Create app.py

app.py

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+# ✅ 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)
+
+# ✅ Step 4: Translation pipeline
+translator = pipeline("translation", model=model, tokenizer=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)
+        direction = "Urdu → English"
+    else:
+        # English to Urdu
+        result = translator(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")
+
+    translate_btn.click(fn=translate_text, inputs=input_text, outputs=output_text)
+
+# ✅ Step 7: Launch in Colab
+demo.launch(share=True)