app.py

# ==========================================================
# CONFLICT OBSERVATORY – MAIN APPLICATION
# ==========================================================
# Streamlit application core
#
# Responsibilities:
# - Data loading
# - Temporal preparation
# - Analytical filters
# - Strategic dashboard
# - Module navigation
# ==========================================================

import streamlit as st
import pandas as pd
import plotly.express as px
import os
import hashlib
import uuid
import base64

from streamlit_folium import st_folium

from modules.statistical_module import render_statistical_module
from modules.methodology_module import render_methodology
from modules.TOO_module import render_overview_map
from modules.Geospatial_analysis_module import (
    render_analytical_map,
    render_sector_pressure_map,
    render_regional_activity_map,
    render_control_zone_map,
    render_operational_corridor_map
)
from modules.table_module import render_table_module
from modules.report_builder import (
    generate_csv, 
    generate_citation_txt, 
    generate_bibtex, 
    generate_ris, 
    generate_mla,
    generate_chicago,
    generate_numbered,
    generate_html_report
)
from modules.mobile_module import inject_mobile_responsiveness

# ==========================================================
# PAGE CONFIGURATION
# ==========================================================

st.set_page_config(
    page_title="Western Sahara War Archive (2020–2024)",
    layout="wide",
    initial_sidebar_state="expanded",
    menu_items={
        'About': "### Western Sahara War Archive\nA Digital Humanities research project by Jorge Teixeira (CEAUP)."
    }
)

# ==========================================================
# PREMIUM CSS INJECTION
# ==========================================================

st.markdown("""
<style>
    /* Styling for the Sidebar */
    [data-testid="stSidebar"] {
        border-right: 1px solid rgba(128, 128, 128, 0.2);
        width: 442px !important;
        min-width: 442px !important;
        max-width: 442px !important;
    }
    
    /* Elegant Sidebar Headers */
    [data-testid="stSidebarNav"] h1, [data-testid="stSidebar"] h1, [data-testid="stSidebar"] h2 {
        font-family: 'Inter', sans-serif;
        font-weight: 700;
        letter-spacing: -0.02em;
    }
    
    /* Enhancing Multiselect appearance */
    .stMultiSelect, .stSelectbox, .stRadio {
        border-radius: 8px !important;
    }

    /* Style for the Select All Checkboxes */
    .stCheckbox {
        padding-bottom: 0px !important;
        margin-bottom: -5px !important;
    }
    
    .stCheckbox label div p {
        font-size: 11px !important;
        font-style: italic;
        opacity: 0.7;
    }

    /* Expander styling */
    .st-emotion-cache-p5msec {
        border-radius: 10px !important;
        border: 1px solid rgba(128, 128, 128, 0.2) !important;
    }

    /* Small text for methodological sidebar section */
    .framework-text {
        font-size: 0.8rem !important;
        line-height: 1.3 !important;
        opacity: 0.9;
    }
    
    .framework-text h3 {
        font-size: 0.9rem !important;
        margin-top: 10px !important;
        margin-bottom: 5px !important;
    }
</style>
""", unsafe_allow_html=True)

# Injetar regras específicas para tablets/telemóveis
inject_mobile_responsiveness()

# ==========================================================
# GLOBAL UNIQUE KEY SYSTEM
# ==========================================================

_visual_counter = 0

def get_unique_key(prefix: str = "viz") -> str:
    """
    Generate a globally unique, stable widget key.

    Uses a monotonically incrementing counter to produce deterministic
    keys that remain stable across reruns as long as render order is unchanged.

    Parameters
    ----------
    prefix : str
        Human-readable prefix to identify the widget type (e.g. 'selectbox').

    Returns
    -------
    str
        A unique key string in the format '<prefix>_<counter>'.
    """
    global _visual_counter
    _visual_counter += 1
    return f"{prefix}_{_visual_counter}"

def st_selectbox_unique(label: str, options: list, **kwargs) -> object:
    """Wrapper for st.selectbox that auto-assigns a unique key if none is provided."""
    if "key" not in kwargs:
        kwargs["key"] = get_unique_key("selectbox")
    return st.selectbox(label, options, **kwargs)

def st_multiselect_unique(label: str, options: list, default: list = None, **kwargs) -> list:
    """Wrapper for st.multiselect that auto-assigns a unique key if none is provided."""
    if "key" not in kwargs:
        kwargs["key"] = get_unique_key("multiselect")
    return st.multiselect(label, options, default=default, **kwargs)

def st_radio_unique(label: str, options: list, **kwargs) -> object:
    """Wrapper for st.radio that auto-assigns a unique key if none is provided."""
    if "key" not in kwargs:
        kwargs["key"] = get_unique_key("radio")
    return st.radio(label, options, **kwargs)

