app.py

from __future__ import annotations

import uuid
from datetime import date, datetime
from pathlib import Path

import pandas as pd
import streamlit as st


APP_DIR = Path(__file__).resolve().parent
DATA_DIR = APP_DIR / "data"
CSV_PATH = DATA_DIR / "prospects.csv"

SEGMENTS = [
    "NGO",
    "CSR",
    "Hospital",
    "Government",
    "Occupational Health",
    "Research Institution",
]

FIT_SCORES = ["High", "Medium", "Low"]
STAGES = [
    "Prospect Identified",
    "Contacted",
    "Discovery",
    "Proposal",
    "Negotiation",
    "Won",
    "Lost",
]

CSV_COLUMNS = [
    "id",
    "organisation_name",
    "segment",
    "state",
    "website",
    "contact_person",
    "email",
    "disease_focus",
    "fit_score",
    "stage",
    "deal_value",
    "probability",
    "next_follow_up_date",
    "notes",
    "created_at",
    "updated_at",
]


def configure_page() -> None:
    st.set_page_config(
        page_title="Respyr BD OS",
        page_icon="🫁",
        layout="wide",
        initial_sidebar_state="expanded",
    )
    st.markdown(
        """
        <style>
            @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap');

            /* ════ BASE ════ */
            html, body, .stApp {
                font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif !important;
                background-color: #f5f7fa !important;
                color: #0f172a !important;
                -webkit-font-smoothing: antialiased;
                -moz-osx-font-smoothing: grayscale;
            }

            /* ════ SIDEBAR — LIGHT ════ */
            [data-testid="stSidebar"] {
                background: #ffffff !important;
                border-right: 1px solid #e8edf2 !important;
            }
            [data-testid="stSidebar"] * {
                font-family: 'Inter', sans-serif !important;
            }
            [data-testid="stSidebar"] p,
            [data-testid="stSidebar"] span {
                color: #64748b !important;
            }
            [data-testid="stSidebarContent"] hr {
                border: none !important;
                border-top: 1px solid #e8edf2 !important;
                margin: 6px 0 !important;
            }
            [data-testid="stSidebar"] label {
                display: block;
                padding: 9px 12px !important;
                border-radius: 8px !important;
                color: #334155 !important;
                font-size: 0.875rem !important;
                font-weight: 500 !important;
                transition: background 0.12s ease, color 0.12s ease !important;
                cursor: pointer !important;
                line-height: 1.4 !important;
            }
            [data-testid="stSidebar"] label:hover {
                background: #f1f5f9 !important;
                color: #0f172a !important;
            }
            /* Hide the radio dot */
            [data-testid="stSidebar"] [data-baseweb="radio"] svg { display: none !important; }
            [data-testid="stSidebar"] [data-baseweb="radio"] > div:first-child { display: none !important; }

            /* ════ LAYOUT ════ */
            .block-container {
                padding-top: 2.25rem !important;
                padding-bottom: 4rem !important;
                max-width: 1400px !important;
                margin: 0 auto !important;
            }

            /* ════ TYPOGRAPHY ════ */
            h1 {
                font-size: 1.875rem !important;
                font-weight: 800 !important;
                letter-spacing: -0.04em !important;
                color: #0f172a !important;
                line-height: 1.15 !important;
                margin-bottom: 0.15rem !important;
            }
            h2 {
                font-size: 1.1rem !important;
                font-weight: 700 !important;
                letter-spacing: -0.025em !important;
                color: #0f172a !important;
                line-height: 1.3 !important;
            }
            h3 {
                font-size: 0.95rem !important;
                font-weight: 700 !important;
                letter-spacing: -0.015em !important;
                color: #1e293b !important;
            }
            p, li, span {
                color: #334155 !important;
            }

            /* ════ METRIC TILES ════ */
            div[data-testid="stMetric"] {
                background: #ffffff;
                border: 1px solid #e8edf2;
                border-top: 3px solid #0d9488;
                border-radius: 14px;
                padding: 20px 24px 22px;
                box-shadow: 0 1px 3px rgba(15,23,42,0.04), 0 4px 16px rgba(15,23,42,0.04);
                transition: transform 0.18s cubic-bezier(.4,0,.2,1), box-shadow 0.18s cubic-bezier(.4,0,.2,1);
            }
            div[data-testid="stMetric"]:hover {
                transform: translateY(-3px);
                box-shadow: 0 6px 12px rgba(15,23,42,0.07), 0 16px 40px rgba(15,23,42,0.09);
            }
            div[data-testid="stMetric"] label {
                font-size: 0.7rem !important;
                font-weight: 700 !important;
                text-transform: uppercase !important;
                letter-spacing: 0.09em !important;
                color: #94a3b8 !important;
            }
            div[data-testid="stMetric"] [data-testid="stMetricValue"] {
                font-size: 1.85rem !important;
                font-weight: 800 !important;
                color: #0f172a !important;
                letter-spacing: -0.04em !important;
                line-height: 1.1 !important;
            }

            /* ════ CARDS ════ */
            .respyr-card {
                background: #ffffff;
                border: 1px solid #e8edf2;
                border-radius: 16px;
                padding: 24px 28px;
                margin-bottom: 20px;
                box-shadow: 0 1px 3px rgba(15,23,42,0.04), 0 4px 16px rgba(15,23,42,0.03);
            }

