Update app.py

app.py

@@ -1,46 +1,34 @@
-# ✅ Step 1: Install required libraries
-!pip install transformers gradio --quiet
-
-# ✅ Step 2: Import required modules
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 import gradio as gr
 
-# ✅ Step 3: Load model and tokenizer (Multilingual translation model)
-model_name = "Helsinki-NLP/opus-mt-en-ur"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+# Load models
+en_ur_model_name = "Helsinki-NLP/opus-mt-en-ur"
+ur_en_model_name = "Helsinki-NLP/opus-mt-ur-en"
+
+en_ur_tokenizer = AutoTokenizer.from_pretrained(en_ur_model_name)
+en_ur_model = AutoModelForSeq2SeqLM.from_pretrained(en_ur_model_name)
+translator_en_ur = pipeline("translation", model=en_ur_model, tokenizer=en_ur_tokenizer)
 
-# ✅ Step 4: Translation pipeline
-translator = pipeline("translation", model=model, tokenizer=tokenizer)
+ur_en_tokenizer = AutoTokenizer.from_pretrained(ur_en_model_name)
+ur_en_model = AutoModelForSeq2SeqLM.from_pretrained(ur_en_model_name)
+translator_ur_en = pipeline("translation", model=ur_en_model, tokenizer=ur_en_tokenizer)
 
-# ✅ Step 5: Translation function
 def translate_text(text):
-    # Detect Urdu using Unicode range
     contains_urdu = any('\u0600' <= ch <= '\u06FF' for ch in text)
-    
     if contains_urdu:
-        # Urdu to English
-        ur_to_en_model = "Helsinki-NLP/opus-mt-ur-en"
-        tokenizer_ur_en = AutoTokenizer.from_pretrained(ur_to_en_model)
-        model_ur_en = AutoModelForSeq2SeqLM.from_pretrained(ur_to_en_model)
-        ur_to_en_translator = pipeline("translation", model=model_ur_en, tokenizer=tokenizer_ur_en)
-        result = ur_to_en_translator(text, max_length=100)
+        result = translator_ur_en(text, max_length=100)
         direction = "Urdu → English"
     else:
-        # English to Urdu
-        result = translator(text, max_length=100)
+        result = translator_en_ur(text, max_length=100)
         direction = "English → Urdu"
-
     return f"🔁 Translation ({direction}):\n\n" + result[0]['translation_text']
 
-# ✅ Step 6: Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 🌐 English ↔ Urdu Translator\nUsing Hugging Face models")
-    input_text = gr.Textbox(label="Enter text (English or Urdu)", placeholder="Type something...")
-    output_text = gr.Textbox(label="Translated Text", interactive=False)
-    translate_btn = gr.Button("Translate")
+    gr.Markdown("## 🌐 English ↔ Urdu Translator using Hugging Face")
+    input_text = gr.Textbox(label="Enter Text (English or Urdu)", placeholder="Type here...")
+    output_text = gr.Textbox(label="Translation Output", interactive=False)
+    translate_button = gr.Button("Translate")
+    translate_button.click(fn=translate_text, inputs=input_text, outputs=output_text)
 
-    translate_btn.click(fn=translate_text, inputs=input_text, outputs=output_text)
+demo.launch()
 
-# ✅ Step 7: Launch in Colab
-demo.launch(share=True)