Upload 28 files

.gitattributes

@@ -36,3 +36,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 app/assets/final_xgb_performance.png filter=lfs diff=lfs merge=lfs -text
 app/assets/redshit.png filter=lfs diff=lfs merge=lfs -text
 app/assets/spectro.png filter=lfs diff=lfs merge=lfs -text
+assets/final_xgb_performance.png filter=lfs diff=lfs merge=lfs -text
+assets/redshit.png filter=lfs diff=lfs merge=lfs -text
+assets/spectro.png filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,18 @@
+# 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"]

Procfile

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

app.py

@@ -0,0 +1,85 @@
+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)

assets/galaxy.js

@@ -0,0 +1,55 @@
+// 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);
+};

assets/style.css

@@ -0,0 +1,72 @@
+/* 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));
+}

models/xgb_production_pipeline.pkl

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

pages/analysis.py

@@ -0,0 +1,175 @@
+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

pages/data.py

@@ -0,0 +1,83 @@
+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"),
+])

pages/introduction.py

@@ -0,0 +1,77 @@
+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"),
+])

pages/phenomenon.py

@@ -0,0 +1,102 @@
+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"),
+])

pages/photometry.py

@@ -0,0 +1,89 @@
+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"),
+])

pages/prediction.py

@@ -0,0 +1,240 @@
+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

pages/sel_target.csv

@@ -0,0 +1,6308 @@
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pages/spectroscopy.py

@@ -0,0 +1,76 @@
+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"),
+])

requirements.txt

@@ -0,0 +1,21 @@
+# 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