.

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import pandas as pd
 from transformers import pipeline, AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
 from peft import PeftModel, PeftConfig
+import gradio as gr
 gr.Blocks(theme= 'pseudolab/huggingface-korea-theme')
 
 #Note this should be used always in compliance with applicable laws and regulations if used with real patient data.
@@ -34,14 +35,14 @@ def prepare_context(data):
 
     return input_ids
 
-if uploaded_file is not None:
+def fn(uploaded_file) -> str:
     data = pd.read_csv(uploaded_file)
-    st.write(data)
+    ret = ""
 
     # Generate text based on the context
     context = prepare_context(data)
     generated_text = pipeline('text-generation', model=model)(context)[0]['generated_text']
-    st.write(generated_text)
+    ret += generated_text
 
     # Internally prompt the model to data analyze the EHR patient data
     prompt = "You are an Electronic Health Records analyst with nursing school training. Please analyze patient data that you are provided here. Give an organized, step-by-step, formatted health records analysis. You will always be truthful and if you do nont know the answer say you do not know."
@@ -52,9 +53,13 @@ if uploaded_file is not None:
 
         # Generate text based on the prompt
         generated_text = pipeline('text-generation', model=model)(input_ids=input_ids)[0]['generated_text']
-        st.write(generated_text)
-    else:
-        st.write("Please enter patient data")
-        
-else:
-    st.write("No file uploaded")
+        ret += generated_text
+
+    return ret
+
+
+demo = gr.Interface(fn=fn, inputs="file", outputs="text")
+
+
+if __name__ == "__main__":
+    demo.launch(show_api=False)