_original_st_folium = st_folium
def st_folium_unique(map_object, **kwargs) -> dict:
    """Wrapper for st_folium that auto-assigns a unique key if none is provided."""
    if "key" not in kwargs:
        kwargs["key"] = get_unique_key("folium")
    return _original_st_folium(map_object, **kwargs)

st_folium = st_folium_unique

# ==========================================================
# UI COMPONENTS (BREADCRUMBS & HELPERS)
# ==========================================================

def render_breadcrumbs(current_menu: str, sub_path: str = None):
    """Render a clean, responsive breadcrumb navigation bar."""
    items = ["Archive", current_menu]
    if sub_path:
        items.append(sub_path)
    
    # Modern breadcrumb style
    bc_links = []
    for i, item in enumerate(items):
        if i == len(items) - 1:
            bc_links.append(f'<span style="color: #64B5F6; font-weight: 700;">{item}</span>')
        else:
            bc_links.append(f'<span style="opacity: 0.6;">{item}</span>')
            
    bc_html = f"""
    <div style="margin-top: -15px; margin-bottom: 25px; font-size: 0.85rem; letter-spacing: 0.02em; display: flex; align-items: center; gap: 10px;">
        {" <span style='opacity: 0.3;'>/</span> ".join(bc_links)}
    </div>
    """
    st.markdown(bc_html, unsafe_allow_html=True)

def metric_card(label: str, value: str, delta: str = None, help_text: str = None):
    """Custom metric card with more professional styling."""
    with st.container():
        st.markdown(f"""
        <div style="background: rgba(255,255,255,0.03); padding: 20px; border-radius: 12px; border: 1px solid rgba(255,255,255,0.05); height: 100%;">
            <div style="font-size: 0.8rem; color: #AAA; text-transform: uppercase; font-weight: 600; margin-bottom: 8px;">{label}</div>
            <div style="font-size: 1.8rem; font-weight: 700; color: #FFF;">{value}</div>
            {f'<div style="font-size: 0.8rem; color: #64B5F6; margin-top: 5px;">{delta}</div>' if delta else ''}
        </div>
        """, unsafe_allow_html=True)

# ==========================================================
# ARCHIVAL INFRASTRUCTURE (DATA & HELPERS)
# ==========================================================

@st.cache_data
def load_events() -> pd.DataFrame:
    """
    Load and pre-process the conflict event dataset from CSV.

    Reads the archival CSV, parses dates, cleans coordinate strings,
    and converts all temporal columns to integer types.
    Results are cached by Streamlit to avoid redundant I/O on reruns.

    Returns
    -------
    pd.DataFrame
        Fully cleaned and typed event dataframe ready for analysis.
    """
    df = pd.read_csv("data/Matrix_Database_2020_2024.csv", sep=";", low_memory=False)

    df["Event_Date"] = pd.to_datetime(df["Event_Date"], dayfirst=True, errors="coerce")
    df["N_of_Event"] = pd.to_numeric(df["N_of_Event"], errors="coerce").fillna(0)

    def clean_coord(val: object, pos_suffix: str, neg_suffix: str) -> float:
        """Parse a coordinate string with cardinal direction suffix to float."""
        if pd.isna(val) or not isinstance(val, str): return 0.0
        val = val.replace("°", "")
        if pos_suffix in val: return float(val.replace(pos_suffix, ""))
        if neg_suffix in val: return -float(val.replace(neg_suffix, ""))
        try: return float(val)
        except: return 0.0

    df["Meso_Level_Latitude"] = df["Meso_Level_Latitude"].apply(lambda x: clean_coord(x, "N", "S"))
    df["Meso_Level_Longitude"] = df["Meso_Level_Longitude"].apply(lambda x: clean_coord(x, "E", "W"))
    
    # Convert temporal columns to numeric/int safely
    temp_cols = [
        "Calender_Year", "Calender_Month", "Calender_Quarter", "Calender_Semester", "Calender_Week",
        "Conflict_Year", "Conflict_Month", "Conflict_Quarter", "Conflict_Semester", "Conflict_Week"
    ]
    for c in temp_cols:
        if c in df.columns:
            df[c] = pd.to_numeric(df[c], errors="coerce").fillna(0).astype(int)
    
    return df

def sort_sectors(sectors: list) -> list:
    """
    Sort sector IDs numerically (e.g. S1, S2, ..., S13).

    Extracts the numeric suffix from each sector label and sorts
    accordingly, avoiding the alphabetical mismatch of 'S10' < 'S2'.

    Parameters
    ----------
    sectors : list
        List of sector ID strings (e.g. ['S3', 'S10', 'S1']).

    Returns
    -------
    list
        Sorted list of sector IDs in natural numeric order.
    """
    def get_num(s: str) -> int:
        """Extract the numeric suffix from a sector ID string."""
        if not isinstance(s, str): return 0
        num_part = ''.join(filter(str.isdigit, s))
        return int(num_part) if num_part else 0
    return sorted(sectors, key=get_num)

df = load_events()

# ==========================================================
# INSTITUTIONAL HEADER
# ==========================================================

