(en_ur_model_name)
+en_ur_model = MarianMTModel.from_pretrained(en_ur_model_name)
+
+# Load Urdu to English model
+ur_en_tokenizer = MarianTokenizer.from_pretrained(ur_en_model_name)
+ur_en_model = MarianMTModel.from_pretrained(ur_en_model_name)
+
+# Translation Function
+def translate(text, direction):
+    if not text.strip():
+        return "Please enter some text."
+
+    if direction == "English to Urdu":
+        tokenizer, model = en_ur_tokenizer, en_ur_model
+    else:
+        tokenizer, model = ur_en_tokenizer, ur_en_model
+
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        translated = model.generate(**inputs)
+    output = tokenizer.decode(translated[0], skip_special_tokens=True)
+    return output
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.Textbox(label="Enter Text", lines=4, placeholder="Type your sentence here..."),
+        gr.Radio(["English to Urdu", "Urdu to English"], label="Select Translation Direction")
+    ],
+    outputs=gr.Textbox(label="Translated Text"),
+    title="English ↔ Urdu Translator",
+    description="Translate text between English and Urdu using Hugging Face MarianMT models."
+)
+
+iface.launch(share=True)  # Use share=True for Colab public link