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| import streamlit as st | |
| from transformers import MarianMTModel, MarianTokenizer | |
| # Path to your model files | |
| model_path = "lz039/translation-ft-de-bg" | |
| tokenizer = MarianTokenizer.from_pretrained(model_path) | |
| model = MarianMTModel.from_pretrained(model_path) | |
| # Check if loaded successfully | |
| print("Model and tokenizer loaded!") | |
| # Streamlit app | |
| st.title("Translation App") | |
| st.write("Translate text from German to Bulgarian using your custom model!") | |
| # Input text box | |
| src_text = st.text_area("Enter text to translate:", placeholder="Type text in German here...") | |
| # Button to trigger translation | |
| if st.button("Translate"): | |
| if src_text.strip(): # Ensure there's input text | |
| try: | |
| # Tokenize and generate translation | |
| inputs = tokenizer(src_text, return_tensors="pt", padding=True) | |
| translated = model.generate(**inputs) | |
| # Decode the translation | |
| result = tokenizer.decode(translated[0], skip_special_tokens=True) | |
| # Display the translation | |
| st.success("Translation:") | |
| st.write(result) | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| else: | |
| st.warning("Please enter some text to translate!") |