# --- Institutional Header (Centered with Logo) ---
logo_path_header = os.path.join("assets", "logos", "logo_wswa.png")
if os.path.exists(logo_path_header):
    with open(logo_path_header, "rb") as f:
        encoded_logo = base64.b64encode(f.read()).decode()
    
    st.markdown(f"""
        <div style="text-align: center; margin-bottom: 30px;">
            <div style="display: flex; align-items: center; justify-content: center; gap: 25px; margin-bottom: 15px; flex-wrap: wrap;">
                <img src="data:image/png;base64,{encoded_logo}" width="120">
                <h1 style="margin: 0; font-family: 'Inter', sans-serif; font-weight: 800; letter-spacing: -0.03em;">
                    Western Sahara War Archive (2020–2024)
                </h1>
            </div>
            <h3 style="margin: 0; font-weight: 400; opacity: 0.9;">Interactive Geospatial Conflict Observatory</h3>
            <p style="margin: 10px 0; font-size: 0.9rem; font-style: italic; opacity: 0.7;">
                Note: This information is subject to continuous update and institutional review.
            </p>
            <a href="https://westernsaharawararchive.com/" target="_blank" style="text-decoration: none; color: #64B5F6; font-weight: 600; font-size: 1.1rem;">
                Visit Project Page
            </a>
        </div>
    """, unsafe_allow_html=True)
else:
    st.title("Western Sahara War Archive (2020–2024)")
    st.markdown("### Interactive Geospatial Conflict Observatory")
    st.markdown("*Note: This information is subject to continuous update and institutional review.*")
    st.markdown("[Visit Project Page](https://westernsaharawararchive.com/)")

# ==========================================================
# CENTRALIZED NAVIGATION
# ==========================================================

st.markdown("""
<style>
    /* Sleek container rounding */
    .block-container {
        padding-top: 2rem !important;
        padding-bottom: 2rem !important;
    }
    
    /* Blocky Navbar Styling (Refined for Streamlit Radio) */
    div[role="radiogroup"] {
        background-color: #000000; /* Solid Black Background */
        padding: 0 !important;
        border-radius: 0 !important;
        display: flex !important;
        flex-direction: row !important;
        flex-wrap: nowrap !important;
        gap: 0 !important;
        width: 100% !important;
        max-width: 100% !important;
        border: none !important;
        margin-top: 15px;
        margin-bottom: 25px;
        box-shadow: 0 4px 15px rgba(0, 0, 0, 0.6);
    }
    
    /* Force all Streamlit wrappers for the radio to be full width */
    div[role="radiogroup"] > div {
        width: 100% !important;
        display: flex !important;
        flex-direction: row !important;
        gap: 0 !important;
    }

    /* Hide the radio button markers (dots) */
    div[role="radiogroup"] [data-testid="stMarker"] {
        display: none !important;
    }

    /* Style the labels as full-width blocks */
    div[role="radiogroup"] label {
        flex: 1 1 0% !important; /* Ensure they grow equally and start from 0 */
        background-color: #000000 !important;
        color: #FFFFFF !important;
        padding: 22px 10px !important;
        border-radius: 0 !important;
        transition: all 0.2s ease;
        cursor: pointer;
        border: none !important;
        border-right: 1px solid rgba(255, 255, 255, 0.15) !important;
        text-align: center;
        display: flex;
        align-items: center;
        justify-content: center;
        font-weight: 600 !important;
        font-size: 0.85rem;
        text-transform: uppercase;
        letter-spacing: 0.08em;
        margin: 0 !important;
    }

    div[role="radiogroup"] label:hover {
        background-color: #1a1a1a !important;
        color: #ef7d00 !important;
    }
    
    /* Active State (Orange highlight) */
    div[role="radiogroup"] label[data-checked="true"],
    div[role="radiogroup"] label:has(input:checked) {
        background-color: #ef7d00 !important;
        color: #000000 !important;
        font-weight: 800 !important;
        box-shadow: inset 0 0 10px rgba(0,0,0,0.2);
    }

    /* Remove border from last child labels */
    div[role="radiogroup"] label:last-child {
        border-right: none !important;
    }
    
    /* Ensure the text inside labels is centered and has no extra space */
    div[role="radiogroup"] label div[data-testid="stMarkdownContainer"] p {
        margin: 0 !important;
        line-height: 1 !important;
        white-space: nowrap; /* Prevent text wrapping within the blocks */
    }
    
    /* Metrics beautification (Dark Theme) */
    div[data-testid="stMetricValue"] {
        font-size: 2.2rem !important;
        font-weight: 700 !important;
        color: #e0e0e0 !important;
    }
</style>
""", unsafe_allow_html=True)

menu = st.radio(
    "Explore the Observatory",
    [
        "Theatre of Operations Overview", 
        "Data", 
        "Conflict Analysis Suite", 
        "Methodology",
        "Documents",
        "About Us"
    ],
    horizontal=True,
    label_visibility="collapsed"
)

# Render Breadcrumbs based on active menu
render_breadcrumbs(menu)

# ==========================================================
# GLOBAL STATE & DATA FILTERING (NO UI HERE)
# ==========================================================

