Create main.py

main.py

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+import streamlit as st
+from transformers import MarianMTModel, MarianTokenizer
+
+# Define available languages with MarianMT models
+LANGUAGES = {
+ 'Spanish': 'es',
+ 'French': 'fr',
+ 'German': 'de',
+ 'Chinese': 'zh',
+ 'Hindi': 'hi',
+ 'Arabic': 'ar',
+ 'Japanese': 'ja',
+ 'Russian': 'ru',
+ 'Italian': 'it',
+ 'Portuguese': 'pt',
+ # Add more languages if needed
+}
+
+# Function to load the model based on the selected language
+@st.cache_resource
+def load_model(src_lang='en', tgt_lang='es'):
+ model_name = f'Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}'
+ model = MarianMTModel.from_pretrained(model_name)
+ tokenizer = MarianTokenizer.from_pretrained(model_name)
+ return model, tokenizer
+
+# Function to translate text
+def translate_text(model, tokenizer, text):
+ inputs = tokenizer.encode(text, return_tensors='pt', truncation=True, padding=True)
+ translated = model.generate(inputs, max_length=512, num_beams=5, early_stopping=True)
+ translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+ return translated_text
+
+# Streamlit app
+st.title("Language Translator")
+st.write("Translate English text to any language using Hugging Face models.")
+
+# Input text
+text = st.text_area("Enter text in English to translate:")
+
+# Language selection
+language = st.selectbox("Choose target language", list(LANGUAGES.keys()))
+
+if st.button("Translate"):
+ if text:
+ # Load model and tokenizer based on selected language
+ tgt_lang = LANGUAGES[language]
+ model, tokenizer = load_model('en', tgt_lang)
+ 
+ # Perform translation
+ translated_text = translate_text(model, tokenizer, text)
+ 
+ # Display the translation
+ st.write(f"**Translated text ({language}):**")
+ st.write(translated_text)
+ else:
+ st.write("Please enter text to translate.")