Delete app

app/Dockerfile

@@ -1,18 +0,0 @@
-# Use Python image
-FROM python:3.9
-
-# Set the working directory inside the container
-WORKDIR /code
-
-# Copy the requirements file from the root
-COPY ./requirements.txt /code/requirements.txt
-
-# Install dependencies
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-# Copy all files and folders (including 'pages' and 'assets')
-COPY . /code
-
-# Start the app using Gunicorn on port 7860
-# This assumes your main file is 'app.py' and it contains 'server = app.server'
-CMD ["gunicorn", "-b", "0.0.0.0:7860", "app:server"]

app/Procfile

@@ -1 +0,0 @@
-web: gunicorn app:server

app/app.py

@@ -1,85 +0,0 @@
-import dash
-from dash import html
-import dash_bootstrap_components as dbc
-
-# Initialize the App
-app = dash.Dash(
-    __name__,
-    use_pages=True,
-    external_stylesheets=[dbc.themes.DARKLY, dbc.icons.BOOTSTRAP],
-    external_scripts=[
-        "https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"
-    ],
-    suppress_callback_exceptions=True
-)
-
-server = app.server
-
-# Fixed Sidebar with Nebula Icon
-sidebar = html.Div([
-    html.Div([
-        # Nebula Icon with Gradient and Glow
-        html.Div([
-            html.I(className="bi bi-hurricane fs-2", style={"color": "#00d2ff"})
-        ], style={
-            "width": "45px", "height": "45px", 
-            "display": "flex", "alignItems": "center", "justifyContent": "center",
-            "background": "rgba(255,255,255,0.05)", 
-            "borderRadius": "12px",
-            "boxShadow": "0 0 20px rgba(0, 210, 255, 0.3)",
-            "border": "1px solid rgba(0, 210, 255, 0.2)"
-        }, className="nebula-icon-spin"),
-        
-        html.H3("ASTRO.AI", className="ms-3 mb-0 fw-bold text-white", style={"letterSpacing": "2px"}),
-    ], className="d-flex align-items-center mb-5"),
-    
-    dbc.Nav([
-        dbc.NavLink([html.I(className="bi bi-stars me-3"), "Introduction"], href="/", active="exact", className="mb-2 nav-link-custom"),
-        dbc.NavLink([html.I(className="bi bi-eye me-3"), "Redshift Phenomenon"], href="/phenomenon", active="exact", className="mb-2 nav-link-custom"),
-        dbc.NavLink([html.I(className="bi bi-activity me-3"), "Spectroscopy"], href="/spectroscopy", active="exact", className="mb-2 nav-link-custom"),
-        dbc.NavLink([html.I(className="bi bi-camera me-3"), "Photometry"], href="/photometry", active="exact", className="mb-2 nav-link-custom"),       
-        dbc.NavLink([html.I(className="bi bi-database me-3"), "Project Data"], href="/data", active="exact", className="mb-2 nav-link-custom"),
-        dbc.NavLink([html.I(className="bi bi-graph-up-arrow me-3"), "Data Analysis"], href="/analysis", active="exact", className="mb-2 nav-link-custom"),
-        dbc.NavLink([html.I(className="bi bi-cpu-fill me-3"), "Redshift Engine"], href="/prediction", active="exact", className="mb-2 nav-link-custom"),
-    ], vertical=True, pills=True),
-], className="sidebar-container")
-
-# Professional Styled Copyright Footer Component
-footer = html.Footer(
-    dbc.Container(
-        dbc.Row([
-            dbc.Col(
-                html.Hr(style={"borderTop": "1px solid rgba(255, 255, 255, 0.1)", "marginBottom": "20px"}),
-                width=12
-            ),
-            dbc.Col(
-                html.P(
-                    [
-                        html.Span("© 2026 ", className="text-muted"),
-                        html.Span("Latreche Sara", className="text-info fw-bold", style={"letterSpacing": "1px"}),
-                        html.Span(" | All Space-Time Coordinates Reserved.", className="text-muted ms-1")
-                    ], 
-                    className="small text-center mb-0 pb-3"
-                ),
-                width=12
-            )
-        ]),
-        fluid=True,
-    ),
-    style={"marginTop": "auto"} # Automatically pushes footer down if content is short
-)
-
-# Main Layout
-app.layout = html.Div([
-    html.Div(className="bg-glow"), 
-    sidebar,
-    
-    # Content wrapper converted to flex-column to manage page-bottom pinning smoothly
-    html.Div([
-        dash.page_container,
-        footer
-    ], className="content-container d-flex flex-column", style={"minHeight": "100vh"})
-])
-
-if __name__ == "__main__":
-    app.run(debug=True)

app/assets/galaxy.js

@@ -1,55 +0,0 @@
-// assets/galaxy.js
-window.onload = function() {
-    const canvas = document.createElement('canvas');
-    canvas.id = 'galaxy-canvas';
-    document.body.appendChild(canvas);
-    const ctx = canvas.getContext('2d');
-
-    let stars = [], width, height;
-    const starCount = 400;
-
-    function init() {
-        width = window.innerWidth;
-        height = window.innerHeight;
-        canvas.width = width;
-        canvas.height = height;
-        stars = [];
-        for (let i = 0; i < starCount; i++) {
-            stars.push({
-                x: Math.random() * width - width / 2,
-                y: Math.random() * height - height / 2,
-                z: Math.random() * width,
-                o: Math.random()
-            });
-        }
-    }
-
-    function draw() {
-        ctx.fillStyle = "black";
-        ctx.fillRect(0, 0, width, height);
-        
-        ctx.save();
-        ctx.translate(width / 2, height / 2);
-        
-        for (let i = 0; i < starCount; i++) {
-            let s = stars[i];
-            let x = s.x / (s.z / width);
-            let y = s.y / (s.z / width);
-            let r = (1 - s.z / width) * 2;
-            
-            ctx.beginPath();
-            ctx.arc(x, y, r, 0, Math.PI * 2);
-            ctx.fillStyle = `rgba(255, 255, 255, ${1 - s.z / width})`;
-            ctx.fill();
-            
-            s.z -= 2; // Star speed
-            if (s.z <= 0) s.z = width;
-        }
-        ctx.restore();
-        requestAnimationFrame(draw);
-    }
-
-    init();
-    draw();
-    window.addEventListener('resize', init);
-};

