Merge conf-mat-addition into main

app/static/conf_mat.py

@@ -0,0 +1,211 @@
+"""
+conf_mat.py
+
+Script to generate confusion matrix png files
+"""
+
+from pathlib import Path
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix
+from models.download_from_hf import download
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+import numpy as np
+import joblib
+import os
+
+MODEL_DIR = Path("models")
+PIPE_PATH = Path("models","pipe.pkl")
+COLUMNS_PATH = Path("models","column_names.pkl")
+CONF_MAT_PATH = Path("app","static","materials","confusion_mat.png")
+
+def initialize_artifacts() -> Pipeline:
+    """
+    Checks if model artifacts exist. If not, runs the training script.
+    """
+    # 1. Ensure the model directory exists
+    os.makedirs(MODEL_DIR, exist_ok=True)
+
+    # 2. Check for missing files
+    pipe_exists = os.path.exists(PIPE_PATH)
+    columns_exists = os.path.exists(COLUMNS_PATH)
+
+    if not pipe_exists or not columns_exists:
+        print("--- MODEL ARTIFACTS MISSING ---")
+        if not pipe_exists:
+            print(f"Missing: {PIPE_PATH}")
+        if not columns_exists:
+            print(f"Missing: {COLUMNS_PATH}")
+
+        print("Downloading the saved models from Hugging Face... This may take a moment.")
+        try:
+            # Run the `download` function from `models/download_from_hf.py`
+            download()
+            print("Download complete. Artifacts generated successfully.")
+            print("---------------------------------")
+        except Exception as e:
+            print(f"\nFATAL: Error during self-heal downloading: {e}")
+            print("Application cannot start without model artifacts. Exitting......")
+            exit(1) # Exit if training fails
+    else:
+        print("Model artifacts found. Loading...")
+        pipe = joblib.load(PIPE_PATH)
+        print("Model artifacts are loaded. Ready for prediction 🚀")
+        return pipe
+
+def get_window(camps, campaign_dates):
+    if pd.isna(camps) or not camps:
+        return np.nan, np.nan
+
+    camps = str(camps).split(',') if isinstance(camps, str) else camps
+    starts, ends = [], []
+
+    for c in camps:
+        try:
+            camp_num = int(c.strip())
+            if camp_num in campaign_dates:
+                start, end = campaign_dates[camp_num]
+                starts.append(start)
+                ends.append(end)
+        except (ValueError, KeyError):
+            continue
+
+    return (min(starts) if starts else np.nan, max(ends) if ends else np.nan)
+
+
+def load_and_prepare_data():
+    # Load Kepler dataset
+    df_raw = pd.read_csv("data/kepler_data.csv", comment="#")
+    feature_list = [
+        "koi_disposition", "koi_period", "koi_time0bk", "koi_depth", "koi_prad",
+        "koi_sma", "koi_incl", "koi_teq", "koi_insol", "koi_impact",
+        "koi_ror", "koi_srho", "koi_dor", "koi_num_transits"
+    ]
+    df_1 = df_raw[feature_list].copy()
+
+    # Load K2 dataset
+    df_2 = pd.read_csv("data/k2_data.csv", comment="#")
+
+    # Define campaign windows
+    campaign_dates = {
+        0: (2456725.0, 2456805.0), 1: (2456808.0, 2456891.0), 2: (2456893.0, 2456975.0),
+        3: (2456976.0, 2457064.0), 4: (2457065.0, 2457159.0), 5: (2457159.0, 2457246.0),
+        6: (2457250.0, 2457338.0), 7: (2457339.0, 2457420.0), 8: (2457421.0, 2457530.0),
+        9: (2457504.0, 2457579.0), 10: (2457577.0, 2457653.0), 11: (2457657.0, 2457732.0),
+        12: (2457731.0, 2457819.0), 13: (2457820.0, 2457900.0), 14: (2457898.0, 2457942.0),
+        15: (2457941.0, 2458022.0), 16: (2458020.0, 2458074.0), 17: (2458074.0, 2458176.0),
+        18: (2458151.0, 2458201.0), 19: (2458232.0, 2458348.0)
+    }
+
+    # Add observation window
+    df_2['campaigns'] = df_2['k2_campaigns']
+    df_2[['obs_start_bjd', 'obs_end_bjd']] = df_2['campaigns'].apply(
