views/lab_admin.py

"""Lab Administrator persona.

Features:
- Lab turnaround time analytics
- Specimen volume tracking
- Positivity rate monitoring
- AI Diagnostics Lab (Vision, Audio)
- Administrative OCR form digitizer
- Lab Intelligence chatbot
"""

import os
import streamlit as st
import plotly.express as px

from data.synthetic import get_lab_results, get_polypharmacy_alerts
from services.diagnostics import analyze_wound_image, analyze_respiratory_audio, process_ocr_form
from components.chatbot import render_chatbot

# Path to sample assets
_ASSETS_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "samples")


def render():
    """Render the Lab Admin dashboard."""
    st.title("Laboratory Intelligence Center")

    lab_data = get_lab_results()

    # Top KPI row
    avg_tat = lab_data["TAT_Hours"].mean()
    total_specimens = lab_data["Specimens_Processed"].sum()
    avg_positivity = lab_data["Positivity_Rate"].mean()
    total_critical = lab_data["Critical_Values_Reported"].sum()
    total_rejected = lab_data["Rejected_Specimens"].sum()

    c1, c2, c3, c4, c5 = st.columns(5)
    with c1:
        st.metric("Avg TAT", f"{avg_tat:.1f} hrs", "-12%")
    with c2:
        st.metric("Specimens (30d)", f"{total_specimens:,}")
    with c3:
        st.metric("Avg Positivity", f"{avg_positivity:.1%}")
    with c4:
        st.metric("Critical Values", total_critical)
    with c5:
        st.metric("Rejected", total_rejected, delta_color="inverse")

    st.divider()

    tab_analytics, tab_medintel, tab_vision, tab_audio, tab_ocr, tab_chat = st.tabs([
        "Lab Analytics", "💊 Medication Intelligence",
        "Wound Vision AI", "Respiratory Audio AI", "Form Digitizer (OCR)", "🔬 Lab Intelligence",
    ])

    # --- TAB: LAB ANALYTICS ---
    with tab_analytics:
        col1, col2 = st.columns(2)
        with col1:
            fig_tat = px.line(
                lab_data, x="Date", y="TAT_Hours",
                title="Lab Result Turnaround Time Trend",
                template="plotly_white",
            )
            fig_tat.add_hline(y=4.0, line_dash="dash", line_color="orange", annotation_text="SLA Target: 4h")
            fig_tat.update_traces(line_color="#3b82f6")
            st.plotly_chart(fig_tat, use_container_width=True)

        with col2:
            fig_pos = px.bar(
                lab_data, x="Date", y="Positivity_Rate",
                title="Infection Positivity Rate",
                template="plotly_white",
                color="Positivity_Rate",
                color_continuous_scale="OrRd",
            )
            st.plotly_chart(fig_pos, use_container_width=True)

        col3, col4 = st.columns(2)
        with col3:
            fig_spec = px.area(
                lab_data, x="Date", y="Specimens_Processed",
                title="Daily Specimen Volume",
                template="plotly_white",
            )
            fig_spec.update_traces(line_color="#10b981", fillcolor="rgba(16,185,129,0.1)")
            st.plotly_chart(fig_spec, use_container_width=True)

        with col4:
            fig_crit = px.bar(
                lab_data, x="Date", y="Critical_Values_Reported",
                title="Critical Values Reported",
                template="plotly_white",
                color="Critical_Values_Reported",
                color_continuous_scale="Reds",
            )
            st.plotly_chart(fig_crit, use_container_width=True)

    # --- TAB: MEDICATION INTELLIGENCE ---
    with tab_medintel:
        st.subheader("Medication Interaction Intelligence")
        st.caption(
            "AI-powered pharmaceutical surveillance across all residents. "
            "Detects polypharmacy risks, duplicate classes, and drug-lab correlations."
        )

        med_alerts = get_polypharmacy_alerts()

        # Summary metrics
        mi1, mi2, mi3 = st.columns(3)
        with mi1:
            st.metric("Active Alerts", len(med_alerts))
        with mi2:
            high_count = sum(1 for a in med_alerts if a["severity"] == "High")
            st.metric("High Severity", high_count, delta_color="inverse")
        with mi3:
            total_meds = sum(len(a["medications_involved"]) for a in med_alerts)
            st.metric("Medications Flagged", total_meds)

        st.divider()

