Create app.py

app.py

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+import gradio as gr
+from transformers import MarianMTModel, MarianTokenizer
+
+# Load English → Urdu model
+en_ur_model_name = 'Helsinki-NLP/opus-mt-en-ur'
+en_ur_tokenizer = MarianTokenizer.from_pretrained(en_ur_model_name)
+en_ur_model = MarianMTModel.from_pretrained(en_ur_model_name)
+
+# Load Urdu → English model
+ur_en_model_name = 'Helsinki-NLP/opus-mt-ur-en'
+ur_en_tokenizer = MarianTokenizer.from_pretrained(ur_en_model_name)
+ur_en_model = MarianMTModel.from_pretrained(ur_en_model_name)
+
+# Define translation function
+def translate_text(text, direction):
+    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)
+    translated = model.generate(**inputs)
+    return tokenizer.decode(translated[0], skip_special_tokens=True)
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=translate_text,
+    inputs=[
+        gr.Textbox(label="Enter Text", placeholder="Type text here..."),
+        gr.Radio(["English to Urdu", "Urdu to English"], label="Translation Direction")
+    ],
+    outputs=gr.Textbox(label="Translated Text"),
+    title="English ↔ Urdu Translator Chatbot",
+    description="Translate between English and Urdu using pre-trained models from Hugging Face."
+)
+
+# Launch the interface
+iface.launch()