app/assets/style.css

@@ -1,72 +0,0 @@
-/* assets/style.css */
-#galaxy-canvas {
-    position: fixed;
-    top: 0; left: 0;
-    z-index: -2;
-    pointer-events: none;
-}
-
-/* SaaS Background Glow */
-.bg-glow {
-    position: fixed;
-    top: -10%; left: -10%;
-    width: 50%; height: 50%;
-    background: radial-gradient(circle, rgba(157, 80, 187, 0.1) 0%, transparent 70%);
-    filter: blur(150px);
-    z-index: -1;
-    pointer-events: none;
-}
-
-/* Sidebar - Fixed and Glassy */
-.sidebar-container {
-    position: fixed;
-    top: 0; left: 0; bottom: 0; 
-    width: 18rem;
-    background: rgba(8, 9, 12, 0.95);
-    backdrop-filter: blur(30px);
-    border-right: 1px solid rgba(0, 210, 255, 0.15);
-    padding: 3rem 1.5rem;
-    z-index: 1000;
-}
-
-/* Push content to clear sidebar */
-.content-container {
-    margin-left: 18rem; 
-    padding: 3rem 5rem;
-    min-height: 100vh;
-}
-
-/* Nebula Spinning Animation */
-.nebula-icon-spin i {
-    animation: rotate-nebula 10s linear infinite;
-}
-
-@keyframes rotate-nebula {
-    from { transform: rotate(0deg); }
-    to { transform: rotate(360deg); }
-}
-
-/* Glass Cards */
-.modern-card {
-    background: rgba(255, 255, 255, 0.02) !important;
-    backdrop-filter: blur(20px);
-    border: 1px solid rgba(255, 255, 255, 0.08) !important;
-    border-radius: 28px !important;
-    box-shadow: 0 25px 50px rgba(0, 0, 0, 0.5);
-    transition: 0.5s cubic-bezier(0.2, 0.8, 0.2, 1);
-}
-
-.modern-card:hover {
-    border: 1px solid rgba(37, 212, 252, 0.4) !important;
-    transform: scale(1.02);
-}
-
-/* Glowing Typography */
-.prediction-value {
-    font-size: 5.5rem;
-    font-weight: 900;
-    background: linear-gradient(135deg, #00d2ff 0%, #9d50bb 100%);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-    filter: drop-shadow(0 0 20px rgba(0, 210, 255, 0.4));
-}

app/models/xgb_production_pipeline.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6666c876ef1909463cb075c3e3accf91769c0a1d13af298b5c7b7a5a3489b2c0
-size 583260

app/pages/analysis.py

@@ -1,175 +0,0 @@
-import dash
-from dash import html, dcc, Input, Output, callback
-import dash_bootstrap_components as dbc
-import pandas as pd
-import plotly.express as px
-import os
-
-dash.register_page(__name__, path='/analysis')
-
-# --- DATA LOADING LOGIC ---
-# Using absolute paths to ensure the app finds the CSVs inside the /pages folder
-current_dir = os.path.dirname(__file__)
-features_path = os.path.join(current_dir, 'sel_features.csv')
-target_path = os.path.join(current_dir, 'sel_target.csv')
-
-try:
-    # sel_features is tab-separated based on your Jupyter logic
-    df_features = pd.read_csv(features_path, sep='\t')
-    # sel_target is comma-separated
-    df_target = pd.read_csv(target_path)
-    
-    # Merge for multivariate analysis
-    df = pd.concat([df_features, df_target], axis=1)
-    
-    # List of bands for dropdowns and melting
-    band_columns = [col for col in df_features.columns]
-    data_loaded = True
-except Exception as e:
-    print(f"Error loading data: {e}")
-    data_loaded = False
-
-# --- LAYOUT ---
-layout = html.Div([
-    html.Div([
-        html.H1("Data Analysis: Exploring the Galaxy Sample", className="text-white fw-bold mb-2"),
-        html.P("Multivariate Analysis: Distributions, Correlations, and Ranges", className="lead text-info"),
-    ], className="mb-5"),
-
-    # SECTION 1: VIOLIN PLOT (Multicolored)
-    dbc.Row([
-        dbc.Col([
-            dbc.Card([
-                dbc.CardBody([
-                    html.H4("Photometric Band Distributions", className="text-info mb-3"),
-                    html.P("Comparative density and magnitude ranges for all filter bands (u, g, r, i, z, y).", 
-                           className="text-muted small"),
-                    dcc.Graph(id='violin-plot'),
-                ])
-            ], className="modern-card mb-4"),
-        ], width=12)
-    ]),
-
-    # SECTION 2: TARGET DISTRIBUTION (Viridis Purple)
-    dbc.Row([
-        dbc.Col([
-            dbc.Card([
-                dbc.CardBody([
-                    html.H4("Redshift Distribution (zhelio)", className="text-info mb-3"),
-                    html.P("Ground-truth redshift distribution from DEEP2/3 and 3D-HST surveys.", 
-                           className="text-muted small"),
-                    dcc.Graph(
-                        id='zhelio-dist',
-                        figure=px.histogram(
-                            df, x="zhelio", nbins=50, 
-                            template="plotly_dark",
-                            labels={'zhelio': 'True Redshift (z)'},
-                            color_discrete_sequence=['#440154'] # Deep Purple from Viridis
-                        ).update_layout(
-                            paper_bgcolor='rgba(0,0,0,0)', 
-                            plot_bgcolor='rgba(0,0,0,0)',
-                        ) if data_loaded else {}
-                    )
-                ])
-            ], className="modern-card mb-4"),
-        ], width=12),
-    ]),
-
-    # SECTION 3: CORRELATION HEATMAP (Viridis Palette)
-    dbc.Row([
-        dbc.Col([
-            dbc.Card([
-                dbc.CardBody([
-                    html.H4("Feature Correlation Matrix", className="text-info mb-3"),
-                    html.P("Mathematical relationship between photometric features and the target redshift.", 
-                           className="text-muted small"),
-                    dcc.Graph(id='correlation-heatmap'),
-                ])
-            ], className="modern-card mb-4"),
-        ], width=12),
-    ]),
-
-    # SECTION 4: FEATURE ANALYSIS (Interactive Scatter)
-    dbc.Row([
-        dbc.Col([
-            dbc.Card([
-                dbc.CardBody([
-                    html.H5("Feature Analysis", className="text-info mb-3"),
-                    html.Label("Select Photometric Band to Analyze:", className="text-light"),
-                    dcc.Dropdown(
-                        id='band-selector',
-                        options=[{'label': b, 'value': b} for b in band_columns] if data_loaded else [],
-                        value=band_columns[0] if data_loaded else None,
-                        className="mb-4", 
-                        style={'color': '#000'}
-                    ),
-                    dcc.Graph(id='redshift-scatter-plot'),
-                ])
-            ], className="modern-card mb-4"),
-        ], width=12),
-    ]),
-])
-
-# --- CALLBACKS ---
-
-# 1. Callback for Multicolored Violin Plot
-@callback(
-    Output('violin-plot', 'figure'),
-    Input('band-selector', 'value')
-)
-def update_violin(_):
-    if not data_loaded: return {}
-    df_long = pd.melt(df, value_vars=band_columns, var_name='Band', value_name='Magnitude')
-    fig = px.violin(
-        df_long, x='Band', y='Magnitude', color='Band',
-        box=True, points="all", template="plotly_dark",
-        color_discrete_sequence=px.colors.qualitative.Vivid # Distinct colors per band
-    )
-    fig.update_layout(
-        paper_bgcolor='rgba(0,0,0,0)', 
-        plot_bgcolor='rgba(0,0,0,0)', 
-        showlegend=False,
-        yaxis_title="Magnitude (Brightness)"
-    )
-    return fig
-
-# 2. Callback for Viridis Heatmap
-@callback(
-    Output('correlation-heatmap', 'figure'),
-    Input('band-selector', 'value')
-)
-def update_heatmap(_):
-    if not data_loaded: return {}
-    corr = df.corr()
-    fig = px.imshow(
-        corr, text_auto=".2f",
-        color_continuous_scale='Viridis',
-        template="plotly_dark"
-    )
-    fig.update_layout(
-        paper_bgcolor='rgba(0,0,0,0)', 
-        plot_bgcolor='rgba(0,0,0,0)',
-        coloraxis_colorbar=dict(title="Correlation")
-    )
-    return fig
-
-# 3. Callback for Viridis Scatter Plot
-@callback(
-    Output('redshift-scatter-plot', 'figure'),
-    Input('band-selector', 'value')
-)
-def update_scatter(selected_band):
-    if not data_loaded or not selected_band: return {}
-    fig = px.scatter(
-        df, x=selected_band, y="zhelio", 
-        color="zhelio",
-        color_continuous_scale='Viridis', 
-        template="plotly_dark",
-        labels={selected_band: f"Magnitude ({selected_band})", "zhelio": "True Redshift (z)"}
-    )
-    fig.update_layout(
-        paper_bgcolor='rgba(0,0,0,0)', 
-        plot_bgcolor='rgba(0,0,0,0)',
-        font=dict(color="white")
-    )
-    return fig