# Initialize defaults in session_state if missing to prevent crashes
if "global_time_framework" not in st.session_state: st.session_state.global_time_framework = "Conflict Time"
if "global_years_filter" not in st.session_state: st.session_state.global_years_filter = sorted(df["Conflict_Year"].dropna().unique())
if "global_quarter_filter" not in st.session_state: st.session_state.global_quarter_filter = sorted(df["Conflict_Quarter"].dropna().unique())
if "global_semester_filter" not in st.session_state: st.session_state.global_semester_filter = sorted(df["Conflict_Semester"].dropna().unique())
if "global_season_filter" not in st.session_state: st.session_state.global_season_filter = sorted(df["Conflict_Season"].dropna().unique())
if "global_mr_filter" not in st.session_state: st.session_state.global_mr_filter = []
if "global_sector_filter" not in st.session_state: st.session_state.global_sector_filter = []
if "global_actor_filter" not in st.session_state: st.session_state.global_actor_filter = []

# Fetch active global filters from session_state
time_mode = st.session_state.global_time_framework
year_col = "Conflict_Year" if time_mode == "Conflict Time" else "Calender_Year"
q_col = "Conflict_Quarter" if time_mode == "Conflict Time" else "Calender_Quarter"
sem_col = "Conflict_Semester" if time_mode == "Conflict Time" else "Calender_Semester"
sea_col = "Conflict_Season" if time_mode == "Conflict Time" else "Calender_Season"

selected_years = st.session_state.global_years_filter
selected_quarters = st.session_state.global_quarter_filter
selected_semesters = st.session_state.global_semester_filter
selected_seasons = st.session_state.global_season_filter

mr_filter = st.session_state.global_mr_filter
sector_filter = st.session_state.global_sector_filter
actor_filter = st.session_state.global_actor_filter

# Global data filtering logic
df_f = df[
    (df[year_col].isin(selected_years)) &
    (df[q_col].isin(selected_quarters)) &
    (df[sem_col].isin(selected_semesters)) &
    (df[sea_col].isin(selected_seasons))
]

if mr_filter:
    df_f = df_f[df_f["Macro_Level_ID"].isin(mr_filter)]
if sector_filter:
    df_f = df_f[df_f["Meso_Level_ID"].isin(sector_filter)]
if actor_filter:
    df_f = df_f[
        (df_f["Attacker"].isin(actor_filter)) | 
        (df_f["N_of_Event"] == 0)
    ]

def get_timeframe_str() -> str:
    years = sorted(selected_years)
    if not years: return "No Period Selected"
    if len(years) == 1: return str(years[0])
    return f"{min(years)}-{max(years)}"

timeframe_str = get_timeframe_str()

# Performance Optimization: "Apply" state for heavy analytics
if "analysis_applied" not in st.session_state: st.session_state.analysis_applied = False
    


st.markdown("---")


# ==========================================================
# COLOR SYSTEM
# ==========================================================

region_colors = {
    "MR1": "#E2EFD9",
    "MR2": "#FEF2CB",
    "MR3": "#FBE4D5"
}

sector_colors = {
    "S1": "#BED5B4", "S2": "#90BB7A", "S3": "#68A242", "S4": "#568736",
    "S5": "#FFDDAD", "S6": "#FFCA69", "S7": "#EFB300", "S8": "#C89600",
    "S9": "#FF6161", "S10": "#FF3737", "S11": "#FF1D1D", "S12": "#D20000",
    "S13": "#A80000"
}

# ==========================================================
# CONFLICT TIMELINE (FUNCTION)
# ==========================================================

def render_timeline():
    st.subheader("Conflict Chronology Since 13 November 2020")
    
    timeline_data = pd.DataFrame({
        "Phase":[
            "Ceasefire Breakdown (Guerguerat)",
            "Initial Low Intensity Exchange Phase",
            "Sustained Attritional Phase",
            "Current Operational Phase"
        ],
        "Start":[pd.to_datetime(d) for d in [
            "2020-11-13",
            "2021-01-01",
            "2022-01-01",
            "2023-01-01"
        ]],
        "End":[
            pd.to_datetime("2020-12-31"),
            pd.to_datetime("2021-12-31"),
            pd.to_datetime("2022-12-31"),
            pd.Timestamp.today()
        ]
    })
    
    fig_timeline = px.timeline(
        timeline_data,
        x_start="Start",
        x_end="End",
        y="Phase"
    )
    
    fig_timeline.update_yaxes(autorange="reversed")
    st.plotly_chart(fig_timeline, width="stretch")

# ==========================================================
# ABOUT US (TEAM & PROJECT)
# ==========================================================

def render_about_us():
    st.header("About the Project")
    
    tab1, tab2, tab3 = st.tabs(["About Project", "Cookies Policy", "Legal Framework (EU)"])
    
    with tab1:
        col1, col2 = st.columns([2, 1])
        
        with col1:
            st.markdown("""
            ### Western Sahara War Archive (WSWA)
            The **Western Sahara War Archive** is a Digital Humanities research project dedicated to the documentation and geospatial analysis of the conflict in Western Sahara since the breakdown of the ceasefire on November 13, 2020.
            
