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app.py

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+"""
+VUTIA - Sistema de Análisis de Viviendas de Uso Turístico
+Ayuntamiento de Dénia - Plataforma de Análisis Institucional
+"""
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import numpy as np
+from datetime import datetime
+from io import BytesIO
+import os
+
+# Configuración de la página
+st.set_page_config(
+    page_title="VUTIA - Sistema de Análisis VUT",
+    page_icon="📊",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# CSS Profesional Institucional (copiado del original)
+st.markdown("""
+<style>
+    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
+
+    /* VARIABLES */
+    :root {
+        --bg-primary: #0a0e27;
+        --bg-secondary: #141832;
+        --bg-tertiary: #1a1f3a;
+        --bg-card: rgba(20, 24, 50, 0.6);
+        --border-color: rgba(99, 102, 241, 0.2);
+        --border-subtle: rgba(99, 102, 241, 0.1);
+        --text-primary: rgba(255, 255, 255, 0.95);
+        --text-secondary: rgba(255, 255, 255, 0.7);
+        --text-tertiary: rgba(255, 255, 255, 0.5);
+        --accent-primary: #006AA7;
+        --accent-hover: #017CB5;
+        --accent-light: rgba(0, 106, 167, 0.2);
+        --shadow-sm: 0 2px 8px rgba(0, 0, 0, 0.3);
+        --shadow-md: 0 4px 12px rgba(0, 0, 0, 0.4);
+        --shadow-lg: 0 8px 24px rgba(0, 0, 0, 0.5);
+    }
+
+    /* RESET BASE */
+    .stApp {
+        background: linear-gradient(135deg, #0a0e27 0%, #141832 100%);
+        font-family: 'Inter', -apple-system, BlinkMacSystemFont, system-ui, sans-serif;
+        color: var(--text-primary);
+    }
+
+    /* === HEADER INSTITUCIONAL MODERNO Y SOBRIO === */
+    .header-institucional {
+        position: relative;
+        padding: 3.5rem 3rem;
+        background: linear-gradient(135deg,
+            rgba(0, 106, 167, 0.15) 0%,
+            rgba(1, 124, 181, 0.12) 25%,
+            rgba(0, 153, 146, 0.10) 50%,
+            rgba(0, 170, 152, 0.08) 75%,
+            rgba(67, 193, 121, 0.05) 100%);
+        border-bottom: 1px solid rgba(67, 193, 121, 0.2);
+        margin-bottom: 2rem;
+        display: flex;
+        justify-content: space-between;
+        align-items: center;
+        overflow: hidden;
+        backdrop-filter: blur(10px);
+        box-shadow:
+            0 4px 30px rgba(0, 0, 0, 0.3),
+            inset 0 1px 0 rgba(255, 255, 255, 0.05);
+    }
+
+    .header-institucional::before {
+        content: '';
+        position: absolute;
+        top: 0;
+        left: 0;
+        right: 0;
+        bottom: 0;
+        background:
+            linear-gradient(90deg,
+                transparent 0%,
+                rgba(67, 193, 121, 0.03) 50%,
+                transparent 100%),
+            linear-gradient(0deg,
+                rgba(0, 106, 167, 0.08) 0%,
+                transparent 100%);
+        pointer-events: none;
+        z-index: 0;
+    }
+
+    /* Línea de escaneo animada - Efecto sobrio */
+    .header-scan-line {
+        position: absolute;
+        top: 0;
+        left: -100%;
+        width: 100%;
+        height: 2px;
+        background: linear-gradient(90deg,
+            transparent 0%,
+            rgba(67, 193, 121, 0.4) 50%,
+            transparent 100%);
+        animation: scan 6s ease-in-out infinite;
+        z-index: 2;
+        opacity: 0.4;
+    }
+
+    @keyframes scan {
+        0%, 100% { left: -100%; opacity: 0; }
+        50% { left: 100%; opacity: 0.4; }
+    }
+
+    /* Efectos de luz sutiles */
+    .header-light-effect-1,
+    .header-light-effect-2 {
