Create app.py

app.py

@@ -0,0 +1,33 @@
+import streamlit as st
+from transformers import MarianMTModel, MarianTokenizer
+
+# Load the model and tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "Helsinki-NLP/opus-mt-en-ur"
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    model = MarianMTModel.from_pretrained(model_name)
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+def translate_text(text, model, tokenizer):
+    inputs = tokenizer.encode(text, return_tensors="pt", truncation=True)
+    outputs = model.generate(inputs, max_length=512, num_beams=5, early_stopping=True)
+    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return translated_text
+
+# Streamlit UI
+st.title("English to Urdu Translation")
+
+# Input text from the user
+text_to_translate = st.text_area("Enter English text to translate:")
+
+if st.button("Translate"):
+    if text_to_translate.strip():
+        with st.spinner("Translating..."):
+            translated_text = translate_text(text_to_translate, model, tokenizer)
+        st.success("Translation completed!")
+        st.markdown(f"### Translated Text:\n{translated_text}")
+    else:
+        st.error("Please enter some text to translate.")