+        lambda x: pd.Series(get_window(x, campaign_dates))
+    )
+
+    # Transit counting
+    df_2['n_min'] = np.ceil((df_2['obs_start_bjd'] - df_2['pl_tranmid']) / df_2['pl_orbper'])
+    df_2['n_max'] = np.floor((df_2['obs_end_bjd'] - df_2['pl_tranmid']) / df_2['pl_orbper'])
+    df_2['num_transits'] = (df_2['n_max'] - df_2['n_min'] + 1).clip(lower=0)
+
+    # Select and rename columns
+    df_2 = df_2[
+        ["disposition", "pl_orbper", "pl_tranmid", "pl_trandep", "pl_rade",
+         "pl_orbsmax", "pl_orbincl", "pl_eqt", "pl_insol", "pl_imppar",
+         "pl_ratror", "pl_dens", "pl_ratdor", "num_transits"]
+    ]
+
+    mapping = {
+        "disposition": "koi_disposition", "pl_orbper": "koi_period", "pl_tranmid": "koi_time0bk",
+        "pl_trandep": "koi_depth", "pl_rade": "koi_prad", "pl_orbsmax": "koi_sma",
+        "pl_orbincl": "koi_incl", "pl_eqt": "koi_teq", "pl_insol": "koi_insol",
+        "pl_imppar": "koi_impact", "pl_ratror": "koi_ror", "pl_dens": "koi_srho",
+        "pl_ratdor": "koi_dor", "num_transits": "koi_num_transits"
+    }
+    df_2 = df_2.rename(columns=mapping)
+
+    # Combine both datasets
+    df = pd.concat([df_1, df_2])
+
+    # Prepare input/output
+    X = df.iloc[:, 1:].to_numpy()
+    y = df["koi_disposition"].map({
+        "FALSE POSITIVE": 0, "CANDIDATE": 1, "CONFIRMED": 2, "REFUTED": 0
+    }).to_numpy()
+
+    return X, y, df.columns[1:]
+
+def main() -> None:
+    pipe = initialize_artifacts()
+    x,y,column_names = load_and_prepare_data()
+
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=1/3, shuffle=True, random_state=91, stratify=y
+    )
+
+    labels = ["FALSE POSITIVE","CANDIDATE","CONFIRMED"]
+    y_true = y_test
+    y_pred = pipe.predict(x_test)
+
+    cm = confusion_matrix(y_true,y_pred)
+
+    # Custom cosmic dark theme
+    plt.style.use('dark_background')
+
+    fig, ax = plt.subplots(figsize=(12, 12))
+    fig.patch.set_facecolor('#0a0a0a')
+    ax.set_facecolor('#0a0a0a')
+
+    # Custom colormap - cosmic blue to cyan
+    cmap = sns.color_palette([
+        '#0a0a0a',
+        '#001a2c',
+        '#003355',
+        '#004d7a',
+        '#0066a3',
+        '#0080cc',
+        '#0099f5',
+        '#00b3ff'
+    ], as_cmap=True)
+
+    # Create heatmap with custom styling
+    sns.heatmap(
+        cm,
+        xticklabels=labels,
+        yticklabels=labels,
+        annot=True,
+        fmt="d",
+        square=True,
+        cmap=cmap,
+        cbar_kws={'label': 'Count', 'shrink': 1.0},
+        linewidths=4,
+        linecolor='#1a1a1a',
+        ax=ax,
+        annot_kws={'size': 32, 'weight': 'bold', 'color': '#ffffff'}
+    )
+
+    # Customize appearance
+    ax.set_xlabel('Predicted', fontsize=22, color='#ffffff', fontweight='bold', labelpad=25)
+    ax.set_ylabel('Actual', fontsize=22, color='#ffffff', fontweight='bold', labelpad=25)
+    ax.set_title('Confusion Matrix', fontsize=36, color='#00BCFF', fontweight='bold', pad=40)
+
+    # Style tick labels
+    ax.tick_params(axis='both', colors='#a0a0a0', labelsize=18)
+    ax.set_xticklabels(ax.get_xticklabels(), rotation=0, ha='center')
+    ax.set_yticklabels(ax.get_yticklabels(), rotation=0, ha='right')
+
+    # Style colorbar
+    cbar = ax.collections[0].colorbar
+    cbar.ax.yaxis.set_tick_params(color='#a0a0a0')
+    cbar.outline.set_edgecolor('#1a1a1a')
+    plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='#a0a0a0', size=16)
+
+    plt.tight_layout()
+    plt.savefig(CONF_MAT_PATH, dpi=300, facecolor='#0a0a0a', edgecolor='none', bbox_inches='tight', pad_inches=0.1)
+    plt.close()
+    print(f"Confusion Matrix is successfully saved at {str(CONF_MAT_PATH)}")
+
+if __name__ == "__main__":
+    main()

app/static/css/style.css

@@ -15,7 +15,7 @@
     --glass-border: rgba(255, 255, 255, 0.15);
 
