Create app.py

app.py

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+import streamlit as st
+import torch
+import pickle
+from transformers import MarianMTModel, MarianTokenizer
+
+# Load the trained model
+with open("nmt_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ar-en")
+
+# Streamlit UI
+st.title("Arabic to English Translator")
+st.write("Enter Arabic text and get the English translation.")
+
+arabic_text = st.text_area("Enter Arabic Text:")
+
+if st.button("Translate"):
+    if arabic_text:
+        # Tokenize and translate
+        inputs = tokenizer(arabic_text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+        with torch.no_grad():
+            translated_ids = model.generate(**inputs)
+        translated_text = tokenizer.batch_decode(translated_ids, skip_special_tokens=True)[0]
+        
+        st.subheader("Translated English Text:")
+        st.write(translated_text)
+    else:
+        st.warning("Please enter Arabic text.")