            .respyr-hero {
                background: linear-gradient(135deg, #0d9488 0%, #0f766e 45%, #134e4a 100%);
                border-radius: 18px;
                padding: 32px 38px;
                margin-bottom: 28px;
                position: relative;
                overflow: hidden;
                border: none;
                box-shadow: 0 4px 24px rgba(13,148,136,0.3), 0 1px 4px rgba(13,148,136,0.2);
            }
            .respyr-hero::before {
                content: "";
                position: absolute;
                width: 320px; height: 320px;
                top: -120px; right: -60px;
                background: radial-gradient(circle, rgba(255,255,255,0.12) 0%, transparent 60%);
                pointer-events: none;
            }
            .respyr-hero::after {
                content: "";
                position: absolute;
                width: 200px; height: 200px;
                bottom: -70px; left: 10%;
                background: radial-gradient(circle, rgba(255,255,255,0.07) 0%, transparent 60%);
                pointer-events: none;
            }
            .respyr-hero h2 {
                color: #ffffff !important;
                font-size: 1.45rem !important;
                font-weight: 700 !important;
                letter-spacing: -0.03em !important;
                margin: 0 0 0.45rem 0 !important;
                position: relative; z-index: 1;
            }
            .respyr-hero p {
                color: rgba(255,255,255,0.7) !important;
                margin: 0 !important;
                font-size: 0.875rem !important;
                position: relative; z-index: 1;
                line-height: 1.6 !important;
            }

            /* ════ CHARTS ════ */
            [data-testid="stArrowVegaLiteChart"] {
                background: #ffffff;
                border-radius: 14px;
                padding: 16px 12px 8px;
                border: 1px solid #e8edf2;
                box-shadow: 0 1px 3px rgba(15,23,42,0.04), 0 4px 12px rgba(15,23,42,0.03);
            }

            /* ════ FORM CARD ════ */
            [data-testid="stForm"] {
                background: #ffffff !important;
                border: 1px solid #e8edf2 !important;
                border-radius: 16px !important;
                padding: 24px 28px !important;
                box-shadow: 0 1px 3px rgba(15,23,42,0.04) !important;
            }

            /* ════ BUTTONS ════ */
            .stButton > button {
                border-radius: 10px !important;
                padding: 9px 20px !important;
                font-weight: 600 !important;
                font-size: 0.875rem !important;
                letter-spacing: 0.005em !important;
                font-family: 'Inter', sans-serif !important;
                transition: all 0.15s cubic-bezier(0.4,0,0.2,1) !important;
                border: 1px solid #e8edf2 !important;
                color: #334155 !important;
                background: #ffffff !important;
                box-shadow: 0 1px 2px rgba(15,23,42,0.05) !important;
            }
            .stButton > button[kind="primary"] {
                background: #0d9488 !important;
                border-color: #0d9488 !important;
                color: #ffffff !important;
                box-shadow: 0 1px 2px rgba(13,148,136,0.2), 0 4px 14px rgba(13,148,136,0.22) !important;
            }
            .stButton > button[kind="primary"]:hover {
                background: #0f766e !important;
                border-color: #0f766e !important;
                box-shadow: 0 2px 6px rgba(13,148,136,0.25), 0 10px 28px rgba(13,148,136,0.28) !important;
                transform: translateY(-1px) !important;
            }
            .stButton > button:not([kind="primary"]):hover {
                background: #f8fafc !important;
                border-color: #cbd5e1 !important;
                color: #0f172a !important;
            }

            /* ════ INPUTS ════ */
            .stTextInput input,
            .stTextArea textarea,
            .stNumberInput input {
                border-radius: 10px !important;
                border: 1px solid #e8edf2 !important;
                background: #ffffff !important;
                font-family: 'Inter', sans-serif !important;
                font-size: 0.875rem !important;
                color: #0f172a !important;
                box-shadow: 0 1px 2px rgba(15,23,42,0.04) !important;
                transition: border-color 0.15s, box-shadow 0.15s !important;
            }
            .stTextInput input:focus,
            .stTextArea textarea:focus,
            .stNumberInput input:focus {
                border-color: #0d9488 !important;
                box-shadow: 0 0 0 3px rgba(13,148,136,0.12), 0 1px 2px rgba(15,23,42,0.04) !important;
                outline: none !important;
            }
            .stTextInput label, .stTextArea label, .stNumberInput label,
            .stSelectbox label, .stSlider label, .stDateInput label,
            .stMultiSelect label {
                font-size: 0.8rem !important;
                font-weight: 600 !important;
                letter-spacing: 0.01em !important;
                color: #475569 !important;
            }

            /* ════ TABS ════ */
            .stTabs [data-baseweb="tab-list"] {
                gap: 3px !important;
                background: #eef2f7 !important;
                padding: 4px !important;
                border-radius: 12px !important;
                border: 1px solid #e8edf2 !important;
                width: fit-content !important;
                margin-bottom: 20px !important;
            }
            .stTabs [data-baseweb="tab"] {
                border-radius: 9px !important;
                font-weight: 600 !important;
                font-size: 0.84rem !important;
                padding: 8px 22px !important;
                color: #64748b !important;
                background: transparent !important;
                border: none !important;
                letter-spacing: 0.005em !important;
                transition: color 0.12s !important;
            }
            .stTabs [aria-selected="true"] {
                background: #ffffff !important;
                color: #0f172a !important;
                box-shadow: 0 1px 4px rgba(15,23,42,0.1), 0 0 0 1px rgba(15,23,42,0.06) !important;
            }
            .stTabs [data-baseweb="tab-highlight"],
            .stTabs [data-baseweb="tab-border"] { display: none !important; }

