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import requests | |
import streamlit as st | |
import os | |
API_URL = 'https://qe55p8afio98s0u3.us-east-1.aws.endpoints.huggingface.cloud' | |
API_KEY = os.getenv('API_KEY') | |
headers = { | |
"Authorization": f"Bearer {API_KEY}", | |
"Content-Type": "application/json" | |
} | |
def query(payload): | |
response = requests.post(API_URL, headers=headers, json=payload) | |
st.markdown(response.json()) | |
return response.json() | |
def get_output(prompt): | |
return query({"inputs": prompt}) | |
def main(): | |
st.title("Medical Transcription Summarizer") | |
example_input = st.text_input("Enter your example text:") | |
if st.button("Summarize with Variation 1"): | |
prompt = f"Write instructions to teach anyone to write a discharge plan. List the entities, features and relationships to CCDA and FHIR objects in boldface. {example_input}" | |
output = get_output(prompt) | |
st.markdown(f"**Output:** {output}") | |
if st.button("Summarize with Variation 2"): | |
prompt = f"Provide a summary of the medical transcription. Highlight the important entities, features, and relationships to CCDA and FHIR objects. {example_input}" | |
output = get_output(prompt) | |
st.markdown(f"**Output:** {output}") | |
if __name__ == "__main__": | |
main() |