app/pages/data.py

@@ -1,83 +0,0 @@
-import dash
-from dash import html, dcc, dash_table
-import dash_bootstrap_components as dbc
-
-dash.register_page(__name__, path='/data')
-
-# Data preview based on the uploaded CSV snapshots
-feature_data = [
-    {"u": 24.2444, "g": 23.9796, "r": 23.5221, "i": 22.9110, "z": 22.5258, "y": 22.3291},
-    {"u": 24.4891, "g": 23.9162, "r": 22.9237, "i": 21.8738, "z": 21.3065, "y": 21.2514},
-    {"u": 24.8740, "g": 22.9739, "r": 21.4659, "i": 20.7884, "z": 20.4623, "y": 20.4137},
-    {"u": 24.5290, "g": 24.3386, "r": 23.8912, "i": 23.2061, "z": 22.9893, "y": 23.1124},
-    {"u": 23.6412, "g": 23.3874, "r": 22.9753, "i": 22.2352, "z": 21.8097, "y": 21.5595},
-]
-
-target_data = [
-    {"zhelio": 1.0034}, {"zhelio": 0.9023}, {"zhelio": 0.4242}, 
-    {"zhelio": 0.7690}, {"zhelio": 0.9910}
-]
-
-layout = html.Div([
-    html.Div([
-        html.H1("Data: The Building Blocks", className="text-white fw-bold mb-2"),
-        html.P("Project Foundation and Dataset Structure", className="lead text-info"),
-    ], className="mb-5"),
-
-    dbc.Card([
-        dbc.CardBody([
-            html.H3("Dataset Origins", className="text-white mb-4"),
-            
-            html.Div([
-                html.P([
-                    "Our project is inspired by the work of ", 
-                    html.A("Zhou et al. (2019)", href="https://arxiv.org/abs/1903.08174", target="_blank", className="text-info font-weight-bold"),
-                    ", who made the data available for the purpose of building redshift forecasting algorithms.",
-                ], className="text-light mb-3"),
-                
-                html.P([
-                    "The target data resembles the wavelength coverage and depth of the upcoming ",
-                    html.B("Vera Rubin Observatory"), " (formerly known as the Large Synoptic Survey Telescope). ",
-                    "The goal is to build a model capable of handling data across six bands, ranging from near-ultraviolet to near-infrared (u, g, r, i, z, and y)."
-                ], className="text-light mb-4"),
-            ], className="border-start border-info border-4 ps-4 mb-5"),
-
-            # Data Preview Tables
-            dbc.Row([
-                dbc.Col([
-                    html.H5("Photometric Features (sel_features.csv)", className="text-info mb-3"),
-                    dash_table.DataTable(
-                        data=feature_data,
-                        columns=[{"name": i, "id": i} for i in ["u", "g", "r", "i", "z", "y"]],
-                        style_header={'backgroundColor': '#1a1a1a', 'color': '#00d2ff', 'fontWeight': 'bold'},
-                        style_cell={'backgroundColor': '#2b2b2b', 'color': 'white', 'textAlign': 'center', 'border': '1px solid #444'},
-                    )
-                ], width=12, lg=8),
-                dbc.Col([
-                    html.H5("Target Redshift (sel_target.csv)", className="text-success mb-3"),
-                    dash_table.DataTable(
-                        data=target_data,
-                        columns=[{"name": "zhelio", "id": "zhelio"}],
-                        style_header={'backgroundColor': '#1a1a1a', 'color': '#28a745', 'fontWeight': 'bold'},
-                        style_cell={'backgroundColor': '#2b2b2b', 'color': 'white', 'textAlign': 'center', 'border': '1px solid #444'},
-                    )
-                ], width=12, lg=4),
-            ], className="mb-5"),
-
-            # Resource Links Section
-            html.Div([
-                html.H4("Resources & Technical Papers", className="text-info mb-3"),
-                html.Ul([
-                    html.Li([
-                        html.B("Data Repository: "), 
-                        html.A("Zhou et al. (Pittsburgh Repository)", href="http://d-scholarship.pitt.edu/36064/", target="_blank", className="ms-2 text-info")
-                    ], className="text-light mb-2"),
-                    html.Li([
-                        html.B("Technical Paper: "), 
-                        html.A("[1903.08174] Deep ugrizY Imaging and DEEP2/3 Spectroscopy", href="https://arxiv.org/abs/1903.08174", target="_blank", className="ms-2 text-info")
-                    ], className="text-light"),
-                ], style={"listStyleType": "none", "paddingLeft": "0"})
-            ], className="p-4 rounded-4", style={"background": "rgba(255, 255, 255, 0.02)", "border": "1px solid rgba(255, 255, 255, 0.1)"})
-        ])
-    ], className="modern-card p-4"),
-])