            /* ════ SELECT / MULTISELECT ════ */
            [data-baseweb="select"] > div:first-child {
                border-radius: 10px !important;
                border: 1px solid #e8edf2 !important;
                background: #ffffff !important;
                font-size: 0.875rem !important;
                font-family: 'Inter', sans-serif !important;
                box-shadow: 0 1px 2px rgba(15,23,42,0.04) !important;
            }

            /* ════ DATA TABLES ════ */
            [data-testid="stDataFrame"],
            [data-testid="stDataEditor"] {
                border-radius: 14px !important;
                overflow: hidden !important;
                border: 1px solid #e8edf2 !important;
                box-shadow: 0 1px 3px rgba(15,23,42,0.04), 0 4px 12px rgba(15,23,42,0.03) !important;
            }

            /* ════ MISC ════ */
            [data-testid="stAlert"] { border-radius: 12px !important; font-size: 0.875rem !important; }
            hr { border: none !important; border-top: 1px solid #e8edf2 !important; }
            .small-muted { color: #94a3b8 !important; font-size: 0.85rem !important; }
            [data-testid="stCaption"] { color: #94a3b8 !important; font-size: 0.8rem !important; }
            [data-testid="stDownloadButton"] button {
                border-radius: 10px !important;
                font-weight: 600 !important;
                font-size: 0.875rem !important;
                font-family: 'Inter', sans-serif !important;
            }
        </style>
        """,
        unsafe_allow_html=True,
    )


def ensure_data_file() -> None:
    DATA_DIR.mkdir(parents=True, exist_ok=True)
    if not CSV_PATH.exists():
        pd.DataFrame(columns=CSV_COLUMNS).to_csv(CSV_PATH, index=False)


def load_prospects() -> pd.DataFrame:
    ensure_data_file()
    try:
        df = pd.read_csv(CSV_PATH)
    except pd.errors.EmptyDataError:
        df = pd.DataFrame(columns=CSV_COLUMNS)

    for column in CSV_COLUMNS:
        if column not in df.columns:
            df[column] = ""

    df = df[CSV_COLUMNS].copy()
    df["deal_value"] = pd.to_numeric(df["deal_value"], errors="coerce").fillna(0.0)
    df["probability"] = pd.to_numeric(df["probability"], errors="coerce").fillna(0.0)
    df["next_follow_up_date"] = pd.to_datetime(
        df["next_follow_up_date"], errors="coerce"
    )
    return df


def save_prospects(df: pd.DataFrame) -> None:
    output = df.copy()
    output["next_follow_up_date"] = pd.to_datetime(
        output["next_follow_up_date"], errors="coerce"
    ).dt.strftime("%Y-%m-%d")
    output.to_csv(CSV_PATH, index=False)


def money(value: float) -> str:
    return f"₹{value:,.0f}"


def compact_money(value: float) -> str:
    if value >= 10_000_000:
        return f"₹{value / 10_000_000:.1f} Cr"
    if value >= 100_000:
        return f"₹{value / 100_000:.1f} L"
    if value >= 1_000:
        return f"₹{value / 1_000:.1f} K"
    return money(value)


def organisation_options(df: pd.DataFrame) -> list[str]:
    if df.empty:
        return []
    return sorted(df["organisation_name"].dropna().astype(str).unique().tolist())


def get_prospect_by_name(df: pd.DataFrame, organisation: str) -> dict:
    if not organisation or df.empty:
        return {}
    match = df[df["organisation_name"] == organisation]
    return match.iloc[0].to_dict() if not match.empty else {}


def build_outreach_email(
    segment: str,
    organisation: str,
    contact_person: str,
    disease_focus: str,
    state: str,
    sender_name: str,
    request: str,
) -> tuple[str, str]:
    contact = contact_person.strip() or "there"
    org = organisation.strip() or "your organisation"
    focus = disease_focus.strip() or "priority respiratory and metabolic conditions"
    location = state.strip() or "your target communities"

    context = {
        "NGO": (
            f"your work with underserved communities in {location}",
            "extend accessible screening without requiring a conventional laboratory setup",
            "explore a community screening pilot for your beneficiaries",
        ),
        "CSR": (
            f"{org}'s focus on measurable health and social impact",
            "create a scalable preventive-health programme with clear reach and outcome metrics",
            "explore a CSR-supported screening initiative in a priority geography",
        ),
        "Hospital": (
            f"your commitment to earlier diagnosis and stronger care pathways",
            "support high-throughput, non-invasive pre-screening and appropriate clinical referrals",
            "evaluate a pilot within an OPD, outreach camp, or preventive-health programme",
        ),
        "Government": (
            f"the public-health priorities being addressed in {location}",
            "enable population-level screening with a portable, non-invasive model",
            "discuss a limited pilot aligned with existing public-health infrastructure",
        ),
        "Occupational Health": (
            f"the need to protect workforce health while keeping screening simple",
            "offer convenient on-site screening with minimal disruption to operations",
            "consider a workforce screening pilot at one site",
        ),
        "Research Institution": (
            f"your work in translational and population-health research",
            "support evidence generation around breath biomarkers and real-world screening pathways",
            "explore a validation study or research collaboration",
        ),
    }

    research, reward, default_request = context[segment]
    request_line = request.strip() or default_request
    subject = f"Exploring a breath-based screening collaboration with {org}"
    body = f"""Dear {contact},

I have been following {research}. It is closely aligned with the problem Respyr is working to solve: making early health screening more accessible through breath-based, non-invasive technology.