            Our mission is to provide an open-access, academically rigorous platform for researchers, analysts, and the public to understand the operational dynamics and human impact of this protracted war.
            """)
            
            st.subheader("Project Team")
            
            team_col1, team_col2 = st.columns(2)
            
            with team_col1:
                st.image("https://www.cienciavitae.pt/portal/FB17-55DF-0E11/foto", width=150) # Fallback image if possible or just text
                st.markdown("""
                **Jorge Teixeira**  
                *Lead Investigator & PI*  
                [CiênciaVitae Profile](https://www.cienciavitae.pt/portal/FB17-55DF-0E11)
                """)
                
            with team_col2:
                st.markdown("""
                <br><br>
                """, unsafe_allow_html=True) # Spacer
                st.markdown("""
                **Isabel Lourenço**  
                *Researcher & Contributor*  
                [CiênciaVitae Profile](https://www.cienciavitae.pt/portal/6E15-6D4A-6324)
                """)
                
        with col2:
            st.info("**Support the Research**")
            st.markdown("""
            This archive is hosted at the **Centro de Estudos Africanos da Universidade do Porto (CEAUP)**. 
            
            For inquiries or data contributions, please visit our [official website](https://westernsaharawararchive.com/).
            """)
            
            st.subheader("Multimedia")
            st.markdown("[![YouTube](https://img.shields.io/badge/YouTube-FF0000?style=for-the-badge&logo=youtube&logoColor=white)](https://www.youtube.com/channel/UCDtLsJ05L5EblXSzfcKbRkA)")

    with tab2:
        st.subheader("Privacy & Cookie Policy (GDPR / ePrivacy)")
        st.markdown("""
        **1. Personal Data Protection (GDPR & PT Law 58/2019)**
        In strict compliance with the European General Data Protection Regulation (Regulation (EU) 2016/679) and the Portuguese Data Protection Law (Law no. 58/2019), the *Western Sahara War Archive* **does not collect, store, or process** any Personally Identifiable Information (PII) from its visitors. Access to the platform is fully anonymous and requires no registration. We do not engage in data brokerage or third-party data sharing.
        
        **2. Cookie Compliance (ePrivacy Directive & PT Law 41/2004)**
        This academic infrastructure utilizes only **strictly necessary technical cookies** natively generated by the *Streamlit* framework. These cookies are exclusively deployed to maintain the active state of your session (e.g., filter memory, interactive map states) during navigation. 
        We do not deploy targeting cookies, corporate marketing trackers (pixels), or third-party analytics engines. In accordance with Portuguese Law no. 46/2012 (transposing the EU ePrivacy Directive), explicit prior consent is not required for purely functional and technical session cookies. By continuing to interact with the dashboard, you acknowledge the temporary use of these technical session identifiers, which are automatically erased upon closing your browser.
        """)

    with tab3:
        st.subheader("Legal Framework (EU & Portugal)")
        st.markdown("""
        **1. Academic Exemption & EU Digital Services Act (DSA)**
        The *Western Sahara War Archive* operates strictly as a **Digital Humanities and Scientific Research Archive**. The platform operates under the explicit exemptions granted for academic research, journalistic purposes, and open scientific inquiry under European legislation, including the Digital Services Act (Regulation (EU) 2022/2065). The overarching objective is to promote open-access transparency and empirical conflict analysis.
        
        **2. Institutional Identity (E-Commerce DL no. 7/2004)**
        In accordance with the Portuguese legal requirements for digital platforms, this project is managed by independent researchers hosted at the **Centro de Estudos Africanos da Universidade do Porto (CEAUP)**. 
        **Scientific Lead:** Jorge Teixeira. This analytical observatory is strictly non-profit and engages in zero commercial activity.
        
        **3. Intellectual Property & Copyright (CDADC)**
        Under the Portuguese Code of Copyright and Related Rights (CDADC) and the EU Copyright Directive (Directive (EU) 2019/790), the custom analytical visualizations (Modules [B] and [G]), underlying algorithmic methodology, and comprehensive statistical reports are protected intellectual property. 
        The primary cartographic and chronological datasets are aggregated from public domain official military communiqués under the scientific "Fair Use" doctrine. Appropriate academic citation leveraging the provided Zenodo/DOI identifiers is mandatory for the replication or reuse of this infrastructure's data.
        """)

# ==========================================================
# DOCUMENTS (FILE MANAGER)
# ==========================================================

def render_documents():
    st.header("Institutional & Research Documents")
    st.markdown("""
    This repository contains official documents and authorizations granted by the **Sahrawi Press Service (SPS)** and the **Sahrawi Arab Democratic Republic (SADR)**. 
    