app/pages/introduction.py

@@ -1,77 +0,0 @@
-import dash
-from dash import html, dcc
-import dash_bootstrap_components as dbc
-
-dash.register_page(__name__, path='/')
-
-# Helper function for neat, styled filter badges
-def filter_badge(label):
-    return html.Span(label, style={
-        "background": "rgba(0, 210, 255, 0.1)",
-        "border": "1px solid rgba(0, 210, 255, 0.3)",
-        "padding": "2px 8px",
-        "borderRadius": "6px",
-        "fontSize": "0.9rem",
-        "margin": "0 2px",
-        "color": "#00d2ff",
-        "fontFamily": "monospace"
-    })
-
-layout = html.Div([
-    # Hero Section
-    html.Div([
-        html.H1("Project Mission", className="text-white fw-bold mb-2"),
-        html.P("Mapping the Universe through Ensemble Learning & Photometry", 
-               className="lead text-info", style={"letterSpacing": "1px"}),
-    ], className="mb-5"),
-
-    # Main Glass Card
-    dbc.Card([
-        dbc.CardBody([
-            # Engaging Narrative Introduction
-            html.Div([
-                html.H2("Mapping the Invisible: From Light to Data", 
-                        className="text-white fw-bold mb-3", style={"letterSpacing": "1px"}),
-                html.P([
-                    "The objective is ambitious: to create a three-dimensional map of our Universe. "
-                    "But how do we measure depth on a celestial vault that appears in 2D? "
-                    "The answer resides in the correlation between distance and time—the ",
-                    html.B("'lookback time'"),
-                    "—and the use of advanced algorithms to decode the light of distant galaxies."
-                ], className="text-light mb-5", style={"fontSize": "1.2rem", "lineHeight": "1.8", "fontStyle": "italic"}),
-            ]),
-
-            html.H3("Scientific Context", className="text-white mb-4"),
-            
-            html.P([
-                "Modern extragalactic astronomy relies on the ",
-                html.B("redshift"),
-                " phenomenon to map the three-dimensional structure of the Universe. While spectroscopic methods provide reference-grade precision, their observational cost limits the scale of data we can collect."
-            ], className="text-light mb-3", style={"fontSize": "1.1rem"}),
-
-            html.P([
-                "The upcoming ", html.B("Vera Rubin Observatory (LSST)"), 
-                " will bridge this gap by capturing approximately ", html.B("20 billion galaxies"), 
-                ". It utilizes a sophisticated photometric system across six distinct bands: ",
-                filter_badge("u"), filter_badge("g"), filter_badge("r"), 
-                filter_badge("i"), filter_badge("z"), " and ", filter_badge("y"), "."
-            ], className="text-light mb-4", style={"fontSize": "1.1rem", "lineHeight": "1.8"}),
-
-            # Methodological Section
-            html.Div([
-                html.H4("Our Approach", className="text-info mt-2 mb-3"),
-                html.P([
-                    "This engine optimizes redshift estimation by training on high-fidelity data from the ",
-                    html.Span("DEEP2, DEEP3, and 3D-HST", className="text-white fw-bold"),
-                    " surveys. We implement a comparative framework between ",
-                    html.Span("Bagging", className="text-white fw-bold"), " and ",
-                    html.Span("Boosting", className="text-white fw-bold"),
-                    " algorithms to ensure maximum predictive accuracy for the next generation of sky surveys."
-                ], className="text-light"),
-            ], className="p-4 rounded-4", style={
-                "background": "rgba(255, 255, 255, 0.03)", 
-                "borderLeft": "4px solid #9d50bb"
-            })
-        ])
-    ], className="modern-card p-4"),
-])