Respyr is developing a portable screening approach for {focus}. A collaboration could help {reward}, while generating practical evidence on uptake, referrals, and programme outcomes.

Would you be open to a 25-minute conversation next week to {request_line}? I would be happy to share a concise pilot concept tailored to {org}.

Warm regards,
{sender_name.strip() or "Respyr Business Development"}
Respyr"""
    return subject, body


DISCOVERY_QUESTIONS = {
    "NGO": [
        "Which communities and geographies are your current health programmes serving?",
        "What conditions are you actively screening for or referring today?",
        "Where do beneficiaries face the greatest barriers to timely testing?",
        "How are screening camps or community-health activities currently delivered?",
        "What monthly or quarterly beneficiary volume could a pilot realistically reach?",
        "Who provides clinical confirmation and referral after an initial positive screen?",
        "Which outcome indicators matter most to your programme and funders?",
        "What approvals, local partners, or community mobilisation would be required?",
        "What budget source could support a pilot if the model is suitable?",
        "What would make a 60–90 day pilot successful from your perspective?",
    ],
    "CSR": [
        "What health themes and geographies are priorities in your current CSR portfolio?",
        "Which beneficiary group would create the strongest strategic fit?",
        "How do you select and monitor implementing partners?",
        "What measurable outputs and outcomes must be reported to leadership?",
        "Is the priority awareness, screening access, referral completion, or all three?",
        "What programme scale and budget range is realistic for an initial pilot?",
        "Which internal and implementation stakeholders need to approve the programme?",
        "Are there existing NGO, hospital, or government partners we should integrate with?",
        "What reporting cadence and evidence standards do you expect?",
        "What would enable the pilot to scale across sites or funding cycles?",
    ],
    "Hospital": [
        "Where in the patient journey would pre-screening create the most value?",
        "Which departments and patient cohorts should be prioritised?",
        "What is the current volume, cost, and turnaround time for relevant tests?",
        "How are at-risk patients identified today?",
        "Who would operate the screening workflow and review results?",
        "What confirmatory testing and referral pathway would follow a positive screen?",
        "Which clinical, operational, and commercial outcomes would justify adoption?",
        "What hospital approvals are needed for a pilot or evaluation?",
        "How should Respyr integrate with existing records and reporting?",
        "What pilot sample size and duration would be credible to your clinical team?",
    ],
    "Government": [
        "Which public-health priority and population should the programme address?",
        "What screening infrastructure and field workforce are already available?",
        "Which districts or facilities would be appropriate for a controlled pilot?",
        "What beneficiary volume and coverage targets would be expected?",
        "How are positive cases currently confirmed, referred, and tracked?",
        "Which programme indicators and reporting formats are mandatory?",
        "What technical, ethical, and administrative approvals would be required?",
        "How would procurement or an implementation partnership be structured?",
        "What evidence would be required before considering broader deployment?",
        "Which government, clinical, and implementation stakeholders should be involved?",
    ],
    "Occupational Health": [
        "Which workforce groups have the highest health or exposure risk?",
        "What annual or periodic medical examinations are currently conducted?",
        "Which conditions, symptoms, or absenteeism patterns are most concerning?",
        "How many employees and sites could be included in an initial pilot?",
        "What level of operational disruption is acceptable for on-site screening?",
        "How are employee consent, confidentiality, and referrals managed?",
        "Which metrics matter most: participation, risk detection, referrals, or productivity?",
        "Who owns the programme across HR, EHS, medical, and operations?",
        "What budget cycle and approval process applies?",
        "What result would justify expanding the programme to other sites?",
    ],
    "Research Institution": [
        "What research hypothesis or evidence gap would be most valuable to investigate?",
        "Which disease area, population, and clinical setting are most relevant?",
        "What reference standard and study design would you consider credible?",
        "What sample size, cohort access, and recruitment capacity are available?",
        "Which endpoints and performance measures should be primary?",
        "What ethics, regulatory, and data-governance approvals are required?",
        "How would samples, metadata, and follow-up outcomes be collected?",
        "What roles should each party play in protocol, operations, and analysis?",
        "What funding routes or grant opportunities could support the study?",
        "What is the expected approach to publications and intellectual property?",
    ],
}


def build_proposal(
    partner_name: str,
    segment: str,
    disease_focus: str,
    geography: str,
    target_population: str,
    population_size: int,
    duration_weeks: int,
    commercial_model: str,
    price_per_screen: float,
) -> str:
    partner = partner_name.strip() or "Partner Organisation"
    focus = disease_focus.strip() or "priority health conditions"
    place = geography.strip() or "the selected pilot geography"
    population = target_population.strip() or "eligible at-risk participants"

    segment_models = {
        "NGO": "community mobilisation through field teams and assisted screening camps",
        "CSR": "a sponsored programme delivered with local implementation and referral partners",
        "Hospital": "screening embedded into an OPD, preventive-health, or outreach workflow",
        "Government": "deployment through selected public facilities and field-health teams",
        "Occupational Health": "scheduled on-site screening integrated with workplace health processes",
        "Research Institution": "protocol-led participant recruitment, screening, and comparative evaluation",
    }
    model = segment_models[segment]

    if commercial_model == "Per-screen":
        commercial = (
            f"Indicative fee: {money(price_per_screen)} per completed screening. "
            f"For {population_size:,} participants, the indicative screening value is "
            f"{money(population_size * price_per_screen)}, excluding applicable taxes, "
            "travel, confirmatory diagnostics, and any site-specific infrastructure."
        )
    elif commercial_model == "Fixed pilot fee":
        commercial = (
            f"A fixed pilot fee will be agreed after finalising sites and scope. "
            f"A working estimate based on {population_size:,} screenings at "
            f"{money(price_per_screen)} per screening is "
            f"{money(population_size * price_per_screen)}."
        )
    else:
        commercial = (
            "The pilot may be jointly funded through partner contribution, grant support, "
            "or an implementation sponsor. Roles, in-kind contributions, and any per-screen "
            "charges will be defined in the final agreement."
        )

    return f"""# Pilot Concept Note

## Breath-based non-invasive screening collaboration
**Prepared for:** {partner}  
**Partner segment:** {segment}  
**Geography:** {place}  
**Proposed duration:** {duration_weeks} weeks

