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| import streamlit as st | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, MarianMTModel, MarianTokenizer | |
| # Load models and tokenizers | |
| def load_healthscribe_model(): | |
| model_name = "har1/HealthScribe-Clinical_Note_Generator" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| return model, tokenizer | |
| def load_translation_model(model_name): | |
| model = MarianMTModel.from_pretrained(model_name) | |
| tokenizer = MarianTokenizer.from_pretrained(model_name) | |
| return model, tokenizer | |
| # Initialize models | |
| healthscribe_model, healthscribe_tokenizer = load_healthscribe_model() | |
| # Language selection options | |
| language_options = { | |
| "English to French": ("en", "fr"), | |
| "French to English": ("fr", "en"), | |
| "English to Spanish": ("en", "es"), | |
| "Spanish to English": ("es", "en"), | |
| "English to German": ("en", "de"), | |
| "German to English": ("de", "en"), | |
| "English to Italian": ("en", "it"), | |
| "Italian to English": ("it", "en"), | |
| } | |
| # Streamlit UI setup | |
| st.title("Multifunctional Text Processing App") | |
| st.write("This app can generate clinical notes or translate text between languages.") | |
| # Choose task | |
| task = st.selectbox("Select a task:", ["Generate Clinical Note", "Translate Text"]) | |
| if task == "Generate Clinical Note": | |
| st.subheader("Clinical Note Generator") | |
| input_text = st.text_area("Enter patient information or medical notes:", height=200) | |
| if st.button("Generate Clinical Note"): | |
| if input_text.strip(): | |
| # Tokenize and generate | |
| inputs = healthscribe_tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True) | |
| outputs = healthscribe_model.generate(inputs["input_ids"], max_length=512, num_beams=5, early_stopping=True) | |
| generated_note = healthscribe_tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Display the result | |
| st.subheader("Generated Clinical Note") | |
| st.write(generated_note) | |
| else: | |
| st.warning("Please enter some text to generate a clinical note.") | |
| elif task == "Translate Text": | |
| st.subheader("Translation Tool") | |
| language_pair = st.selectbox("Select language pair", list(language_options.keys())) | |
| src_lang, tgt_lang = language_options[language_pair] | |
| # Load the corresponding translation model and tokenizer | |
| model_name = f"Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}" | |
| translation_model, translation_tokenizer = load_translation_model(model_name) | |
| # Input text to translate | |
| text = st.text_area("Enter text to translate:") | |
| if st.button("Translate"): | |
| if text.strip(): | |
| # Prepare the input for the model | |
| inputs = translation_tokenizer(text, return_tensors="pt", padding=True, truncation=True) | |
| # Generate translation | |
| translation = translation_model.generate(**inputs) | |
| # Decode the output | |
| translated_text = translation_tokenizer.decode(translation[0], skip_special_tokens=True) | |
| # Display translation | |
| st.write("**Original Text**:", text) | |
| st.write("**Translated Text**:", translated_text) | |
| else: | |
| st.warning("Please enter some text to translate.") | |