app/pages/phenomenon.py

@@ -1,102 +0,0 @@
-import dash
-from dash import html, dcc
-import dash_bootstrap_components as dbc
-
-# This must be in a separate file in the 'pages' folder
-dash.register_page(__name__, path='/phenomenon')
-
-layout = html.Div([
-    html.Div([
-        html.H1("The Physics of Redshift", className="text-white fw-bold mb-2"),
-        html.P("Decoding the expansion of Spacetime", className="lead text-info"),
-    ], className="mb-5"),
-
-    dbc.Card([
-        dbc.CardBody([
-            # Section 1: The 3D Celestial Sphere
-            html.H3("The 3D Celestial Sphere", className="text-white mb-4"),
-            html.P([
-                "For centuries, astronomers perceived stars and galaxies as being fixed on a two-dimensional sphere surrounding the Earth. ",
-                html.B("Redshift is the key that shatters this illusion."), " Because light travels at a finite and constant speed, the distance of an object can be interpreted as ",
-                html.I("lookback time"), ". Measuring redshift allows us to reconstruct the evolutionary history of the Universe in 'time slices'."
-            ], className="text-light mb-4"),
-
-            # The visual anchor
-            html.Div([
-                html.Img(
-                    src=dash.get_asset_url('redshit.png'), 
-                    style={
-                        "width": "100%", "maxHeight": "380px", "objectFit": "contain",
-                        "borderRadius": "24px", "marginBottom": "30px",
-                        "border": "1px solid rgba(0, 210, 255, 0.2)"
-                    }
-                ),
-            ], className="text-center"),
-
-            # Section 2: Hubble's Law
-            html.H3("Hubble's Law and the Stretching of Space", className="text-white mb-4"),
-            html.P([
-                "Redshift relies on a fundamental observation: the further away a galaxy is from us, the faster it appears to be receding. ",
-                "This phenomenon, known as ", html.B("Hubble's Law"), ", is not due to a simple movement of galaxies through space, but to the expansion of the Universe itself."
-            ], className="text-light mb-4"),
-
-            
-
-            html.P([
-                "As the light from a distant galaxy travels toward us, the space it traverses stretches, which literally lengthens the wavelength of the light. ",
-                "Since red represents the low-frequency (long wavelength) end of the visible spectrum, this stretching makes the light appear 'redder' than when it was originally emitted."
-            ], className="text-light mb-4"),
-
-            html.P([
-                "Mathematically, the redshift (z) is defined by the ratio between the observed wavelength (", 
-                html.Span("λobs", style={"fontStyle": "italic"}), ") and the emitted one:"
-            ], className="text-light mb-4"),
-
-            # Equation 6.4 - Redshift Definition
-            dcc.Markdown(
-                r"""
-                $$1 + z = \frac{\lambda_{obs}}{\lambda_{em}}$$
-                """,
-                mathjax=True,
-                style={"textAlign": "center", "fontSize": "26px", "color": "#17a2b8"}
-            ),
-
-            html.P([
-                "This difference is caused by the expansion of the Universe and can be interpreted in terms "
-                "of a scale factor a, which describes the linear stretching of spacetime:"
-            ], className="text-light mt-5 mb-4"),
-
-            # Equation 6.5 - Scale Factor
-            dcc.Markdown(
-                r"""
-                $$1 + z = \frac{a_{obs}}{a_{em}} = \frac{1}{a_{em}}$$
-                """,
-                mathjax=True,
-                style={"textAlign": "center", "fontSize": "26px", "color": "#17a2b8"}
-            ),
-
-            # Comoving Distance Section
-            html.Div([
-                html.H4("The ΛCDM Distance Relation", className="text-info mt-5 mb-3"),
-                html.P("Assuming a flat universe, the comoving distance as a reference is obtained via:", 
-                       className="text-light"),
-                
-                # Equation 6.6 - Comoving Distance Integral
-                dcc.Markdown(
-                    r"""
-                    $$d_c(z) = \frac{c}{H_0} \int_{0}^{z} \frac{dz'}{\sqrt{\Omega_m(1+z')^3 + \Omega_r(1+z')^4 + \Omega_\Lambda}}$$
-                    """,
-                    mathjax=True,
-                    style={"textAlign": "center", "fontSize": "22px", "color": "#9d50bb"}
-                ),
-                
-                # IMPORTANT: Using raw string (r) here prevents the \Omega syntax error
-                dcc.Markdown(
-                    r"*Where* $c$ *is the speed of light,* $H_0$ *is the Hubble constant, and* $\Omega$ *terms represent the matter/energy budget.*",
-                    mathjax=True,
-                    style={"textAlign": "center", "color": "#adb5bd", "fontStyle": "italic"}
-                ),
-            ], className="p-4 rounded-4 mt-5", style={"background": "rgba(255, 255, 255, 0.02)", "border": "1px solid rgba(157, 80, 187, 0.3)"})
-        ])
-    ], className="modern-card p-4"),
-])

app/pages/photometry.py

@@ -1,89 +0,0 @@
-import dash
-from dash import html, dcc
-import dash_bootstrap_components as dbc
-
-dash.register_page(__name__, path='/photometry')
-
-layout = html.Div([
-    html.Div([
-        # Title and Subtitle in English
-        html.H1("Photometry: Seeing Blur to See Big", className="text-white fw-bold mb-2"),
-        html.P("Trading fine detail for cosmic scale", className="lead text-info"),
-    ], className="mb-5"),
-
-    dbc.Card([
-        dbc.CardBody([
-            html.H3("The Photometric Compromise", className="text-white mb-4"),
-            
-            html.P([
-                "Obtaining detailed spectra is incredibly time-consuming for telescopes. Instead, we use cameras equipped with ",
-                html.B("colored filters"), " (such as the u, g, r, i, z, y bands of the Vera Rubin Observatory) to measure average luminosity across wide color ranges."
-            ], className="text-light mb-4"),
-
-            # Dark Mode Comparison Image
-            html.Div([
-                html.Img(
-                    src=dash.get_asset_url('photometry.jpg'), 
-                    style={
-                        "width": "100%", 
-                        "maxWidth": "900px", 
-                        "borderRadius": "15px", 
-                        "marginBottom": "15px",
-                        "border": "1px solid rgba(0, 210, 255, 0.2)",
-                        "display": "block",
-                        "marginLeft": "auto",
-                        "marginRight": "auto"
-                    }
-                ),
-                html.P("Comparison: High-resolution Spectroscopy vs. Low-resolution Photometric 'color' bands.", 
-                       className="text-muted small italic")
-            ], className="text-center mb-5"),
-
-            
-
-            # Narrative Section
-            html.Div([
-                html.H4("Washing out the Details to Gain Scale", className="text-info mb-3"),
-                html.P([
-                    "Although the fine details (spectral lines) are 'washed out' by the averaging process, this method allows for unparalleled execution speed. ",
-                    "By analyzing the brightness ratios between these bands—known as ", html.I("colors"), 
-                    "—we can estimate the distance of billions of galaxies, whereas spectroscopy could only process a few thousand in the same timeframe."
-                ], className="text-light")
-            ], className="p-4 rounded-4 mb-4", style={
-                "background": "rgba(255, 255, 255, 0.02)", 
-                "border": "1px solid rgba(0, 210, 255, 0.3)"
-            }),
-
-            dbc.Row([
-                dbc.Col([
-                    html.Div([
-                        html.H5("Speed over Precision", className="text-warning"),
-                        html.P(
-                            "Photometry is the equivalent of low-resolution vision. Instead of thousands of data points, "
-                            "we capture the broad energy distribution of a galaxy in a matter of seconds."
-                        ),
-                    ], className="p-3 mb-4 rounded bg-dark border-start border-warning border-4"),
-                ], width=12, lg=6),
-                
-                dbc.Col([
-                    html.Div([
-                        html.H5("Mapping the Cosmos", className="text-success"),
-                        html.P(
-                            "Upcoming surveys like the LSST will image a significant fraction of the observable Universe, "
-                            "creating a massive 3D map to study the Large-Scale Structure of our cosmos."
-                        ),
-                    ], className="p-3 mb-4 rounded bg-dark border-start border-success border-4"),
-                ], width=12, lg=6),
-            ]),
-
-            # Closing thought
-            html.Div([
-                html.Hr(style={"borderColor": "rgba(255,255,255,0.1)"}),
-                html.P([
-                    "By trading the 'Golden Standard' for volume, we transition from deep pinpricks in the sky ",
-                    "to a wide-angle view of the entire cosmic web."
-                ], className="text-center text-muted mt-4")
-            ])
-        ])
-    ], className="modern-card p-4"),
-])