        # Alert detail cards
        for alert in med_alerts:
            severity_color = {"High": "#ef4444", "Moderate": "#f59e0b", "Low": "#10b981"}.get(
                alert["severity"], "#64748b"
            )
            with st.expander(
                f"{'🔴' if alert['severity'] == 'High' else '🟡'} "
                f"{alert['resident']} — {alert['alert_type']}",
                expanded=(alert["severity"] == "High"),
            ):
                st.markdown(
                    f"""<div style="border-left: 4px solid {severity_color};
                    padding: 12px 16px; background: white; border-radius: 0 8px 8px 0;">
                    <strong>{alert['alert_type']}</strong> — Severity: {alert['severity']}
                    — Confidence: {alert['confidence']:.0%}
                    </div>""",
                    unsafe_allow_html=True,
                )
                st.markdown(alert["description"])

                st.markdown("**Medications Involved:**")
                for med in alert["medications_involved"]:
                    st.markdown(f"- 💊 `{med}`")

                lab_col, rec_col = st.columns(2)
                with lab_col:
                    st.markdown("**Lab Correlation:**")
                    if "diuretic" in alert["description"].lower():
                        st.markdown(
                            "- **K+:** Monitor q24h (risk: hypo/hyperkalemia)\n"
                            "- **Na+:** Monitor q24h (risk: hyponatremia)\n"
                            "- **Cr/BUN:** Check within 24h (renal function)\n"
                            "- **Orthostatic BP:** TID checks"
                        )
                    elif "metformin" in alert["description"].lower():
                        st.markdown(
                            "- **eGFR:** Trending 62 → 48 over 3 months\n"
                            "- **Lactate:** Check if eGFR < 45\n"
                            "- **HbA1c:** Due for recheck\n"
                            "- **BMP:** Next scheduled draw"
                        )
                    else:
                        st.markdown(
                            "- **CBC:** Monitor for bleeding (bruising)\n"
                            "- **PT/INR:** If on anticoagulant\n"
                            "- **Fall risk assessment:** Ongoing"
                        )
                with rec_col:
                    st.markdown("**Recommendation:**")
                    st.info(alert["recommendation"])

    # --- TAB: WOUND VISION AI ---
    with tab_vision:
        st.subheader("Wound / Skin Vision Analysis")
        st.caption("Upload a wound photograph for AI-powered staging and assessment.")

        # Sample demo option
        use_sample_wound = st.checkbox(
            "Use built-in sample image (Sacral Pressure Ulcer - Demo)",
            key="use_sample_wound",
        )

        if use_sample_wound:
            sample_path = os.path.join(_ASSETS_DIR, "sample_wound.jpg")
            if os.path.exists(sample_path):
                st.image(sample_path, width=400, caption="Sample: Sacral Pressure Ulcer (Synthetic Demo)")
                if st.button("Run Vision Diagnostics", type="primary", key="btn_vision_sample"):
                    with st.spinner("Vision agent analyzing image..."):
                        result = analyze_wound_image(None)
                        st.success("Analysis complete!")
                        st.markdown(result["summary"])
                        with st.expander("Detailed Results (JSON)"):
                            st.json(result["result"])
                        with st.expander("FHIR Observation (auto-generated)"):
                            st.code(
                                '{\n'
                                '  "resourceType": "Observation",\n'
                                '  "status": "final",\n'
                                '  "category": [{"coding": [{"code": "exam", "display": "Exam"}]}],\n'
                                f'  "code": {{"text": "Wound Assessment - {result["result"]["stage"]}"}},\n'
                                f'  "bodySite": {{"text": "{result["result"]["location"]}"}},\n'
                                f'  "valueString": "{result["result"]["size_cm"]} cm, {result["result"]["stage"]}",\n'
                                '  "interpretation": [{"text": "AI Vision Analysis"}],\n'
                                f'  "note": [{{"text": "{result["result"]["recommendation"]}"}}]\n'
                                '}',
                                language="json",
                            )
            else:
                st.warning("Sample file not found. Please upload an image instead.")
        else:
            img_file = st.file_uploader("Upload Wound Photo", type=["jpg", "jpeg", "png"], key="wound_upload")
            if img_file:
                st.image(img_file, width=350, caption="Uploaded Image")
                if st.button("Run Vision Diagnostics", type="primary", key="btn_vision"):
                    with st.spinner("Vision agent analyzing image..."):
                        result = analyze_wound_image(img_file)
                        st.success("Analysis complete!")
                        st.markdown(result["summary"])
                        with st.expander("Detailed Results (JSON)"):
                            st.json(result["result"])
                        with st.expander("FHIR Observation (auto-generated)"):
                            st.code(
                                '{\n'
                                '  "resourceType": "Observation",\n'
                                '  "status": "final",\n'
                                '  "category": [{"coding": [{"code": "exam", "display": "Exam"}]}],\n'
                                f'  "code": {{"text": "Wound Assessment - {result["result"]["stage"]}"}},\n'
                                f'  "bodySite": {{"text": "{result["result"]["location"]}"}},\n'
                                f'  "valueString": "{result["result"]["size_cm"]} cm, {result["result"]["stage"]}",\n'
                                '  "interpretation": [{"text": "AI Vision Analysis"}],\n'
                                f'  "note": [{{"text": "{result["result"]["recommendation"]}"}}]\n'
                                '}',
                                language="json",
                            )