    These documents formally authorize the research team to utilize, archive, analyse, and publish conflict data, official communiqués, and news released through their institutional platforms for academic and historical documentation.
    """)
    
    docs_dir = "docs"
    if not os.path.exists(docs_dir):
        st.error(f"Directory '{docs_dir}' not found.")
        return
        
    files = [f for f in os.listdir(docs_dir) if os.path.isfile(os.path.join(docs_dir, f))]
    
    if not files:
        st.info("No documents currently available in this section.")
        return
        
    for file in files:
        col1, col2 = st.columns([3, 1])
        file_path = os.path.join(docs_dir, file)
        with col1:
            st.write(f"**{file}**")
        with col2:
            with open(file_path, "rb") as f:
                st.download_button(
                    label="Download PDF",
                    data=f,
                    file_name=file,
                    mime="application/pdf",
                    key=f"dl_{file}"
                )
        st.markdown("---")

# Logic replaced and moved to the infrastructure section above

# Rendering Logic - Replaced by global sidebar and direct module calls in selection blocks

# ==========================================================
# RENDERING LOGIC
# ==========================================================

if menu == "Theatre of Operations Overview":
    st.header("Theatre of Operations Overview")
    
    # --- Strategic Metrics ---
    with st.container():
        m1, m2, m3, m4 = st.columns(4)
        total_ev = int(df_f["N_of_Event"].sum())
        active_sectors = df_f[df_f["N_of_Event"] > 0]["Meso_Level_ID"].nunique()
        total_sectors = df_f["Meso_Level_ID"].nunique()
        territorial_spread = (active_sectors / total_sectors) * 100 if total_sectors > 0 else 0
        
        # Combat Pressure (Avg events per day in selection)
        days_in_selection = df_f["Event_Date"].nunique()
        strategic_tempo = total_ev / days_in_selection if days_in_selection > 0 else 0

        with m1: metric_card("Total Conflict Events", f"{total_ev:,}")
        with m2: metric_card("Sectors with Combat", str(active_sectors))
        with m3: metric_card("Territorial Spread", f"{territorial_spread:.1f}%")
        with m4: metric_card("Strategic Tempo", f"{strategic_tempo:.2f} Ev/Day")
    
    st.markdown("---")
    st.subheader("B1: Full Operational Theater Overview")
    render_overview_map(timeframe_str, mode="B1")
    st.caption("**How to read (B1):** Displays the comprehensive geographic layout of the operational theater. © Jorge Teixeira")
    
    st.markdown("---")
    render_timeline()
    st.caption("**How to read (Timeline):** Displays key milestones and chronological progression.")

elif menu == "Conflict Analysis Suite":
    st.header("Conflict Analysis Suite")
    
    # 1. THE CONTROL PANEL (FILTERS UI)
    st.markdown("### 1. Global Analytical Control Panel")
    with st.container():
        # Compact style for the filter section
        st.markdown('<div style="background: rgba(100, 181, 246, 0.05); padding: 20px; border-radius: 12px; border: 1px solid rgba(100, 181, 246, 0.2); margin-bottom: 25px;">', unsafe_allow_html=True)
        
        fcol1, fcol2, fcol3 = st.columns([1.5, 1.2, 1.2])
        
        with fcol1:
            st.markdown("**Temporal Parameters**")
            new_time_mode = st_radio_unique("Framework", ["Conflict Time", "Civil Time"], key="global_time_framework", horizontal=True)
            new_year_col = "Conflict_Year" if new_time_mode == "Conflict Time" else "Calender_Year"
            
            fy1, fy2 = st.columns(2)
            avail_y = sorted(df[new_year_col].dropna().unique())
            def_y = [y for y in st.session_state.get("global_years_filter", []) if y in avail_y]
            with fy1: st_multiselect_unique("Year(s)", avail_y, default=def_y if def_y else avail_y, key="global_years_filter")
            
            avail_q = sorted(df[q_col].dropna().unique())
            def_q = [q for q in st.session_state.get("global_quarter_filter", []) if q in avail_q]
            with fy2: st_multiselect_unique("Quarter(s)", avail_q, default=def_q if def_q else avail_q, key="global_quarter_filter")
            
        with fcol2:
            st.markdown("**Territorial Parameters**")
            avail_mrs = sorted(df["Macro_Level_ID"].unique())
            def_mr = [m for m in st.session_state.get("global_mr_filter", []) if m in avail_mrs]
            st_multiselect_unique("Military Region (Macro)", avail_mrs, default=def_mr, key="global_mr_filter")
            
            sector_opts = sort_sectors(df["Meso_Level_ID"].unique())
            def_sec = [s for s in st.session_state.get("global_sector_filter", []) if s in sector_opts]
            st_multiselect_unique("Sector (Meso)", sector_opts, default=def_sec, key="global_sector_filter")
            
        with fcol3:
            st.markdown("**Actor Initiative**")
            if "Attacker" in df.columns:
                avail_actors = sorted([str(x) for x in df["Attacker"].dropna().unique() if str(x).strip() != ""])
                def_act = [a for a in st.session_state.get("global_actor_filter", []) if a in avail_actors]
                st_multiselect_unique("Attacker", avail_actors, default=def_act if def_act else avail_actors, key="global_actor_filter")
        
        st.markdown(f"**Status:** {int(df_f['N_of_Event'].sum())} events filtered for {timeframe_str}.")
        