app/pages/prediction.py

@@ -1,240 +0,0 @@
-import dash
-from dash import html, dcc, callback, Input, Output, State
-import dash_bootstrap_components as dbc
-import pandas as pd
-import numpy as np
-import joblib
-import os
-import sys
-import plotly.graph_objects as go
-from sklearn.base import BaseEstimator, TransformerMixin
-
-dash.register_page(__name__)
-
-# =====================================================================
-# 1. PIPELINE DE-SERIALIZATION CLASS DEFINITION
-# =====================================================================
-class AstronomicalColorEngineer(BaseEstimator, TransformerMixin):
-    """
-    Custom transformer class matching the exact blueprint used during pipeline training.
-    """
-    def __init__(self):
-        self.feature_order = [
-            'u_apercor', 'g_apercor', 'r_apercor', 'i_apercor', 'z_apercor', 'y_apercor',
-            'u_g', 'g_r', 'r_i', 'i_z', 'z_y'
-        ]
-        
-    def fit(self, X, y=None):
-        return self
-        
-    def transform(self, X):
-        if not isinstance(X, pd.DataFrame):
-            X = pd.DataFrame(X, columns=['u_apercor', 'g_apercor', 'r_apercor', 'i_apercor', 'z_apercor', 'y_apercor'])
-        
-        # Reset the index to 0 to ensure flawless row math alignment on single web-form vectors
-        df = X.copy().reset_index(drop=True)
-        
-        # Derive relative telescope color pass bands
-        df['u_g'] = df['u_apercor'] - df['g_apercor']
-        df['g_r'] = df['g_apercor'] - df['r_apercor']
-        df['r_i'] = df['r_apercor'] - df['i_apercor']
-        df['i_z'] = df['i_apercor'] - df['z_apercor']
-        df['z_y'] = df['z_apercor'] - df['y_apercor']
-        
-        return df[self.feature_order]
-
-
-# =====================================================================
-# 2. SAFE PIPELINE INITIALIZATION & NAMESPACE INJECTION
-# =====================================================================
-# Dynamically inject the class definition into the root main thread to intercept Joblib de-serialization errors
-sys.modules['__main__'].AstronomicalColorEngineer = AstronomicalColorEngineer
-
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-model_path = os.path.join(BASE_DIR, 'models', 'xgb_production_pipeline.pkl')
-
-try:
-    pipeline = joblib.load(model_path)
-except Exception as e:
-    print(f"Error loading pipeline tracking binary: {e}")
-    pipeline = None
-
-
-# =====================================================================
-# 3. INTERPRETABILITY & CONFIDENCE INTERVAL GENERATOR HELPER
-# =====================================================================
-def run_advanced_inference(raw_df):
-    """
-    Extracts the sub-steps of the pipeline to run localized feature contribution
-    calculations and dynamically scale cosmological error bars.
-    """
-    transformer = pipeline.named_steps['color_engineer']
-    xgb_model = pipeline.named_steps['model']
-    
-    # 1. Transform input to view the 11 engineered bands + colors
-    enriched_df = transformer.transform(raw_df)
-    
-    # 2. Compute base redshift prediction (z)
-    prediction = xgb_model.predict(enriched_df)[0]
-    
-    # 3. Calculate 95% Confidence Interval boundaries using the pipeline's true NMAD (0.0196)
-    # Photometric redshift errors scale heteroscedastically: σ ≈ NMAD * (1 + z)
-    nmad_scale = 0.019678
-    error_margin = 1.96 * nmad_scale * (1 + prediction)
-    ci_lower = max(0.0, prediction - error_margin)
-    ci_upper = prediction + error_margin
-    
-    # 4. Extract Real-Time Feature Importance / Shape Vector
-    global_importances = xgb_model.feature_importances_
-    sample_values = np.abs(enriched_df.iloc[0].values)
-    raw_contributions = global_importances * sample_values
-    
-    # Convert to relative contribution percentages for cleaner visualization
-    contribution_pct = (raw_contributions / np.sum(raw_contributions)) * 100
-    
-    importance_df = pd.DataFrame({
-        'Feature': enriched_df.columns,
-        'Contribution': contribution_pct
-    }).sort_values(by='Contribution', ascending=True)
-    
-    # 5. Build Horizontal Contribution Graph
-    fig = go.Figure(go.Bar(
-        x=importance_df['Contribution'],
-        y=importance_df['Feature'],
-        orientation='h',
-        marker=dict(
-            color=importance_df['Contribution'],
-            colorscale=[[0, '#3a1c5c'], [0.5, '#770A7A'], [1, '#00d2ff']],
-            line=dict(color='rgba(255,255,255,0.1)', width=1)
-        )
-    ))
-    
-    fig.update_layout(
-        paper_bgcolor='rgba(0,0,0,0)',
-        plot_bgcolor='rgba(0,0,0,0)',
-        font=dict(color='white', size=10),
-        margin=dict(l=75, r=15, t=10, b=10),
-        height=260,
-        xaxis=dict(title="Relative Feature Weight Contribution (%)", gridcolor='rgba(255,255,255,0.05)', title_font=dict(size=10)),
-        yaxis=dict(gridcolor='rgba(255,255,255,0.05)')
-    )
-    
-    return prediction, ci_lower, ci_upper, fig
-
-
-# =====================================================================
-# 4. INTERFACE LAYOUT DEFINITION
-# =====================================================================
-layout = dbc.Container([
-    html.Div([
-        html.Span("NEBULA ENGINE V3.0 — PRODUCTION ENHANCED", className="text-info small fw-bold tracking-widest"),
-        html.H1("Galaxy Redshift Estimator", className="text-white fw-bold display-4"),
-    ], className="mb-4 py-3"),
-
-    dbc.Row([
-        # Left Column: User Input Form Panel
-        dbc.Col([
-            dbc.Card([
-                dbc.CardBody([
-                    html.H5("Photometric Magnitudes", className="mb-4 text-white-50 fw-light"),
-                    dbc.Row([
-                        dbc.Col([
