Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, MarianMTModel, MarianTokenizer
+
+# Load models and tokenizers
+@st.cache_resource
+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
+
+@st.cache_resource
+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.")