## 1. Problem statement
Delayed detection of {focus} can lead to poorer outcomes, higher treatment costs, and avoidable pressure on health systems. Conventional diagnostic pathways may be limited by cost, access, infrastructure, time, or low screening uptake—especially for dispersed or underserved populations.

## 2. Respyr solution
Respyr proposes a portable, breath-based, non-invasive screening approach designed to make early risk identification simpler and more accessible. The pilot will assess operational feasibility, participant uptake, screening outputs, and the effectiveness of referral pathways. Screening results are intended to support risk stratification and referral; they do not replace confirmatory clinical diagnosis.

## 3. Target population
The pilot will target **{population}**, with an indicative reach of **{population_size:,} participants** in {place}. Final eligibility criteria will be agreed with {partner}.

## 4. Screening model
The delivery model will use {model}. The workflow will cover participant registration and consent, breath-based screening, result communication, referral of at-risk participants, and programme reporting.

## 5. Implementation plan
1. **Co-design:** Confirm objectives, sites, cohort, roles, consent, referral pathways, and success metrics.
2. **Readiness:** Train operators, prepare devices and materials, test data capture, and complete site approvals.
3. **Deployment:** Mobilise participants and conduct screening under agreed operating procedures.
4. **Referral:** Direct at-risk participants to the partner's nominated clinical or diagnostic pathway.
5. **Review:** Analyse programme data, document learning, and agree recommendations for scale.

## 6. Indicative timeline
- **Weeks 1–2:** Programme design, approvals, site selection, and workflow finalisation
- **Weeks 3–4:** Training, mobilisation, device readiness, and dry run
- **Weeks 5–{max(duration_weeks - 2, 6)}:** Screening delivery and referral tracking
- **Final 2 weeks:** Analysis, partner review, and final report

## 7. Deliverables
- Agreed pilot protocol and implementation plan
- Operator training and deployment support
- Screening of up to {population_size:,} eligible participants
- Secure programme-level data capture and monitoring
- Referral list for participants requiring follow-up, subject to consent
- Interim progress update and final pilot report
- Scale-up recommendations based on operational and outcome data

## 8. Expected outcomes
- Improved access to convenient, non-invasive screening
- Identification of participants who may benefit from confirmatory evaluation
- Evidence on participation, throughput, field feasibility, and referral completion
- A practical understanding of cost and resource requirements
- A decision framework for expansion to additional sites or populations

## 9. Commercial model
{commercial}

## 10. Proposed next step
Respyr and {partner} will hold a working session to finalise the use case, clinical referral pathway, target cohort, governance requirements, success metrics, and commercial scope before signing a pilot agreement.
"""


_NAV = {
    "📊   Dashboard": "Dashboard",
    "🗂   Prospect CRM": "Prospect CRM",
    "✉️   Outreach Generator": "Outreach Generator",
    "🔍   Discovery Questions": "Discovery Questions",
    "📄   Proposal Generator": "Proposal Generator",
}


def render_sidebar() -> str:
    with st.sidebar:
        st.markdown(
            """
            <div style="padding:0.5rem 0 1.25rem 0;">
              <div style="font-size:1.3rem;font-weight:800;color:#0f172a;
                          letter-spacing:-0.03em;line-height:1.2;">
                🫁 Respyr
              </div>
              <div style="font-size:0.68rem;font-weight:600;color:#94a3b8;
                          text-transform:uppercase;letter-spacing:0.13em;margin-top:5px;">
                Business Development OS
              </div>
            </div>
            """,
            unsafe_allow_html=True,
        )
        st.divider()
        nav_label = st.radio(
            "Workspace",
            list(_NAV.keys()),
            label_visibility="collapsed",
        )
        st.divider()
        st.markdown(
            '<p style="font-size:0.75rem;color:#94a3b8;margin:0;">Built for a focused healthtech BD team.</p>',
            unsafe_allow_html=True,
        )
    return _NAV[nav_label]


def render_dashboard(df: pd.DataFrame) -> None:
    st.title("Sales Forecast Dashboard")
    st.caption("A live view of pipeline health, follow-ups, and likely revenue.")