+        position: absolute;
+        width: 500px;
+        height: 160%;
+        pointer-events: none;
+        z-index: 1;
+        animation: glow-pulse-advanced 6s ease-in-out infinite;
+        filter: blur(30px);
+    }
+
+    .header-light-effect-1 {
+        top: -40%;
+        right: -10%;
+        background: radial-gradient(ellipse at center,
+            rgba(67, 193, 121, 0.2) 0%,
+            rgba(0, 170, 152, 0.12) 25%,
+            rgba(139, 182, 64, 0.06) 50%,
+            transparent 70%);
+    }
+
+    .header-light-effect-2 {
+        top: -30%;
+        left: -10%;
+        background: radial-gradient(ellipse at center,
+            rgba(0, 106, 167, 0.18) 0%,
+            rgba(1, 124, 181, 0.10) 25%,
+            rgba(0, 153, 146, 0.05) 50%,
+            transparent 70%);
+        animation-delay: 1s;
+    }
+
+    @keyframes glow-pulse-advanced {
+        0%, 100% { opacity: 0.5; transform: scale(1); }
+        50% { opacity: 0.8; transform: scale(1.08); }
+    }
+
+    /* Contenido del header */
+    .header-content {
+        position: relative;
+        z-index: 3;
+        flex: 1;
+    }
+
+    .header-title {
+        font-size: 2.8rem;
+        font-weight: 900;
+        color: white;
+        margin: 0;
+        letter-spacing: 0.02em;
+        line-height: 1.2;
+        text-shadow:
+            0 0 12px rgba(67, 193, 121, 0.8),
+            0 0 25px rgba(67, 193, 121, 0.6),
+            0 0 40px rgba(0, 170, 152, 0.4),
+            0 4px 20px rgba(0, 0, 0, 0.7);
+        position: relative;
+        animation: text-glow-holographic 4s ease-in-out infinite;
+        filter: drop-shadow(0 0 25px rgba(67, 193, 121, 0.6));
+    }
+
+    @keyframes text-glow-holographic {
+        0%, 100% {
+            text-shadow:
+                0 0 12px rgba(67, 193, 121, 0.8),
+                0 0 25px rgba(67, 193, 121, 0.6),
+                0 0 40px rgba(0, 170, 152, 0.4),
+                0 4px 20px rgba(0, 0, 0, 0.7);
+        }
+        50% {
+            text-shadow:
+                0 0 15px rgba(139, 182, 64, 0.8),
+                0 0 30px rgba(181, 164, 53, 0.6),
+                0 0 50px rgba(67, 193, 121, 0.4),
+                0 4px 20px rgba(0, 0, 0, 0.7);
+        }
+    }
+
+    .header-subtitle {
+        font-size: 1rem;
+        font-weight: 400;
+        color: rgba(255, 255, 255, 0.85);
+        margin: 0.5rem 0 0 0;
+        letter-spacing: 0.05em;
+        text-transform: uppercase;
+        opacity: 0.9;
+    }
+
+    /* Badge VUT Moderno y Sobrio */
+    .header-vut-badge {
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        position: relative;
+        z-index: 4;
+        padding: 1rem 1.8rem;
+        background: linear-gradient(135deg,
+            rgba(0, 0, 0, 0.5) 0%,
+            rgba(0, 0, 0, 0.3) 100%);
+        border-radius: 16px;
+        border: 2px solid rgba(67, 193, 121, 0.4);
+        backdrop-filter: blur(20px);
+        box-shadow:
+            0 8px 25px rgba(0, 0, 0, 0.5),
+            inset 0 1px 0 rgba(255, 255, 255, 0.15);
+        transition: all 0.3s ease;
+    }
+
+    .header-vut-badge:hover {
+        transform: translateY(-2px);
+        box-shadow:
+            0 12px 35px rgba(0, 0, 0, 0.6),
+            inset 0 1px 0 rgba(255, 255, 255, 0.2);
+        border-color: rgba(67, 193, 121, 0.6);
+    }
+
+    .header-vut-text {
+        display: flex;
+        flex-direction: column;
+        align-items: center;
+        gap: 0.2rem;
+    }
+
+    .header-vut-acronym {
+        font-size: 2.8rem;
+        font-weight: 900;
+        color: white;
+        margin: 0;
+        letter-spacing: 0.12em;
