Create app.py

app.py

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+# Install necessary libraries (run this once)
+!pip install transformers gradio
+
+# Now import them
+from transformers import MarianMTModel, MarianTokenizer
+import gradio as gr
+
+# Define the models
+models = {
+    "English to Urdu": {
+        "model_name": "Helsinki-NLP/opus-mt-en-ur"
+    },
+    "Urdu to English": {
+        "model_name": "Helsinki-NLP/opus-mt-ur-en"
+    }
+}
+
+# Load models and tokenizers
+loaded_models = {}
+for direction, info in models.items():
+    tokenizer = MarianTokenizer.from_pretrained(info["model_name"])
+    model = MarianMTModel.from_pretrained(info["model_name"])
+    loaded_models[direction] = (tokenizer, model)
+
+# Define the translation function
+def translate_text(text, direction):
+    tokenizer, model = loaded_models[direction]
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    translated = model.generate(**inputs, max_length=512)
+    output = tokenizer.decode(translated[0], skip_special_tokens=True)
+    return output
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=translate_text,
+    inputs=[
+        gr.Textbox(label="Enter text here", placeholder="Type your English or Urdu text..."),
+        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 pretrained models.",
+    theme="default"
+)
+
+# Launch the app
+iface.launch()