    st.markdown(
        """
        <div class="respyr-hero">
            <h2>Pipeline at a glance</h2>
            <p>Track open deals, follow-up timing, and forecast revenue across your full funnel.</p>
        </div>
        """,
        unsafe_allow_html=True,
    )

    active = df[~df["stage"].isin(["Lost"])].copy()
    open_pipeline = active[~active["stage"].isin(["Won"])].copy()
    total_pipeline = open_pipeline["deal_value"].sum()
    weighted_pipeline = (
        open_pipeline["deal_value"] * open_pipeline["probability"] / 100
    ).sum()
    won_value = df.loc[df["stage"] == "Won", "deal_value"].sum()
    today = pd.Timestamp(date.today())
    overdue = open_pipeline[
        open_pipeline["next_follow_up_date"].notna()
        & (open_pipeline["next_follow_up_date"] < today)
    ]
    high_priority = open_pipeline[
        (open_pipeline["fit_score"] == "High")
        & (open_pipeline["probability"] >= 40)
    ]

    cols = st.columns(5)
    cols[0].metric("Open pipeline", compact_money(total_pipeline))
    cols[1].metric("Weighted pipeline", compact_money(weighted_pipeline))
    cols[2].metric("Won value", compact_money(won_value))
    cols[3].metric("Overdue follow-ups", len(overdue))
    cols[4].metric("High-priority leads", len(high_priority))

    if df.empty:
        st.info("Add your first prospect in the CRM to activate the dashboard.")
        return

    chart_left, chart_right = st.columns(2)
    with chart_left:
        st.subheader("Deals by stage")
        stage_counts = (
            df["stage"].value_counts().reindex(STAGES, fill_value=0).rename("Deals")
        )
        st.bar_chart(stage_counts, color="#0d9488")

    with chart_right:
        st.subheader("Expected revenue by month")
        forecast = open_pipeline[open_pipeline["next_follow_up_date"].notna()].copy()
        if forecast.empty:
            st.caption("Add follow-up dates to produce a monthly forecast.")
        else:
            forecast["month"] = forecast["next_follow_up_date"].dt.to_period("M").astype(str)
            forecast["weighted_value"] = (
                forecast["deal_value"] * forecast["probability"] / 100
            )
            monthly = (
                forecast.groupby("month", as_index=False)["weighted_value"]
                .sum()
                .set_index("month")
            )
            st.bar_chart(monthly, color="#f97316")

    list_left, list_right = st.columns(2)
    with list_left:
        st.subheader("Overdue follow-ups")
        if overdue.empty:
            st.success("No overdue follow-ups.")
        else:
            overdue_view = overdue[
                [
                    "organisation_name",
                    "contact_person",
                    "stage",
                    "next_follow_up_date",
                    "deal_value",
                ]
            ].sort_values("next_follow_up_date")
            st.dataframe(
                overdue_view,
                width="stretch",
                hide_index=True,
                column_config={
                    "organisation_name": "Organisation",
                    "contact_person": "Contact",
                    "stage": "Stage",
                    "next_follow_up_date": st.column_config.DateColumn("Follow-up"),
                    "deal_value": st.column_config.NumberColumn(
                        "Deal value", format="₹ %.0f"
                    ),
                },
            )

    with list_right:
        st.subheader("High-priority leads")
        if high_priority.empty:
            st.caption("High-fit leads with probability of 40% or more will appear here.")
        else:
            priority_view = high_priority[
                [
                    "organisation_name",
                    "segment",
                    "stage",
                    "probability",
                    "deal_value",
                ]
            ].sort_values(["probability", "deal_value"], ascending=False)
            st.dataframe(
                priority_view,
                width="stretch",
                hide_index=True,
                column_config={
                    "organisation_name": "Organisation",
                    "segment": "Segment",
                    "stage": "Stage",
                    "probability": st.column_config.ProgressColumn(
                        "Probability", min_value=0, max_value=100, format="%d%%"
                    ),
                    "deal_value": st.column_config.NumberColumn(
                        "Deal value", format="₹ %.0f"
                    ),
                },
            )


def render_crm(df: pd.DataFrame) -> pd.DataFrame:
    st.title("Prospect CRM")
    st.caption("Capture opportunities, follow-up dates, and deal confidence in one place.")

    add_tab, manage_tab = st.tabs(["Add prospect", "Manage pipeline"])

    with add_tab:
        with st.form("add_prospect", clear_on_submit=True):
            left, middle, right = st.columns(3)
            organisation = left.text_input("Organisation name *")
            segment = middle.selectbox("Segment *", SEGMENTS)
            state = right.text_input("State")

            website = left.text_input("Website")
            contact_person = middle.text_input("Contact person")
            email = right.text_input("Email")

            disease_focus = left.text_input(
                "Disease focus", placeholder="e.g. TB, diabetes, lung cancer"
            )
            fit_score = middle.selectbox("Fit score", FIT_SCORES)
            stage = right.selectbox("Stage", STAGES)

            deal_value = left.number_input(
                "Deal value (₹)", min_value=0.0, step=50_000.0, format="%.0f"
            )
            probability = middle.slider("Probability (%)", 0, 100, 20, 5)
            follow_up = right.date_input("Next follow-up date", value=date.today())
            notes = st.text_area("Notes", height=110)

            submitted = st.form_submit_button(
                "Add prospect", type="primary", width="stretch"
            )
            if submitted:
                if not organisation.strip():
                    st.error("Organisation name is required.")
                else:
                    now = datetime.now().isoformat(timespec="seconds")
                    new_row = {
                        "id": str(uuid.uuid4()),
                        "organisation_name": organisation.strip(),
                        "segment": segment,
                        "state": state.strip(),
                        "website": website.strip(),
                        "contact_person": contact_person.strip(),
                        "email": email.strip(),
                        "disease_focus": disease_focus.strip(),
                        "fit_score": fit_score,
                        "stage": stage,
                        "deal_value": deal_value,
                        "probability": probability,
                        "next_follow_up_date": pd.Timestamp(follow_up),
                        "notes": notes.strip(),
                        "created_at": now,
                        "updated_at": now,
                    }
                    df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
                    save_prospects(df)
                    st.success(f"{organisation.strip()} added to the pipeline.")