        # Performance "Apply" Button
        apply_col1, apply_col2 = st.columns([1, 1])
        with apply_col1:
            if st.button("Execute Analytical Deep-Dive", type="primary", use_container_width=True):
                st.session_state.analysis_applied = True
                st.rerun()
        with apply_col2:
            if st.button("Reset Parameters", use_container_width=True):
                st.session_state.analysis_applied = False
                # Reset logic could be more thorough if needed
                st.rerun()
        
        st.markdown('</div>', unsafe_allow_html=True)

    st.markdown("---")
    
    # 2. THE PERSPECTIVES (SUB-MENU)
    st.markdown("### 2. Analytical Perspectives")
    suite_view = st.radio("Select Perspective", [
        "Strategic Macro Maps",
        "Geospatial Analytics",
        "Statistical Models",
        "Report Exporter"
    ], horizontal=True, label_visibility="collapsed")
    
    # Update breadcrumbs with sub-path
    render_breadcrumbs(menu, suite_view)
    
    st.markdown("---")
    
    if not st.session_state.analysis_applied and suite_view != "Report Exporter":
        st.info("💡 Adjust your filters above and click **Execute Analytical Deep-Dive** to generate the maps and models.")
        st.stop()

    if suite_view == "Strategic Macro Maps":
        with st.popover("ℹ️ About Strategic Macro Maps (B2–B6)"):
            st.markdown("""
            **Strategic Macro Maps [B2–B6]** focus on the static and structural elements of the operational theater. 
            They provide the baseline geospatial context (territorial control, command boundaries, and defensive infrastructure) 
            necessary to interpret dynamic tactical events.
            """)
        b_tabs = st.tabs(["B2: Territorial Control", "B3: Military Regions", "B4: Operational Sectors", "B5: Military Wall", "B6: Logistics & Hydro"])
        with b_tabs[0]: st.subheader("B2: Territorial Control Map"); render_overview_map(timeframe_str, mode="B2")
        with b_tabs[1]: st.subheader("B3: Military Regions Map"); render_overview_map(timeframe_str, mode="B3")
        with b_tabs[2]: st.subheader("B4: Operational Sectors Map"); render_overview_map(timeframe_str, mode="B4")
        with b_tabs[3]: st.subheader("B5: Moroccan Military Wall Map"); render_overview_map(timeframe_str, mode="B5")
        with b_tabs[4]: st.subheader("B6: Logistics & Strategic Geography"); render_overview_map(timeframe_str, mode="B6")
        
    elif suite_view == "Geospatial Analytics":
        if df_f.empty:
            st.warning("No events for the selected filters.")
        else:
            with st.popover("ℹ️ About Geospatial Analytics (G1–G8)"):
                st.markdown("""
                **Geospatial Analytics [G1–G8]** apply advanced spatial modeling (KDE, Cluster Analysis) to conflict events.
                These dynamic maps identify tactical density, conflict hotspots, and operational corridors, 
                allowing for the visualization of shifting combat pressure over time.
                """)
            selection_hash = hashlib.md5(str(selected_years + mr_filter + sector_filter + actor_filter).encode()).hexdigest()[:8]
            g_tabs = st.tabs(["G1: Control Zones", "G2: Sector Pressure", "G3: Regional Activity", "G4: Tactical Density", "G5: Hotspots", "G6: Wall Pressure", "G7: Corridors", "G8: Actor Activity"])
            with g_tabs[0]: st.markdown(f"### G1: Control Zone Map"); render_control_zone_map(df_f, map_key=f"control_{selection_hash}", timeframe=timeframe_str)
            with g_tabs[1]: st.markdown(f"### G2: Sector Pressure Map"); render_sector_pressure_map(df_f, map_key=f"sector_{selection_hash}", timeframe=timeframe_str)
            with g_tabs[2]: st.markdown(f"### G3: Regional Activity Map"); render_regional_activity_map(df_f, map_key=f"region_{selection_hash}", timeframe=timeframe_str)
            with g_tabs[3]: st.markdown(f"### G4: Tactical Density Map"); render_analytical_map(df_f, mode="density", map_key=f"heat_{selection_hash}", timeframe=timeframe_str)
            with g_tabs[4]: st.markdown(f"### G5: Conflict Hotspots Map"); render_analytical_map(df_f, mode="hotspot", map_key=f"hotspot_{selection_hash}", timeframe=timeframe_str)
            with g_tabs[5]: st.markdown(f"### G6: Tactical Wall Pressure Map"); render_analytical_map(df_f, mode="wall_pressure", map_key=f"wall_p_{selection_hash}", timeframe=timeframe_str)
            with g_tabs[6]: st.markdown(f"### G7: Operational Corridors Map"); render_operational_corridor_map(df_f, map_key=f"corridor_{selection_hash}", timeframe=timeframe_str)
            with g_tabs[7]: st.markdown(f"### G8: Actor Activity Distribution Map"); render_analytical_map(df_f, mode="actor", map_key=f"actor_{selection_hash}", timeframe=timeframe_str)