+        line-height: 1;
+        text-shadow:
+            0 0 15px rgba(67, 193, 121, 0.8),
+            0 0 30px rgba(139, 182, 64, 0.6),
+            0 0 45px rgba(181, 164, 53, 0.4),
+            0 4px 15px rgba(0, 0, 0, 0.7);
+        animation: vut-glow 5s ease-in-out infinite;
+    }
+
+    @keyframes vut-glow {
+        0%, 100% {
+            text-shadow:
+                0 0 15px rgba(67, 193, 121, 0.8),
+                0 0 30px rgba(139, 182, 64, 0.6),
+                0 0 45px rgba(181, 164, 53, 0.4),
+                0 4px 15px rgba(0, 0, 0, 0.7);
+        }
+        50% {
+            text-shadow:
+                0 0 20px rgba(139, 182, 64, 0.9),
+                0 0 40px rgba(67, 193, 121, 0.7),
+                0 0 60px rgba(0, 170, 152, 0.5),
+                0 4px 15px rgba(0, 0, 0, 0.7);
+        }
+    }
+
+    .header-vut-subtext {
+        font-size: 0.75rem;
+        font-weight: 500;
+        color: rgba(255, 255, 255, 0.7);
+        letter-spacing: 0.1em;
+        text-transform: uppercase;
+        margin: 0;
+    }
+
+    /* Resto del CSS original... */
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    header {visibility: hidden;}
+</style>
+""", unsafe_allow_html=True)
+
+# Colores corporativos
+COLORS = {
+    'VUT_CONFIRMADA': '#006AA7',
+    'VUT_POSIBLE': '#017CB5',
+    'VUT_BAJACONFIANZA_NOREGLADA': '#009992',
+    'HABITUAL': '#00AA98',
+    'SEGUNDA_RESIDENCIA': '#43C179',
+    'VACIA': '#8BB640'
+}
+
+def get_chart_layout():
+    return {
+        'paper_bgcolor': 'rgba(20, 24, 50, 0.5)',
+        'plot_bgcolor': 'rgba(26, 31, 58, 0.5)',
+        'font': {'family': 'Inter', 'size': 12, 'color': 'white'},
+        'margin': {'t': 40, 'r': 20, 'b': 40, 'l': 50},
+        'xaxis': {'gridcolor': 'rgba(67, 193, 121, 0.1)', 'color': 'white'},
+        'yaxis': {'gridcolor': 'rgba(67, 193, 121, 0.1)', 'color': 'white'}
+    }
+
+@st.cache_data
+def cargar_datos(uploaded_file=None):
+    """Carga datos desde archivo subido o usa datos de demostración"""
+    if uploaded_file is not None:
+        try:
+            df = pd.read_excel(uploaded_file)
+            return df
+        except Exception as e:
+            st.error(f"Error al cargar el archivo: {e}")
+            return None
+    else:
+        # Datos de demostración
+        st.info("📊 Usando datos de demostración. Sube tu archivo Excel para ver tus datos reales.")
+        return crear_datos_demo()
+
+def crear_datos_demo():
+    """Crea datos de demostración para pruebas"""
+    np.random.seed(42)
+    n_samples = 1000
+
+    categorias = ['VUT_CONFIRMADA', 'VUT_POSIBLE', 'VUT_BAJACONFIANZA_NOREGLADA',
+                  'HABITUAL', 'SEGUNDA_RESIDENCIA', 'VACIA']
+    barrios = ['Centro', 'Les Rotes', 'Les Marines', 'La Xara', 'Jesus Pobre',
+               'Els Poblets', 'Dénia Nord', 'Dénia Sud']
+
+    data = {
+        'Direccion': [f'Calle Demo {i}' for i in range(n_samples)],
+        'Barrio': np.random.choice(barrios, n_samples),
+        'Categoria': np.random.choice(categorias, n_samples,
+                                      p=[0.15, 0.20, 0.15, 0.25, 0.15, 0.10]),
+        'Confianza': np.random.uniform(0.3, 1.0, n_samples),
+        'Consumo_Total_Periodo_m3': np.random.uniform(50, 500, n_samples),
+        'Ratio_Verano_Invierno': np.random.uniform(0.5, 3.0, n_samples)
+    }
+
+    return pd.DataFrame(data)
+
+# Header institucional
+st.markdown("""
+<div class="header-institucional">
+    <div class="header-scan-line"></div>
+    <div class="header-light-effect-1"></div>
+    <div class="header-light-effect-2"></div>
