Create app.py

app.py

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+import streamlit as st
+from transformers import MarianMTModel, MarianTokenizer
+
+# Function to load the translation model and tokenizer
+@st.cache_resource
+def load_model_and_tokenizer():
+    model_name = "Helsinki-NLP/opus-mt-en-ROMANCE"  # Example: English to Romance languages (like Spanish, French, etc.)
+    model = MarianMTModel.from_pretrained(model_name)
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+# Function to translate text
+def translate_text(text, model, tokenizer, src_lang, tgt_lang):
+    # Prepare the text for translation
+    translation_input = tokenizer(text, return_tensors="pt", padding=True)
+    
+    # Translate text
+    translated = model.generate(**translation_input)
+    
+    # Decode the translated text
+    translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+    return translated_text
+
+# Streamlit UI components
+st.title("Language Translation App")
+st.write("Translate text between multiple languages using the Helsinki-NLP translation models.")
+
+# Load model and tokenizer
+model, tokenizer = load_model_and_tokenizer()
+
+# Language selection for input and output
+available_languages = ["en", "es", "fr", "de", "it", "pt", "ro", "nl", "pl", "ca"]
+input_lang = st.selectbox("Select input language", available_languages)
+output_lang = st.selectbox("Select output language", available_languages)
+
+# Text input
+text_to_translate = st.text_area("Enter text to translate:")
+
+# Perform translation if text is entered
+if text_to_translate:
+    if input_lang != output_lang:
+        translated_text = translate_text(text_to_translate, model, tokenizer, input_lang, output_lang)
+        st.write("Translated Text:")
+        st.write(translated_text)
+    else:
+        st.warning("Input and output languages cannot be the same.")