    # --- TAB: RESPIRATORY AUDIO AI ---
    with tab_audio:
        st.subheader("Respiratory Bioacoustic Analysis")
        st.caption("Upload a lung sound or cough recording for AI-powered respiratory assessment.")

        use_sample_audio = st.checkbox(
            "Use built-in sample audio (Simulated Respiratory Recording - Demo)",
            key="use_sample_audio",
        )

        if use_sample_audio:
            sample_path = os.path.join(_ASSETS_DIR, "sample_respiratory.wav")
            if os.path.exists(sample_path):
                st.audio(sample_path, format="audio/wav")
                st.caption("Sample: Simulated lung sounds with cough events at ~1.2s and ~2.8s")
                if st.button("Run Audio Bioacoustics", type="primary", key="btn_audio_sample"):
                    with st.spinner("Audio agent analyzing recording..."):
                        result = analyze_respiratory_audio(None)
                        st.success("Analysis complete!")
                        st.markdown(result["summary"])

                        # Visual analysis display
                        col_a, col_b = st.columns(2)
                        with col_a:
                            st.markdown("**Detection Summary:**")
                            st.markdown(
                                f"- **Cough Rate:** {result['result']['cough_rate_per_hr']} coughs/hour\n"
                                f"- **Cough Type:** {result['result']['cough_type']}\n"
                                f"- **Trend:** {result['result']['trend']}\n"
                                f"- **Pneumonia Risk:** {result['result']['pneumonia_risk_pct']}%"
                            )
                        with col_b:
                            st.markdown("**AI Recommendation:**")
                            st.info(result["result"]["recommendation"])

                        with st.expander("Detailed Results (JSON)"):
                            st.json(result["result"])
            else:
                st.warning("Sample file not found. Please upload an audio file instead.")
        else:
            audio_file = st.file_uploader("Upload Audio Recording", type=["mp3", "wav"], key="audio_upload")
            if audio_file:
                st.audio(audio_file)
                if st.button("Run Audio Bioacoustics", type="primary", key="btn_audio"):
                    with st.spinner("Audio agent analyzing recording..."):
                        result = analyze_respiratory_audio(audio_file)
                        st.success("Analysis complete!")
                        st.markdown(result["summary"])

                        col_a, col_b = st.columns(2)
                        with col_a:
                            st.markdown("**Detection Summary:**")
                            st.markdown(
                                f"- **Cough Rate:** {result['result']['cough_rate_per_hr']} coughs/hour\n"
                                f"- **Cough Type:** {result['result']['cough_type']}\n"
                                f"- **Trend:** {result['result']['trend']}\n"
                                f"- **Pneumonia Risk:** {result['result']['pneumonia_risk_pct']}%"
                            )
                        with col_b:
                            st.markdown("**AI Recommendation:**")
                            st.info(result["result"]["recommendation"])

