Create app.py

app.py

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+import streamlit as st
+from transformers import MarianMTModel, MarianTokenizer
+
+# Define a dictionary to map language names to model identifiers
+models = {
+    'French': 'Helsinki-NLP/opus-mt-en-fr',
+    'Spanish': 'Helsinki-NLP/opus-mt-en-es',
+    'German': 'Helsinki-NLP/opus-mt-en-de',
+    'Italian': 'Helsinki-NLP/opus-mt-en-it',
+    'Urdu': 'Helsinki-NLP/opus-mt-en-ur',
+    'Arabic': 'Helsinki-NLP/opus-mt-en-ar', 
+    # Add more language models if needed
+}
+
+def load_model(model_name):
+    """Load the model and tokenizer based on the selected model name."""
+    model = MarianMTModel.from_pretrained(model_name)
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+def translate_text(text, model, tokenizer):
+    """Translate text using the provided model and tokenizer."""
+    inputs = tokenizer.encode(text, return_tensors="pt")
+    translated = model.generate(inputs)
+    translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+    return translated_text
+
+def main():
+    st.title("Multilingual Translator")
+
+    # User input for English text
+    text_to_translate = st.text_area("Enter text in English:")
+
+    # Language options
+    selected_language = st.selectbox("Select target language:", list(models.keys()))
+
+    if st.button("Translate"):
+        if text_to_translate:
+            # Load the selected model
+            model_name = models[selected_language]
+            model, tokenizer = load_model(model_name)
+            
+            translated_text = translate_text(text_to_translate, model, tokenizer)
+            st.write(f"**Translation in {selected_language}:**")
+            st.write(translated_text)
+        else:
+            st.warning("Please enter text to translate.")
+
+if __name__ == "__main__":
+    main()