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import streamlit as st
from pubmed_rag import search_pubmed, fetch_pubmed_abstracts, summarize_text
from image_pipeline import analyze_medical_image
from models import chat_with_openai
st.set_page_config(page_title="Advanced Medical AI", layout="wide")
def main():
st.title("Advanced Medical AI")
st.sidebar.title("Features")
task = st.sidebar.selectbox("Choose a task:", ["PubMed Q&A", "Medical Image Analysis"])
if task == "PubMed Q&A":
st.subheader("PubMed Question Answering")
query = st.text_input("Enter your medical question:", "What are the latest treatments for diabetes?")
max_results = st.slider("Number of PubMed articles to retrieve:", 1, 10, 5)
if st.button("Run Query"):
with st.spinner("Searching PubMed..."):
pmids = search_pubmed(query, max_results)
if not pmids:
st.error("No results found. Try another query.")
return
with st.spinner("Fetching and summarizing abstracts..."):
abstracts = fetch_pubmed_abstracts(pmids)
summaries = {pmid: summarize_text(abstract) for pmid, abstract in abstracts.items()}
st.subheader("PubMed Summaries")
for pmid, summary in summaries.items():
st.write(f"**PMID {pmid}**: {summary}")
system_message = "You are a medical assistant with access to PubMed summaries."
user_message = query
with st.spinner("Generating answer..."):
answer = chat_with_openai(system_message, user_message)
st.subheader("AI-Powered Answer")
st.write(answer)
elif task == "Medical Image Analysis":
st.subheader("Medical Image Analysis")
uploaded_file = st.file_uploader("Upload a medical image (PNG/JPG):", type=["png", "jpg", "jpeg"])
if uploaded_file:
st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
with st.spinner("Analyzing image..."):
result = analyze_medical_image(uploaded_file)
st.subheader("Diagnostic Insight")
st.write(result)
if __name__ == "__main__":
main() |