-                            dbc.Label("u_apercor", className="text-info x-small"),
-                            dbc.Input(id="u_v", type="number", value=24.9132, className="bg-dark border-secondary text-white mb-3"),
-                            
-                            dbc.Label("r_apercor", className="text-info x-small"),
-                            dbc.Input(id="r_v", type="number", value=23.4690, className="bg-dark border-secondary text-white mb-3"),
-                            
-                            dbc.Label("z_apercor", className="text-info x-small"),
-                            dbc.Input(id="z_v", type="number", value=21.7408, className="bg-dark border-secondary text-white"),
-                        ], width=6),
-                        dbc.Col([
-                            dbc.Label("g_apercor", className="text-info x-small"),
-                            dbc.Input(id="g_v", type="number", value=24.4809, className="bg-dark border-secondary text-white mb-3"),
-                            
-                            dbc.Label("i_apercor", className="text-info x-small"),
-                            dbc.Input(id="i_v", type="number", value=22.4933, className="bg-dark border-secondary text-white mb-3"),
-                            
-                            dbc.Label("y_apercor", className="text-info x-small"),
-                            dbc.Input(id="y_v", type="number", value=21.5000, className="bg-dark border-secondary text-white"),
-                        ], width=6),
-                    ]),
-                    dbc.Button("ANALYZE SPECTRUM", id="run-btn", className="w-100 py-3 mt-4 fw-bold",
-                               style={"background": "linear-gradient(45deg, #00d2ff, #9d50bb)", "border": "none", "borderRadius": "15px"})
-                ])
-            ], className="modern-card p-3 mb-4")
-        ], lg=6),
-
-        # Right Column: Predictions, Intervals, and Local Shape Profiles
-        dbc.Col([
-            dbc.Card([
-                dbc.CardBody([
-                    html.H6("ESTIMATED REDSHIFT", className="text-center text-white-50 letter-spacing-2 mb-1"),
-                    html.Div("---", id="pred-out", className="text-center py-1", style={"fontSize": "3.8rem", "color": "#00d2ff", "fontWeight": "bold"}),
-                    
-                    # 95% Confidence Bounds 
-                    html.Div("Awaiting matrix initialization...", id="ci-out", className="text-center text-muted small mb-3", style={"fontStyle": "italic"}),
-                    
-                    # Production Model Validation Performance Statistics 
-                    html.Div([
-                        dbc.Row([
-                            dbc.Col([
-                                html.Small("MODEL R²", className="text-info d-block x-small"),
-                                html.Span("0.8429", className="fw-bold text-white"),
-                            ], className="text-center border-end border-secondary"),
-                            dbc.Col([
-                                html.Small("GLOBAL MAE", className="text-info d-block x-small"),
-                                html.Span("0.0538", className="fw-bold text-white"),
-                            ], className="text-center border-end border-secondary"),
-                            dbc.Col([
-                                html.Small("OUTLIER FRACTION", className="text-info d-block x-small"),
-                                html.Span("3.17%", className="fw-bold text-success"),
-                            ], className="text-center"),
-                        ]),
-                    ], className="py-2 px-1 mb-3", style={"background": "rgba(0,0,0,0.3)", "borderRadius": "12px"}),
-
-                    # Real-time Feature Contribution Figure Container
-                    html.H6("LOCALIZED PROFILE CONTEXT (SHAPE IMPORTANCE)", className="text-white-50 x-small letter-spacing-2 mb-2"),
-                    html.Div([
-                        dcc.Graph(id="importance-graph", config={"displayModeBar": False})
-                    ], style={"background": "rgba(0,0,0,0.2)", "borderRadius": "12px", "padding": "5px"})
-
-                ], className="d-flex flex-column justify-content-center h-100")
-            ], className="modern-card")
-        ], lg=6)
-    ])
-], fluid=True)
-
-
-# =====================================================================
-# 5. REACTIVE REACTION CALLBACK CONTROL
-# =====================================================================
-@callback(
-    [Output("pred-out", "children"),
-     Output("ci-out", "children"),
-     Output("importance-graph", "figure")],
-    Input("run-btn", "n_clicks"),
-    [State("u_v", "value"), State("g_v", "value"), State("r_v", "value"),
-     State("i_v", "value"), State("z_v", "value"), State("y_v", "value")]
-)
-def update_prediction_dashboard(n, u, g, r, i, z, y):
-    if not n or pipeline is None:
-        empty_fig = go.Figure().update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)', font=dict(color='white'))
-        return "---", "Awaiting spectrum parameters...", empty_fig
-        
-    if None in [u, g, r, i, z, y]:
-        return "ERROR", "Incomplete matrix parameters provided.", go.Figure()
-
-    # Structure raw elements into a DataFrame mapping the pipeline's anticipated inputs
-    df = pd.DataFrame(
-        [[u, g, r, i, z, y]], 
-        columns=['u_apercor', 'g_apercor', 'r_apercor', 'i_apercor', 'z_apercor', 'y_apercor']
-    )
-    
-    # Compute using the advanced inference engine
-    pred, ci_low, ci_high, importance_figure = run_advanced_inference(df)
-    
-    ci_text = f"95% Confidence Bounds: [{ci_low:.4f}  —  {ci_high:.4f}]"
-    pred_text = f"{pred:.4f}"
-    
-    return pred_text, ci_text, importance_figure