+    <div class="header-content">
+        <div class="header-title">VUTIA</div>
+        <div class="header-subtitle">Sistema de Análisis de Viviendas de Uso Turístico</div>
+    </div>
+    <div class="header-vut-badge">
+        <div class="header-vut-text">
+            <div class="header-vut-acronym">VUT</div>
+            <div class="header-vut-subtext">Sistema de Análisis</div>
+        </div>
+    </div>
+</div>
+""", unsafe_allow_html=True)
+
+# Subida de archivo
+with st.sidebar:
+    st.markdown("### 📁 CARGAR DATOS")
+    uploaded_file = st.file_uploader(
+        "Sube tu archivo Excel",
+        type=['xlsx', 'xls'],
+        help="Archivo con datos de clasificación VUT"
+    )
+    st.markdown("---")
+
+# Cargar datos
+df = cargar_datos(uploaded_file)
+
+if df is None:
+    st.error("No se pudieron cargar los datos")
+    st.stop()
+
+# SIDEBAR DE FILTROS
+with st.sidebar:
+    st.markdown("### 🔍 FILTROS")
+
+    categorias = df['Categoria'].unique().tolist()
+    cat_sel = st.multiselect("Categorías", categorias, default=categorias)
+
+    barrios = ['Todos'] + sorted(df['Barrio'].unique().tolist())
+    barrio_sel = st.selectbox("Barrio", barrios)
+
+    confianza_min = st.slider("Confianza Mínima (%)", 0, 100, 0, 5)
+
+    # Aplicar filtros
+    df_filtrado = df.copy()
+    if cat_sel:
+        df_filtrado = df_filtrado[df_filtrado['Categoria'].isin(cat_sel)]
+    if barrio_sel != 'Todos':
+        df_filtrado = df_filtrado[df_filtrado['Barrio'] == barrio_sel]
+    df_filtrado = df_filtrado[df_filtrado['Confianza'] >= confianza_min / 100]
+
+    # Estadísticas
+    st.markdown("### ESTADÍSTICAS")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.metric("Filtradas", f"{len(df_filtrado):,}")
+    with col2:
+        st.metric("Total", f"{len(df):,}")
+
+    if len(df_filtrado) < len(df):
+        pct = (len(df_filtrado) / len(df)) * 100
+        st.caption(f"Mostrando {pct:.1f}% del total")
+
+    st.markdown("---")
+    st.caption("**VUTIA v3.0**")
+    st.caption("© 2026 Ayuntamiento de Dénia")
+
+# TABS
+tab1, tab2, tab3, tab4 = st.tabs([
+    "Panel General",
+    "Análisis VUT",
+    "Datos",
+    "Exportación"
+])
+
+# TAB 1: PANEL GENERAL
+with tab1:
+    st.markdown("## Panel General")
+    st.markdown("")
+
+    # Métricas principales
+    col1, col2, col3, col4, col5, col6 = st.columns(6)
+
+    with col1:
+        n = len(df_filtrado[df_filtrado['Categoria'] == 'VUT_CONFIRMADA'])
+        st.metric("VUT Confirmada", f"{n:,}",
+                 f"{(n/len(df_filtrado)*100):.1f}%" if len(df_filtrado) > 0 else "0%")
+
+    with col2:
+        n = len(df_filtrado[df_filtrado['Categoria'] == 'VUT_POSIBLE'])
+        st.metric("VUT Posible", f"{n:,}",
+                 f"{(n/len(df_filtrado)*100):.1f}%" if len(df_filtrado) > 0 else "0%")
+
+    with col3:
+        n = len(df_filtrado[df_filtrado['Categoria'] == 'VUT_BAJACONFIANZA_NOREGLADA'])
+        st.metric("VUT Baja Conf.", f"{n:,}",
+                 f"{(n/len(df_filtrado)*100):.1f}%" if len(df_filtrado) > 0 else "0%")
+
+    with col4:
+        n = len(df_filtrado[df_filtrado['Categoria'] == 'HABITUAL'])
+        st.metric("Habitual", f"{n:,}",
+                 f"{(n/len(df_filtrado)*100):.1f}%" if len(df_filtrado) > 0 else "0%")
+
+    with col5:
+        n = len(df_filtrado[df_filtrado['Categoria'] == 'SEGUNDA_RESIDENCIA'])
+        st.metric("Segunda Res.", f"{n:,}",
+                 f"{(n/len(df_filtrado)*100):.1f}%" if len(df_filtrado) > 0 else "0%")
+
+    with col6:
+        n = len(df_filtrado[df_filtrado['Categoria'] == 'VACIA'])
+        st.metric("Vacía", f"{n:,}",
