app.py

import streamlit as st
import os

from config import (
    OPENAI_API_KEY,
    OPENAI_DEFAULT_MODEL,
    MAX_PUBMED_RESULTS
)
from pubmed_rag import (
    search_pubmed, fetch_pubmed_abstracts, chunk_and_summarize,
    upsert_documents, semantic_search
)
from models import chat_with_openai
from image_pipeline import analyze_medical_image

###############################################################################
#                               STREAMLIT SETUP                               #
###############################################################################
st.set_page_config(page_title="Advanced Medical AI", layout="wide")

def main():
    st.title("Advanced Medical AI: Multi-Modal RAG & Image Diagnostics")

    st.markdown("""
    **Features**:
    1. **PubMed RAG**: Retrieve and summarize medical literature, store in a vector DB, 
       and use advanced semantic search for context. 
    2. **LLM Q&A**: Leverage OpenAI for final question-answering with RAG context.
    3. **Medical Image Analysis**: Use `HuggingFaceTB/SmolVLM-500M-Instruct` for diagnostic insights.
    4. **Multi-Lingual & Extended Triage**: Placeholder expansions for real-time translation or advanced triage logic.
    5. **Production-Ready**: Modular, concurrent, disclaimers, and synergy across tasks.
    """)

    menu = ["PubMed RAG Q&A", "Medical Image Analysis", "Semantic Search (Vector DB)"]
    choice = st.sidebar.selectbox("Select Task", menu)

    if choice == "PubMed RAG Q&A":
        pubmed_rag_qna()
    elif choice == "Medical Image Analysis":
        medical_image_analysis()
    else:
        vector_db_search_ui()

    st.markdown("---")
    st.markdown("""
    **Disclaimer**: This is an **advanced demonstration** for educational or research purposes only. 
    Always consult a qualified healthcare professional for personal medical decisions.
    """)

def pubmed_rag_qna():
    st.subheader("PubMed Retrieval-Augmented Q&A")
    query = st.text_area(
        "Ask a medical question (e.g., 'What are the latest treatments for type 2 diabetes?'):",
        height=100
    )
    max_art = st.slider("Number of PubMed Articles to Retrieve", 1, 10, 5)

    if st.button("Search & Summarize"):
        if not query.strip():
            st.warning("Please enter a query.")
            return

        with st.spinner("Searching PubMed..."):
            pmids = search_pubmed(query, max_art)
        
        if not pmids:
            st.error("No articles found. Try another query.")
            return

        with st.spinner("Fetching and Summarizing..."):
            raw_abstracts = fetch_pubmed_abstracts(pmids)
            # Summarize each
            summarized = {}
            for pmid, text in raw_abstracts.items():
                if text.startswith("Error"):
                    summarized[pmid] = text
                else:
                    summary = chunk_and_summarize(text)
                    summarized[pmid] = summary
        
        st.subheader("Summaries")
        for i, (pmid, summary) in enumerate(summarized.items(), start=1):
            st.markdown(f"**[Ref{i}] PMID {pmid}**")
            st.write(summary)

        # Upsert into vector DB
        upsert_documents(summarized)  # store raw or summarized texts

        # Build system prompt
        system_prompt = "You are an advanced medical assistant with the following references:\n"
        for i, (pmid, summary) in enumerate(summarized.items(), start=1):
            system_prompt += f"[Ref{i}] PMID {pmid}: {summary}\n"
        system_prompt += "\nUsing these references, provide an evidence-based answer."

        with st.spinner("Generating final answer..."):
            final_answer = chat_with_openai(system_prompt, query)
        st.subheader("Final Answer")
        st.write(final_answer)

def 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 is not None:
        st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
        if st.button("Analyze Image"):
            with st.spinner("Analyzing..."):
                result = analyze_medical_image(uploaded_file)
            st.subheader("Diagnostic Insight")
            st.write(result)

def vector_db_search_ui():
    st.subheader("Semantic Search in Vector DB")
    user_query = st.text_input("Enter a query to find relevant documents", "")
    top_k = st.slider("Number of results", 1, 10, 3)
    if st.button("Search"):
        if not user_query.strip():
            st.warning("Please enter a query.")
            return
        with st.spinner("Performing semantic search..."):
            results = semantic_search(user_query, top_k=top_k)
        st.subheader("Search Results")
        for i, doc in enumerate(results, start=1):
            st.markdown(f"**Result {i}** - PMID {doc['pmid']} (Distance: {doc['score']:.4f})")
            st.write(doc["text"])
            st.write("---")

if __name__ == "__main__":
    main()