    with manage_tab:
        if df.empty:
            st.info("No prospects yet. Add one in the first tab.")
            return df

        filter_cols = st.columns(4)
        segment_filter = filter_cols[0].multiselect("Segment", SEGMENTS)
        stage_filter = filter_cols[1].multiselect("Stage", STAGES)
        fit_filter = filter_cols[2].multiselect("Fit", FIT_SCORES)
        search = filter_cols[3].text_input("Search organisation")

        filtered = df.copy()
        if segment_filter:
            filtered = filtered[filtered["segment"].isin(segment_filter)]
        if stage_filter:
            filtered = filtered[filtered["stage"].isin(stage_filter)]
        if fit_filter:
            filtered = filtered[filtered["fit_score"].isin(fit_filter)]
        if search:
            filtered = filtered[
                filtered["organisation_name"]
                .astype(str)
                .str.contains(search, case=False, na=False)
            ]

        display_columns = [
            "id",
            "organisation_name",
            "segment",
            "state",
            "contact_person",
            "email",
            "disease_focus",
            "fit_score",
            "stage",
            "deal_value",
            "probability",
            "next_follow_up_date",
            "notes",
        ]
        edited = st.data_editor(
            filtered[display_columns],
            width="stretch",
            hide_index=True,
            disabled=["id"],
            num_rows="fixed",
            column_config={
                "id": None,
                "organisation_name": "Organisation",
                "segment": st.column_config.SelectboxColumn(
                    "Segment", options=SEGMENTS, required=True
                ),
                "state": "State",
                "contact_person": "Contact person",
                "email": "Email",
                "disease_focus": "Disease focus",
                "fit_score": st.column_config.SelectboxColumn(
                    "Fit", options=FIT_SCORES, required=True
                ),
                "stage": st.column_config.SelectboxColumn(
                    "Stage", options=STAGES, required=True
                ),
                "deal_value": st.column_config.NumberColumn(
                    "Deal value", min_value=0, format="₹ %.0f"
                ),
                "probability": st.column_config.NumberColumn(
                    "Probability", min_value=0, max_value=100, format="%d%%"
                ),
                "next_follow_up_date": st.column_config.DateColumn(
                    "Next follow-up", format="DD MMM YYYY"
                ),
                "notes": st.column_config.TextColumn("Notes", width="large"),
            },
            key="pipeline_editor",
        )

        action_left, action_middle, action_right = st.columns([1, 1.6, 1])
        if action_left.button("Save changes", type="primary", width="stretch"):
            original = df.set_index("id")
            updates = edited.set_index("id")
            for prospect_id, row in updates.iterrows():
                for column in updates.columns:
                    original.loc[prospect_id, column] = row[column]
                original.loc[prospect_id, "updated_at"] = datetime.now().isoformat(
                    timespec="seconds"
                )
            df = original.reset_index()
            save_prospects(df)
            st.success("Pipeline changes saved.")

        delete_options = {
            f"{row.organisation_name} · {row.stage}": row.id
            for row in df.itertuples()
        }
        delete_label = action_middle.selectbox(
            "Delete a prospect",
            ["Select…"] + list(delete_options.keys()),
            label_visibility="collapsed",
        )
        if action_right.button("Delete selected", width="stretch"):
            if delete_label == "Select…":
                st.warning("Choose a prospect to delete.")
            else:
                df = df[df["id"] != delete_options[delete_label]].copy()
                save_prospects(df)
                st.success("Prospect deleted.")

        export = df.copy()
        export["next_follow_up_date"] = export["next_follow_up_date"].dt.strftime(
            "%Y-%m-%d"
        )
        st.download_button(
            "Download pipeline CSV",
            export.to_csv(index=False).encode("utf-8"),
            file_name=f"respyr_pipeline_{date.today().isoformat()}.csv",
            mime="text/csv",
        )
    return df


def render_outreach(df: pd.DataFrame) -> None:
    st.title("Outreach Generator")
    st.caption("Create concise partner emails using Research, Reference, Reward, Request.")

    organisations = organisation_options(df)
    source = st.radio(
        "Start from",
        ["CRM prospect", "New organisation"],
        horizontal=True,
        disabled=not organisations,
    )

    selected_data: dict = {}
    if source == "CRM prospect" and organisations:
        selected = st.selectbox("Prospect", organisations)
        selected_data = get_prospect_by_name(df, selected)

    col1, col2 = st.columns(2)
    organisation = col1.text_input(
        "Organisation",
        value=str(selected_data.get("organisation_name", "")),
    )
    segment_value = str(selected_data.get("segment", SEGMENTS[0]))
    segment = col2.selectbox(
        "Segment",
        SEGMENTS,
        index=SEGMENTS.index(segment_value) if segment_value in SEGMENTS else 0,
    )
    contact_person = col1.text_input(
        "Contact person", value=str(selected_data.get("contact_person", ""))
    )
    state = col2.text_input("State / geography", value=str(selected_data.get("state", "")))
    disease_focus = col1.text_input(
        "Disease focus", value=str(selected_data.get("disease_focus", ""))
    )
    sender_name = col2.text_input("Sender name", value="Respyr Partnerships Team")
    request = st.text_input(
        "Specific request (optional)",
        placeholder="e.g. discuss a 500-person screening pilot in Maharashtra",
    )

    subject, body = build_outreach_email(
        segment,
        organisation,
        contact_person,
        disease_focus,
        state,
        sender_name,
        request,
    )

    st.subheader("Generated email")
    st.text_input("Subject", value=subject)
    st.text_area("Email body", value=body, height=390)
    st.caption(
        "Tip: add one specific observation about the partner before sending. "
        "The first line should feel researched, not mass-produced."
    )


def render_discovery_questions(df: pd.DataFrame) -> None:
    st.title("Discovery Question Generator")
    st.caption("Use these prompts to uncover need, workflow, authority, evidence, and budget.")

