99d1d2b4f5
- Remove protocol_summarizer_webapp submodule reference - Add all webapp files as regular files to enable proper PR creation - Includes Streamlit app, documentation, and configuration files
122 lines
5.1 KiB
Python
122 lines
5.1 KiB
Python
import os
|
|
from dotenv import load_dotenv
|
|
import streamlit as st
|
|
import requests
|
|
from openai import OpenAI
|
|
|
|
load_dotenv()
|
|
|
|
st.title("Protocol Summarizer")
|
|
|
|
st.markdown("""
|
|
Search for clinical trials by keyword, select a study, and generate a protocol summary using an LLM.
|
|
""")
|
|
|
|
# Search input
|
|
|
|
# Show results only after user presses Enter
|
|
with st.form(key="search_form"):
|
|
query = st.text_input("Enter a disease, study title, or keyword:")
|
|
max_results = st.slider("Number of results", 1, 20, 5)
|
|
submitted = st.form_submit_button("Search")
|
|
|
|
@st.cache_data(show_spinner=False)
|
|
def search_clinical_trials(query, max_results=5):
|
|
if not query:
|
|
return []
|
|
url = f"https://clinicaltrials.gov/api/v2/studies?query.term={query}&pageSize={max_results}&format=json"
|
|
resp = requests.get(url)
|
|
studies = []
|
|
if resp.status_code == 200:
|
|
data = resp.json()
|
|
for study in data.get('studies', []):
|
|
nct = study.get('protocolSection', {}).get('identificationModule', {}).get('nctId', 'N/A')
|
|
title = study.get('protocolSection', {}).get('identificationModule', {}).get('officialTitle', 'N/A')
|
|
studies.append({'nct': nct, 'title': title})
|
|
return studies
|
|
|
|
results = search_clinical_trials(query, max_results) if query else []
|
|
|
|
if results:
|
|
st.subheader("Search Results")
|
|
for i, study in enumerate(results):
|
|
st.markdown(f"**{i+1}. {study['title']}** (NCT: {study['nct']})")
|
|
selected = st.number_input("Select study number to summarize", min_value=1, max_value=len(results), value=1)
|
|
selected_study = results[selected-1]
|
|
st.markdown(f"### Selected Study\n**{selected_study['title']}** (NCT: {selected_study['nct']})")
|
|
if st.button("Summarize Protocol"):
|
|
# Fetch the brief summary for the selected study
|
|
nct_id = selected_study['nct']
|
|
|
|
# Use the V2 API which we know works reliably
|
|
url = f"https://clinicaltrials.gov/api/v2/studies/{nct_id}?format=json"
|
|
with st.spinner("Fetching study details..."):
|
|
resp = requests.get(url)
|
|
brief = ""
|
|
|
|
if resp.status_code == 200:
|
|
try:
|
|
data = resp.json()
|
|
|
|
# V2 API has protocolSection at the root level
|
|
if 'protocolSection' in data:
|
|
desc_mod = data.get('protocolSection', {}).get('descriptionModule', {})
|
|
brief = desc_mod.get('briefSummary', '')
|
|
|
|
# If briefSummary is empty, try detailedDescription
|
|
if not brief:
|
|
brief = desc_mod.get('detailedDescription', '')
|
|
except Exception as e:
|
|
st.error(f"Error parsing study data: {e}")
|
|
|
|
# If API fails, try HTML scraping as a fallback
|
|
if not brief and resp.status_code != 200:
|
|
st.warning(f"API returned status code {resp.status_code}. Trying alternative method...")
|
|
html_url = f"https://clinicaltrials.gov/ct2/show/{nct_id}"
|
|
html_resp = requests.get(html_url)
|
|
|
|
if "Brief Summary:" in html_resp.text:
|
|
start = html_resp.text.find("Brief Summary:") + 15
|
|
excerpt = html_resp.text[start:start+1000]
|
|
|
|
# Clean up HTML
|
|
import re
|
|
excerpt = re.sub('<[^<]+?>', ' ', excerpt)
|
|
excerpt = re.sub('\\s+', ' ', excerpt)
|
|
brief = excerpt.strip()
|
|
|
|
if not brief:
|
|
st.error("No brief summary or detailed description found for this study.")
|
|
st.stop()
|
|
|
|
# Now we have the brief summary, send it to the LLM
|
|
openai = OpenAI()
|
|
def user_prompt_for_protocol_brief(brief_text):
|
|
return (
|
|
"Extract the following details from the clinical trial brief summary in markdown format with clear section headings (e.g., ## Study Design, ## Population, etc.):\n"
|
|
"- Study design\n"
|
|
"- Population\n"
|
|
"- Interventions\n"
|
|
"- Primary and secondary endpoints\n"
|
|
"- Study duration\n\n"
|
|
f"Brief summary text:\n{brief_text}"
|
|
)
|
|
system_prompt = "You are a clinical research assistant. Extract and list the requested protocol details in markdown format with clear section headings."
|
|
messages = [
|
|
{"role": "system", "content": system_prompt},
|
|
{"role": "user", "content": user_prompt_for_protocol_brief(brief)}
|
|
]
|
|
with st.spinner("Summarizing with LLM..."):
|
|
try:
|
|
response = openai.chat.completions.create(
|
|
model="gpt-4o-mini",
|
|
messages=messages
|
|
)
|
|
summary = response.choices[0].message.content
|
|
st.markdown(summary)
|
|
except Exception as e:
|
|
st.error(f"LLM call failed: {e}")
|
|
else:
|
|
if query:
|
|
st.info("No results found. Try a different keyword.")
|