src/pages/analysis.py

"""
Human+ — Page 2: AI Analysis
Refactored: loading states & biomarker rows pakai Streamlit native.
"""

import streamlit as st
from ui.components import (
    render_loading_steps,
    render_progress_bar,
    render_section_label,
    render_error_state,
    render_biomarker_summary_banner,
    render_biomarker_row,
    render_success_banner,
    ANALYSIS_STEPS,
)
from ui.chart_components import (
    render_gauge_grid,
    render_biomarker_bar_chart,
    render_status_donut,
)


def render_analysis_page():
    if not st.session_state.get("uploaded_pdf_bytes"):
        render_error_state(
            title="Tidak ada file PDF",
            message="Silakan kembali ke langkah pertama dan upload hasil lab Anda.",
        )
        if st.button("← Kembali ke Upload", use_container_width=True):
            st.session_state.step = 1
            st.rerun()
        return

    if st.session_state.get("extracted_biomarkers") is not None:
        _render_biomarker_results()
        return

    _run_analysis()


# ── Analysis runner ──────────────────────────────────────────────────

def _run_analysis():
    render_section_label("MENGANALISIS LAB ANDA", "⚡")

    status_ph   = st.empty()
    progress_ph = st.empty()
    steps_ph    = st.empty()

    def _update(step_idx: int, msg: str):
        with status_ph:
            st.caption(f"⚙️ {msg}...")
        with progress_ph:
            render_progress_bar((step_idx + 0.5) / len(ANALYSIS_STEPS))
        with steps_ph:
            render_loading_steps(ANALYSIS_STEPS, step_idx)

    # Step 0 — warm up vector store
    _update(0, ANALYSIS_STEPS[0])
    try:
        from core.rag_engine import get_vector_store
        get_vector_store()
    except Exception:
        pass

    # Step 1 — Gemini PDF extraction
    _update(1, ANALYSIS_STEPS[1])
    try:
        from core.pdf_processor import extract_biomarkers
        biomarkers = extract_biomarkers(st.session_state.uploaded_pdf_bytes)
    except Exception as e:
        _clear(steps_ph, status_ph, progress_ph)
        render_error_state(
            title="Gagal Menganalisis PDF",
            message=f"{e}\n\nPastikan PDF berisi hasil lab yang valid dan terbaca.",
            show_contact=True,
        )
        col1, col2 = st.columns(2)
        with col1:
            if st.button("← Upload Lagi", use_container_width=True):
                st.session_state.step = 1
                st.session_state.uploaded_pdf_bytes = None
                st.rerun()
        with col2:
            if st.button("🔄 Coba Lagi", type="primary", use_container_width=True):
                st.rerun()
        return

    # Step 2 & 3 — finalize
    _update(2, ANALYSIS_STEPS[2])
    import time; time.sleep(0.3)
    _update(3, ANALYSIS_STEPS[3])
    import time; time.sleep(0.3)

    st.session_state.extracted_biomarkers = biomarkers

    with progress_ph:
        render_progress_bar(1.0)
    with status_ph:
        st.caption("✅ Analisis selesai")

    import time; time.sleep(0.4)
    st.rerun()


def _clear(*placeholders):
    for ph in placeholders:
        ph.empty()


# ── Results renderer ─────────────────────────────────────────────────

def _render_biomarker_results():
    biomarkers = st.session_state.extracted_biomarkers

    if not biomarkers:
        render_error_state(
            title="Tidak ada biomarker terdeteksi",
            message="Coba upload file PDF yang lebih jelas atau format hasil lab standar.",
            show_contact=True,
        )
        if st.button("← Upload Ulang", use_container_width=True):
            for k in ["uploaded_pdf_bytes", "extracted_biomarkers", "generated_protocol"]:
                st.session_state[k] = None
            st.session_state.step = 1
            st.rerun()
        return

    optimal = sum(1 for b in biomarkers if b["status"] == "optimal")
    warning  = sum(1 for b in biomarkers if b["status"] == "warning")
    danger   = sum(1 for b in biomarkers if b["status"] == "danger")

    render_success_banner(
        title=f"{len(biomarkers)} biomarker berhasil dianalisis",
        subtitle="Dibandingkan dengan Human+ optimal ranges — bukan range lab standar",
    )
    render_biomarker_summary_banner(optimal, warning, danger)

    # ── Charts ────────────────────────────────────────────────────
    render_section_label("VISUALISASI BIOMARKER", "📊")

    col_donut, col_bar = st.columns([1, 2])
    with col_donut:
        render_status_donut(optimal, warning, danger)
    with col_bar:
        render_biomarker_bar_chart(biomarkers)

    render_section_label("GAUGE — MARKER PRIORITAS", "🎯")
    render_gauge_grid(biomarkers)

    # ── Biomarker rows ─────────────────────────────────────────────
    if danger:
        render_section_label(f"PERLU PERHATIAN — {danger} MARKER", "🔴")
        for b in biomarkers:
            if b["status"] == "danger":
                render_biomarker_row(b["name"], b["value"], b["unit"], b["status"], b.get("reference", ""))

    if warning:
        render_section_label(f"SUB-OPTIMAL — {warning} MARKER", "🟡")
        for b in biomarkers:
            if b["status"] == "warning":
                render_biomarker_row(b["name"], b["value"], b["unit"], b["status"], b.get("reference", ""))

    if optimal:
        render_section_label(f"SUDAH OPTIMAL — {optimal} MARKER", "🟢")
        for b in biomarkers:
            if b["status"] == "optimal":
                render_biomarker_row(b["name"], b["value"], b["unit"], b["status"], b.get("reference", ""))

    neutral = [b for b in biomarkers if b["status"] == "neutral"]
    if neutral:
        with st.expander(f"📊 {len(neutral)} marker lainnya"):
            for b in neutral:
                render_biomarker_row(b["name"], b["value"], b["unit"], "neutral", "")

    st.write("")
    if st.button("🔬 Generate Protocol Personal Saya", type="primary", use_container_width=True):
        st.session_state.step = 3
        st.rerun()

    if st.button("← Upload Lab Lain", use_container_width=True):
        for k in ["uploaded_pdf_bytes", "extracted_biomarkers", "generated_protocol", "pdf_filename"]:
            st.session_state[k] = None
        st.session_state.step = 1
        st.rerun()