                        with st.expander("Detailed Results (JSON)"):
                            st.json(result["result"])

    # --- TAB: ADMINISTRATIVE OCR ---
    with tab_ocr:
        st.subheader("Administrative Form Digitizer")
        st.caption("Convert paper orders, lab reports, and invoices into structured HL7/FHIR-ready data.")

        use_sample_form = st.checkbox(
            "Use built-in sample form (Lab Order - Margaret Chen - Demo)",
            key="use_sample_form",
        )

        if use_sample_form:
            sample_path = os.path.join(_ASSETS_DIR, "sample_lab_order.jpg")
            if os.path.exists(sample_path):
                st.image(sample_path, width=450, caption="Sample: Laboratory Order Form (Synthetic Demo)")
                if st.button("Digitize Form", type="primary", key="btn_ocr_sample"):
                    with st.spinner("OCR agent processing document..."):
                        result = process_ocr_form(None)
                        st.success(
                            f"Form digitized successfully. Type: **{result['result']['form_type']}**. "
                            f"Confidence: **{result['result'].get('confidence', 'N/A')}**. "
                            f"Output format: HL7 {result['hl7_version']} / FHIR R4."
                        )

                        col_o1, col_o2 = st.columns(2)
                        with col_o1:
                            st.markdown("**Extracted Fields:**")
                            st.json(result["result"])
                        with col_o2:
                            st.markdown("**FHIR ServiceRequest (auto-generated):**")
                            st.code(
                                '{\n'
                                '  "resourceType": "ServiceRequest",\n'
                                '  "status": "active",\n'
                                '  "intent": "order",\n'
                                f'  "subject": {{"reference": "Patient/{result["result"].get("mrn", "MC-402")}"}},\n'
                                f'  "requester": {{"display": "{result["result"].get("ordering_physician", "Dr. Sarah Kim")}"}},\n'
                                '  "code": {"text": "Laboratory Tests"},\n'
                                '  "priority": "routine",\n'
                                '  "authoredOn": "2026-03-26"\n'
                                '}',
                                language="json",
                            )

                        st.markdown("**HL7 v2.5.1 Message (auto-generated):**")
                        st.code(
                            "MSH|^~\\&|SENIORCARE|LAB|EHR|FACILITY|20260326||ORM^O01|MSG001|P|2.5.1\n"
                            f"PID|1||{result['result'].get('mrn', 'MC-402')}||{result['result'].get('patient', 'Chen^Margaret')}||\n"
                            "ORC|NW|ORD001||||||20260326|||Dr. Sarah Kim\n"
                            "OBR|1|ORD001||CBC^Complete Blood Count|||20260326\n"
                            "OBR|2|ORD001||CMP^Comprehensive Metabolic Panel|||20260326\n"
                            "OBR|3|ORD001||UA^Urinalysis with Culture|||20260326",
                            language="text",
                        )
            else:
                st.warning("Sample file not found. Please upload a form instead.")
        else:
            admin_doc = st.file_uploader("Upload Form (Image/PDF)", type=["jpg", "jpeg", "png", "pdf"], key="ocr_upload")
            if admin_doc:
                if admin_doc.type and "image" in admin_doc.type:
                    st.image(admin_doc, width=300, caption="Uploaded Form")
                else:
                    st.info(f"Uploaded: {admin_doc.name}")

                if st.button("Digitize Form", type="primary", key="btn_ocr"):
                    with st.spinner("OCR agent processing document..."):
                        result = process_ocr_form(admin_doc)
                        st.success(
                            f"Form digitized successfully. Type: **{result['result']['form_type']}**. "
                            f"Confidence: **{result['result'].get('confidence', 'N/A')}**. "
                            f"Output format: HL7 {result['hl7_version']} / FHIR R4."
                        )
                        with st.expander("Extracted Data"):
                            st.json(result["result"])

    # --- TAB: LAB INTELLIGENCE CHATBOT ---
    with tab_chat:
        st.subheader("Lab Intelligence Assistant")
        st.caption(
            "AI-powered lab Q&A. Ask about turnaround times, specimen tracking, "
            "critical values, infection surveillance, and diagnostic AI modules."
        )
        render_chatbot(persona="lab_admin")