    organisations = organisation_options(df)
    left, right = st.columns(2)
    selected = left.selectbox(
        "CRM prospect (optional)", ["None"] + organisations
    )
    selected_data = get_prospect_by_name(df, selected) if selected != "None" else {}
    segment_value = str(selected_data.get("segment", SEGMENTS[0]))
    segment = right.selectbox(
        "Segment",
        SEGMENTS,
        index=SEGMENTS.index(segment_value) if segment_value in SEGMENTS else 0,
    )

    st.markdown(
        f'<p style="font-size:0.75rem;font-weight:700;text-transform:uppercase;'
        f'letter-spacing:0.08em;color:#0d9488;margin:0 0 12px 0;">'
        f'{segment} · Discovery Guide</p>',
        unsafe_allow_html=True,
    )

    rows = "".join(
        f'<div style="display:flex;align-items:flex-start;gap:14px;'
        f'padding:14px 18px;border-radius:10px;background:#ffffff;'
        f'border:1px solid #e8edf2;margin-bottom:8px;'
        f'box-shadow:0 1px 2px rgba(15,23,42,0.04);">'
        f'<span style="flex-shrink:0;width:26px;height:26px;border-radius:50%;'
        f'background:#f0fdfa;border:1px solid #99f6e4;color:#0d9488;'
        f'font-size:0.72rem;font-weight:700;display:flex;align-items:center;'
        f'justify-content:center;">{i}</span>'
        f'<span style="color:#1e293b;font-size:0.9rem;line-height:1.6;'
        f'font-family:Inter,sans-serif;">{q}</span>'
        f'</div>'
        for i, q in enumerate(DISCOVERY_QUESTIONS[segment], start=1)
    )
    st.markdown(rows, unsafe_allow_html=True)

    st.markdown(
        '<div style="margin-top:20px;padding:16px 20px;border-radius:12px;'
        'background:#f0fdfa;border:1px solid #99f6e4;">'
        '<span style="color:#0f766e;font-size:0.875rem;line-height:1.6;'
        'font-family:Inter,sans-serif;">'
        '<strong style="color:#0d9488;">Close the call with three confirmations:</strong> '
        'the problem worth solving, the people involved in a decision, '
        'and the agreed next step with a date.'
        '</span></div>',
        unsafe_allow_html=True,
    )


def render_proposal(df: pd.DataFrame) -> None:
    st.title("Proposal Generator")
    st.caption("Draft a practical pilot concept note for internal review and partner discussion.")

    organisations = organisation_options(df)
    selected = st.selectbox("CRM prospect (optional)", ["None"] + organisations)
    selected_data = get_prospect_by_name(df, selected) if selected != "None" else {}

    col1, col2 = st.columns(2)
    partner_name = col1.text_input(
        "Partner organisation",
        value=str(selected_data.get("organisation_name", "")),
    )
    segment_value = str(selected_data.get("segment", SEGMENTS[0]))
    segment = col2.selectbox(
        "Segment",
        SEGMENTS,
        index=SEGMENTS.index(segment_value) if segment_value in SEGMENTS else 0,
    )
    disease_focus = col1.text_input(
        "Disease focus", value=str(selected_data.get("disease_focus", ""))
    )
    geography = col2.text_input(
        "Pilot geography", value=str(selected_data.get("state", ""))
    )
    target_population = col1.text_input(
        "Target population",
        placeholder="e.g. adults aged 40+ in underserved urban communities",
    )
    population_size = col2.number_input(
        "Indicative participants", min_value=10, value=500, step=50
    )
    duration_weeks = col1.number_input(
        "Pilot duration (weeks)", min_value=6, max_value=52, value=12
    )
    commercial_model = col2.selectbox(
        "Commercial model",
        ["Per-screen", "Fixed pilot fee", "Co-funded / grant-supported"],
    )
    price_per_screen = st.number_input(
        "Indicative price per screen (₹)",
        min_value=0.0,
        value=1000.0,
        step=100.0,
        format="%.0f",
    )

    proposal = build_proposal(
        partner_name,
        segment,
        disease_focus,
        geography,
        target_population,
        int(population_size),
        int(duration_weeks),
        commercial_model,
        price_per_screen,
    )

    st.subheader("Pilot concept note")
    st.markdown(proposal)
    st.download_button(
        "Download concept note",
        proposal.encode("utf-8"),
        file_name=f"respyr_pilot_concept_{date.today().isoformat()}.md",
        mime="text/markdown",
        type="primary",
    )


def main() -> None:
    configure_page()
    ensure_data_file()
    df = load_prospects()
    page = render_sidebar()

    if page == "Dashboard":
        render_dashboard(df)
    elif page == "Prospect CRM":
        render_crm(df)
    elif page == "Outreach Generator":
        render_outreach(df)
    elif page == "Discovery Questions":
        render_discovery_questions(df)
    elif page == "Proposal Generator":
        render_proposal(df)


if __name__ == "__main__":
    main()