Create app.py

app.py

@@ -0,0 +1,31 @@
+import streamlit as st
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+# Load the HealthScribe Clinical Note Generator model and tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "har1/HealthScribe-Clinical_Note_Generator"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+st.title("HealthScribe Clinical Note Generator")
+st.write("Generate clinical notes based on input text.")
+
+# Input section
+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 = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
+        outputs = model.generate(inputs["input_ids"], max_length=512, num_beams=5, early_stopping=True)
+        generated_note = 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.")