    elif suite_view == "Statistical Models":
        with st.popover("ℹ️ About Statistical Analysis Suite (A1–A25)"):
            st.markdown("""
            **Statistical Models [A1–A25]** provide quantitative insights into the conflict's behavior. 
            From high-resolution descriptive statistics to predictive Markov chains, these models analyze 
            operational tempo, seasonal dynamics, and tactical initiative distribution.
            """)
        if df_f.empty:
            st.warning("No events correspond to the selected analytical configuration.")
        else:
            df_stat = df_f.copy()
            for col, default in [("Micro_Level_Name", "Unknown"), ("Meso_Level_Name", "Unknown"), ("Macro_Level_Name", "Unknown"), ("Country", "Unknown")]:
                if col not in df_stat.columns: df_stat[col] = default
            render_statistical_module(df_stat, timeframe=timeframe_str)
            
    elif suite_view == "Report Exporter":
        st.subheader("Data Export & Reporting")
        rcol1, rcol2 = st.columns(2)
        with rcol1:
            st.markdown("#### Raw Database Export")
            st.download_button("Download Active Dataset (CSV)", data=generate_csv(df_f), file_name=f"WSWA_Data_{timeframe_str.replace('-','_')}.csv", mime="text/csv", width="stretch")
        with rcol2:
            st.markdown("#### Comprehensive HTML Report")
            if "report_html" not in st.session_state: st.session_state.report_html = None
            if st.button("Compile Full Diagnostic Report", width="stretch"):
                with st.spinner("Compiling full analytical report..."):
                    st.session_state.report_html = generate_html_report(df_f, timeframe_str, time_mode)
                st.rerun()
            if st.session_state.report_html:
                st.download_button("Download Generated Report (HTML)", data=st.session_state.report_html, file_name=f"WSWA_Report_{timeframe_str.replace('-','_')}.html", mime="text/html", width="stretch")

elif menu == "Data":
    render_table_module(df_f)

elif menu == "Methodology":
    render_methodology(df_f, timeframe_str)

elif menu == "Documents":
    render_documents()

elif menu == "About Us":
    render_about_us()

# ==========================================================
# FOOTER (LOGOS)
# ==========================================================

st.markdown("---")
footer_col1, footer_col2, footer_col3, footer_col4 = st.columns([1,1,1,3])
logo_path = os.path.join("assets", "logos")

with footer_col1:
    if os.path.exists(os.path.join(logo_path, "logo_wswa.png")):
        with open(os.path.join(logo_path, "logo_wswa.png"), "rb") as f:
            encoded = base64.b64encode(f.read()).decode()
            st.markdown(f'<div style="background: white; padding: 12px; border-radius: 10px; display: inline-block; box-shadow: 0 4px 8px rgba(0,0,0,0.3);"><img src="data:image/png;base64,{encoded}" width="180"></div>', unsafe_allow_html=True)
    st.markdown("<br>", unsafe_allow_html=True)
    st.markdown("[![YouTube](https://img.shields.io/badge/YouTube-red?style=for-the-badge&logo=youtube&logoColor=white)](https://www.youtube.com/channel/UCDtLsJ05L5EblXSzfcKbRkA)")

with footer_col2:
    if os.path.exists(os.path.join(logo_path, "logo_ceaup.jpg")):
        with open(os.path.join(logo_path, "logo_ceaup.jpg"), "rb") as f:
            encoded = base64.b64encode(f.read()).decode()
            st.markdown(f'<div style="background: white; padding: 12px; border-radius: 10px; display: inline-block; box-shadow: 0 4px 8px rgba(0,0,0,0.3);"><img src="data:image/jpeg;base64,{encoded}" width="180"></div>', unsafe_allow_html=True)
    st.markdown("<p style='font-size:20px; margin-top:8px;'><b>Host Institution</b></p>", unsafe_allow_html=True)

with footer_col3:
    if os.path.exists(os.path.join(logo_path, "logo_fct.jpg")):
        with open(os.path.join(logo_path, "logo_fct.jpg"), "rb") as f:
            encoded = base64.b64encode(f.read()).decode()
            st.markdown(f'<div style="background: white; padding: 12px; border-radius: 10px; display: inline-block; box-shadow: 0 4px 8px rgba(0,0,0,0.3);"><img src="data:image/jpeg;base64,{encoded}" width="180"></div>', unsafe_allow_html=True)
    st.markdown("<p style='font-size:20px; margin-top:8px;'><b>Funding Agency</b></p>", unsafe_allow_html=True)

with footer_col4:
    st.markdown("""
<div style="font-size: 18px; line-height: 1.8; color: #EEE; background: rgba(0,0,0,0.2); padding: 20px; border-radius: 10px;">
<b style="color: #FFF;">© 2024–2026 Jorge Teixeira / Western Sahara War Archive.</b> All rights reserved.<br>
<i style="color: #CCC;">Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)</i><br><br>
This work is funded by Portuguese national funds through <b>FCT – Fundação para a Ciência e a Tecnologia, I.P.</b>, 
under the Project <b>UIDB/00495/2020</b> (DOI: <a href="https://doi.org/10.54499/UIDB/00495/2020" target="_blank" style="color: #64B5F6; text-decoration: none; font-weight: bold;">10.54499/UIDB/00495/2020</a>).

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""", unsafe_allow_html=True)