app.py

import streamlit as st
from interm import generate_clinical_content, generate_summary, generate_soap_note, fetch_pubmed_articles, save_as_text, save_as_pdf

# Set page configuration
st.set_page_config(page_title="Clinical AI Assistant", page_icon="🩺", layout="wide")

st.title("🩺 AI-Powered Clinical Intelligence Assistant")
st.write("AI-driven **clinical research, medical documentation, and PubMed insights**.")

# Tabs for different functionalities
tabs = st.tabs(["📝 Clinical Content", "📑 Research Summaries", "🩺 SOAP Notes", "🔍 PubMed Research", "📊 Medical Reports"])

# Tab 1: Generate Clinical Content
with tabs[0]:
    st.header("Generate Medical Articles & Patient Education")
    
    prompt = st.text_area("Enter a Medical Topic (e.g., AI in Radiology, Hypertension Management):")
    target_audience = st.selectbox("Select Audience:", ["Clinicians", "Patients", "Researchers"])

    if st.button("Generate Content"):
        if prompt:
            with st.spinner("Generating medical content..."):
                result = generate_clinical_content(prompt, target_audience)
                st.session_state["medical_content"] = result
                st.subheader("Generated Medical Content")
                st.write(result)
        else:
            st.warning("Please enter a medical topic.")

# Tab 2: Summarize Research
with tabs[1]:
    st.header("Summarize Clinical Trials & Medical Research")

    if "medical_content" in st.session_state:
        if st.button("Generate Summary"):
            with st.spinner("Summarizing medical research..."):
                summary = generate_summary(st.session_state["medical_content"])
                st.session_state["research_summary"] = summary
                st.subheader("Clinical Summary")
                st.markdown(summary)
    else:
        st.warning("Generate content in Tab 1 first.")

# Tab 3: SOAP Notes Generator
with tabs[2]:
    st.header("Generate SOAP Notes for Patient Consultations")

    symptoms = st.text_area("Enter Symptoms (e.g., fever, cough, chest pain):")
    patient_history = st.text_area("Brief Patient History:")

    if st.button("Generate SOAP Note"):
        if symptoms:
            with st.spinner("Generating SOAP Note..."):
                soap_note = generate_soap_note(symptoms, patient_history)
                st.session_state["soap_note"] = soap_note
                st.subheader("Generated SOAP Note")
                st.code(soap_note, language="text")
        else:
            st.warning("Please enter patient symptoms.")

# Tab 4: PubMed Research Integration
with tabs[3]:
    st.header("Fetch Latest Research from PubMed")

    query = st.text_input("Enter a medical keyword (e.g., COVID-19, AI in Oncology, Diabetes):")

    if st.button("Fetch PubMed Articles"):
        if query:
            with st.spinner("Retrieving PubMed articles..."):
                articles = fetch_pubmed_articles(query)
                st.session_state["pubmed_results"] = articles
                for article in articles:
                    st.subheader(article["title"])
                    st.write(f"**Authors:** {article['authors']}")
                    st.write(f"**Abstract:** {article['abstract']}")
                    st.write(f"[Read More]({article['url']})")
        else:
            st.warning("Please enter a medical topic.")

# Tab 5: Download Clinical Reports
with tabs[4]:
    st.header("Download Clinical Reports")

    if "soap_note" in st.session_state:
        report_content = st.session_state["soap_note"]
        text_file_path, text_filename = save_as_text(report_content, "Medical_Report.txt")
        pdf_file_path, pdf_filename = save_as_pdf(report_content, "Medical_Report.pdf")

        with open(text_file_path, "rb") as file:
            st.download_button("Download Report as TXT", data=file, file_name=text_filename, mime="text/plain")

        with open(pdf_file_path, "rb") as file:
            st.download_button("Download Report as PDF", data=file, file_name=pdf_filename, mime="application/pdf")