Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import gradio as gr
+
+# Load English to Urdu model
+en_ur_model_name = "Helsinki-NLP/opus-mt-en-ur"
+en_ur_tokenizer = AutoTokenizer.from_pretrained(en_ur_model_name)
+en_ur_model = AutoModelForSeq2SeqLM.from_pretrained(en_ur_model_name)
+
+# Load Urdu to English model
+ur_en_model_name = "Helsinki-NLP/opus-mt-ur-en"
+ur_en_tokenizer = AutoTokenizer.from_pretrained(ur_en_model_name)
+ur_en_model = AutoModelForSeq2SeqLM.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":
+        inputs = en_ur_tokenizer.encode(text, return_tensors="pt")
+        outputs = en_ur_model.generate(inputs, max_length=512, num_beams=4, early_stopping=True)
+        result = en_ur_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    else:  # Urdu to English
+        inputs = ur_en_tokenizer.encode(text, return_tensors="pt")
+        outputs = ur_en_model.generate(inputs, max_length=512, num_beams=4, early_stopping=True)
+        result = ur_en_tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return result
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.Textbox(label="Enter text", lines=3),
+        gr.Radio(["English to Urdu", "Urdu to English"], label="Translation Direction")
+    ],
+    outputs=gr.Textbox(label="Translated text"),
+    title="English ↔ Urdu Translator",
+    description="Translate between English and Urdu using Helsinki-NLP models"
+)
+
+interface.launch()