+                 f"{(n/len(df_filtrado)*100):.1f}%" if len(df_filtrado) > 0 else "0%")
+
+    st.markdown("---")
+
+    # Gráfico de distribución
+    st.markdown("### Distribución por Categorías")
+    dist = df_filtrado['Categoria'].value_counts()
+
+    fig = go.Figure(data=[go.Pie(
+        labels=dist.index,
+        values=dist.values,
+        hole=.45,
+        marker=dict(
+            colors=[COLORS.get(c, '#017CB5') for c in dist.index],
+            line=dict(color='rgba(10, 14, 39, 0.8)', width=3)
+        ),
+        textfont=dict(size=12, family='Inter', color='white'),
+        hovertemplate='<b>%{label}</b><br>%{value:,} viviendas<br>%{percent}<extra></extra>'
+    )])
+
+    layout = get_chart_layout()
+    layout['height'] = 400
+    fig.update_layout(layout)
+
+    st.plotly_chart(fig, use_container_width=True)
+
+# TAB 2: ANÁLISIS VUT
+with tab2:
+    st.markdown("## Análisis Detallado de VUT")
+
+    vut_categories = ['VUT_CONFIRMADA', 'VUT_POSIBLE', 'VUT_BAJACONFIANZA_NOREGLADA']
+    df_vut = df_filtrado[df_filtrado['Categoria'].isin(vut_categories)]
+
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.metric("Total VUT", f"{len(df_vut):,}")
+    with col2:
+        st.metric("Confirmadas", f"{len(df_vut[df_vut['Categoria']=='VUT_CONFIRMADA']):,}")
+    with col3:
+        avg_conf = df_vut['Confianza'].mean() * 100 if len(df_vut) > 0 else 0
+        st.metric("Confianza Media", f"{avg_conf:.1f}%")
+
+    st.markdown("---")
+
+    # Top 20 VUT
+    st.markdown("### Top 20 VUT por Confianza")
+    top20 = df_vut.nlargest(20, 'Confianza')
+
+    for idx, row in top20.iterrows():
+        with st.container():
+            col1, col2, col3, col4 = st.columns([3, 2, 2, 2])
+            with col1:
+                st.write(f"**{row['Direccion']}**")
+            with col2:
+                st.write(f"Barrio: {row['Barrio']}")
+            with col3:
+                st.write(f"Categoría: {row['Categoria']}")
+            with col4:
+                st.write(f"Confianza: {row['Confianza']*100:.1f}%")
+            st.markdown("---")
+
+# TAB 3: DATOS
+with tab3:
+    st.markdown("## Datos Detallados")
+
+    # Búsqueda
+    busqueda = st.text_input("🔍 Buscar por dirección o barrio")
+
+    df_mostrar = df_filtrado.copy()
+    if busqueda:
+        mask = (df_mostrar['Direccion'].str.contains(busqueda, case=False, na=False) |
+                df_mostrar['Barrio'].str.contains(busqueda, case=False, na=False))
+        df_mostrar = df_mostrar[mask]
+
+    st.dataframe(
+        df_mostrar[['Direccion', 'Barrio', 'Categoria', 'Confianza', 'Consumo_Total_Periodo_m3']],
+        use_container_width=True,
+        height=600
+    )
+
+    st.caption(f"Mostrando {len(df_mostrar):,} de {len(df_filtrado):,} viviendas")
+
+# TAB 4: EXPORTACIÓN
+with tab4:
+    st.markdown("## Exportación de Datos")
+
+    st.markdown("### Descargar Datos Filtrados")
+
+    # Convertir a Excel
+    buffer = BytesIO()
+    with pd.ExcelWriter(buffer, engine='openpyxl') as writer:
+        df_filtrado.to_excel(writer, index=False, sheet_name='Datos')
+    buffer.seek(0)
+
+    st.download_button(
+        label="📊 Descargar Excel",
+        data=buffer,
+        file_name=f"vutia_datos_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx",
+        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+    )
+
+    # Convertir a CSV
+    csv = df_filtrado.to_csv(index=False).encode('utf-8')
+
+    st.download_button(
+        label="📄 Descargar CSV",
+        data=csv,
+        file_name=f"vutia_datos_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
+        mime="text/csv"
+    )