Update app.py

app.py

@@ -1,45 +1,37 @@
+!pip install transformers gradio torch --quiet
+
 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)
+    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")
+        gr.Textbox(label="Enter Text"),
+        gr.Radio(["English to Urdu", "Urdu to English"], label="Translation Direction")
     ],
-    outputs=gr.Textbox(label="Translated Text"),
+    outputs="text",
     title="English ↔ Urdu Translator",
-    description="Translate text between English and Urdu using Hugging Face MarianMT models."
+    description="Translate text between English and Urdu."
 )
 
-iface.launch(share=True)  # Use share=True for Colab public link
+iface.launch(share=True)