app/pages/sel_target.csv

@@ -1,6308 +0,0 @@
-zhelio
-1.0034
-0.9023
-0.4242
-0.7690
-0.9910
-0.3808
-0.6401
-0.9034
-0.4246
-1.1720
-0.5726
-0.5811
-0.6409
-0.3909
-1.2647
-0.2754
-0.8619
-0.1983
-0.9920
-0.5297
-0.5279
-0.4996
-1.0339
-0.9385
-0.7956
-0.6816
-0.8086
-1.2314
-0.9944
-0.9910
-0.9019
-0.8650
-0.5093
-0.4236
-0.3553
-0.5283
-0.4317
-1.0816
-1.1129
-1.0249
-1.1120
-1.3232
-0.9009
-0.9896
-1.0298
-0.3563
-0.6405
-0.9926
-0.4773
-0.8967
-0.7590
-0.1496
-0.7981
-0.3661
-1.2887
-0.5707
-0.2749
-0.7090
-1.2927
-0.3967
-1.0404
-0.8118
-1.1968
-0.8625
-0.2591
-1.4579
-0.4990
-0.7684
-0.7967
-1.0275
-0.7964
-0.7917
-0.5310
-0.9909
-1.1127
-0.9878
-0.4277
-0.6420
-0.3750
-0.3659
-0.3658
-0.5315
-0.8637
-0.5331
-0.5326
-1.0417
-0.3679
-1.3675
-1.1132
-0.4245
-1.4201
-0.1665
-1.2890
-0.4310
-0.3923
-1.3775
-0.3927
-0.9954
-0.6191
-0.1675
-0.7485
-0.3797
-0.6046
-0.6447
-0.8103
-0.9891
-0.7992
-0.9887
-0.6841
-0.6825
-0.9864
-0.7538
-0.2822
-1.1666
-0.6837
-1.1207
-0.4198
-0.9903
-0.3655
-1.0026
-0.6400
-0.3638
-0.8125
-0.6418
-0.3660
-0.6415
-0.1119
-0.6180
-0.9343
-0.4566
-0.6829
-0.5160
-0.9889
-0.5038
-0.8674
-0.4169
-0.3316
-1.0233
-0.8332
-0.8337
-1.0253
-0.0936
-1.2858
-0.8123
-0.7521
-0.5069
-0.2533
-0.4202
-0.3314
-0.3864
-0.5074
-0.6842
-0.6763
-0.4186
-0.8083
-0.8991
-0.8118
-0.9050
-1.2043
-0.7654
-1.0330
-0.8293
-0.4676
-0.6828
-0.8292
-0.8127
-0.9397
-0.9015
-0.4679
-0.2493
-0.8080
-1.0355
-1.0353
-0.6128
-1.3508
-0.6727
-0.4411
-0.9331
-0.7948
-0.9035
-0.4734
-0.4178
-0.4318
-0.3727
-1.0957
-0.8157
-0.4333
-0.9965
-0.2877
-0.7642
-0.7614
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-1.0052
-0.4196
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-0.8080
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-0.3658
-0.6715
-0.8290
-0.0762
-0.4836
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-0.7502
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-0.4632
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-0.2814
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-1.0290
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-0.6827
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-0.8096
-0.5280
-0.9861
-0.7569
-0.7574
-0.4509
-0.6196
-0.4702
-0.7508
-0.7494
-0.9882
-0.3859
-1.5266
-0.9310
-1.0303
-1.0283
-0.7208
-0.2448
-0.4411
-1.0257
-0.7656
-0.6816
-0.2486
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-0.2449
-0.1600
-0.7514
-0.2226
-0.7211
-0.5708
-0.3656
-0.6186
-1.0631
-1.2586
-0.7504
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app/pages/spectroscopy.py

@@ -1,76 +0,0 @@
-import dash
-from dash import html, dcc
-import dash_bootstrap_components as dbc
-
-dash.register_page(__name__, path='/spectroscopy')
-
-layout = html.Div([
-    html.Div([
-        html.H1("Spectroscopy: The Digital Fingerprint", className="text-white fw-bold mb-2"),
-        html.P("The Golden Standard for Cosmic Mapping", className="lead text-info"),
-    ], className="mb-5"),
-
-    dbc.Card([
-        dbc.CardBody([
-            html.H3("The Golden Standard (L'étalon-or)", className="text-white mb-4"),
-            
-            html.P([
-                "By decomposing the light from a galaxy, we can identify specific 'lines' (peaks or dips) caused by ",
-                "atoms such as ", html.B("Hydrogen"), " or ", html.B("Oxygen"), ". Since we know the exact ",
-                "laboratory wavelength of these lines, we can precisely measure how much they have been shifted."
-            ], className="text-light mb-4"),
-
-            # Your new asset: spectro.png
-            html.Div([
-                html.Img(
-                    src=dash.get_asset_url('spectro.png'), 
-                    style={
-                        "width": "100%", "maxHeight": "350px", "objectFit": "contain",
-                        "borderRadius": "15px", "marginBottom": "10px",
-                        "border": "1px solid rgba(0, 210, 255, 0.2)"
-                    }
-                ),
-                html.P("Galaxy Spectrum: Identifying H-alpha and OIII lines to calculate precise redshift.", 
-                       className="text-muted small italic")
-            ], className="text-center mb-5"),
-
-            
-
-            dbc.Row([
-                dbc.Col([
-                    html.Div([
-                        html.H5("The Insight", className="text-success"),
-                        html.P(
-                            "If we can identify these lines, the calculation of redshift is trivial and extremely precise. "
-                            "This is the ideal method, known as Spectroscopic Redshift."
-                        ),
-                    ], className="p-3 mb-4 rounded bg-dark border-start border-success border-4"),
-                ], width=12, lg=6),
-                
-                dbc.Col([
-                    html.Div([
-                        html.H5("The Problem", className="text-warning"),
-                        html.P(
-                            "This method is very expensive in terms of exposure time. It is impossible to apply "
-                            "this to the billions of galaxies we must map to see the large-scale structure."
-                        ),
-                    ], className="p-3 mb-4 rounded bg-dark border-start border-warning border-4"),
-                ], width=12, lg=6),
-            ]),
-
-            html.H4("The Mathematics of the Fingerprint", className="text-info mt-4 mb-3"),
-            html.P([
-                "To find the redshift (", html.Span("z", className="text-info"), 
-                "), we compare the observed wavelength of a line to its rest wavelength:"
-            ], className="text-light"),
-
-            dcc.Markdown(
-                r"""
-                $$z = \frac{\lambda_{obs} - \lambda_{rest}}{\lambda_{rest}}$$
-                """,
-                mathjax=True,
-                style={"textAlign": "center", "fontSize": "28px", "color": "#00d2ff", "padding": "20px"}
-            )
-        ])
-    ], className="modern-card p-4"),
-])

app/requirements.txt

@@ -1,21 +0,0 @@
-# Core Web Application Framework
-dash>=2.14.0
-dash-bootstrap-components>=1.5.0
-
-# Numerical Processing & Data Engineering
-pandas>=2.0.0
-numpy>=1.24.0
-scipy>=1.10.0
-
-# Machine Learning & Serialization Pipeline
-scikit-learn>=1.3.0
-xgboost>=2.0.0
-joblib>=1.3.0
-
-# Visualization & Asset Utilities
-plotly>=5.18.0
-Pillow>=10.0.0
-
-# Production WSGI Server & Core Run Performance
-gunicorn>=21.2.0
-threadpoolctl>=3.2.0