     /* Spacing & Layout */
-    --container-width: 1200px;
+    --container-width: 1400px;
     --header-height: 80px;
     --radius-lg: 24px;
     --radius-md: 12px;
@@ -819,6 +819,175 @@ h1, h2, h3, h4, h5, h6 {
     background: rgba(255, 255, 255, 0.02);
 }
 
+/* =========================================
+    Performance Tab Styles
+    ========================================= */
+.performance-section {
+    padding: 4rem 0;
+}
+
+.performance-section .glass-card {
+    max-width: 1300px;
+    width: 100%;
+    margin: 0 auto;
+}
+
+.performance-grid {
+    display: flex;
+    flex-direction: column;
+    gap: 4rem;
+    align-items: center;
+    width: 100%;
+}
+
+@media (max-width: 1200px) {
+    .performance-grid {
+        grid-template-columns: 1fr;
+    }
+}
+
+.cm-container {
+    background: var(--bg-card);
+    border: 1px solid var(--glass-border);
+    border-radius: var(--radius-lg);
+    padding: 3rem;
+    text-align: center;
+    width: 100%;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+}
+
+.section-subtitle {
+    color: var(--accent-primary);
+    margin-bottom: 2rem;
+    font-size: 1.5rem;
+    position: relative;
+    z-index: 1;
+}
+
+.confusion-matrix {
+    width: 100%;
+    max-width: 880px;
+    height: auto;
+    border-radius: var(--radius-md);
+    box-shadow: 0 20px 60px rgba(0, 0, 0, 0.4), 0 0 30px rgba(0, 188, 255, 0.1);
+    transition: var(--transition-smooth);
+    background: #0a0a0a;
+}
+
+.confusion-matrix:hover {
+    transform: scale(1.02);
+    box-shadow: 0 30px 70px rgba(0, 0, 0, 0.5), 0 0 40px rgba(0, 188, 255, 0.2);
+}
+
+.metrics-container {
+    display: flex;
+    flex-direction: column;
+    gap: 2rem;
+    width: 100%;
+    max-width: 1000px;
+}
+
+.metrics-container .section-subtitle {
+    margin-bottom: 0.5rem;
+}
+
+.accuracy-card {
+    background: linear-gradient(135deg, rgba(0, 188, 255, 0.1), rgba(0, 119, 255, 0.1));
+    border: 1px solid var(--glass-border);
+    border-radius: var(--radius-md);
+    padding: 2rem;
+    text-align: center;
+}
+
+.accuracy-value {
+    font-size: 3.5rem;
+    font-weight: 700;
+    color: var(--accent-primary);
+    font-family: 'Lexend Deca', sans-serif;
+    line-height: 1;
+}
+
+.accuracy-label {
+    color: var(--text-muted);
+    margin-top: 0.5rem;
+    font-size: 1rem;
+}
+
+.metrics-table {
+    width: 100%;
+    border-collapse: collapse;
+    background: var(--bg-card);
+    border: 1px solid var(--glass-border);
+    border-radius: var(--radius-md);
+    overflow: hidden;
+    font-size: 0.9rem;
+}
+
+.metrics-table th,
+.metrics-table td {
+    padding: 1rem;
+    text-align: center;
+    border-bottom: 1px solid var(--border-color);
+}
+
+.metrics-table th {
+    background: rgba(0, 188, 255, 0.1);
+    color: var(--accent-primary);
+    font-weight: 600;
+    font-size: 0.85rem;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+}
+
+.metrics-table td:first-child {
+    text-align: left;
+}
+
+.metrics-table tbody tr:hover td {
+    background: rgba(255, 255, 255, 0.02);
+}
+
+.metrics-table tfoot td {
+    background: rgba(0, 188, 255, 0.05);
+    color: var(--text-muted);
+    font-size: 0.85rem;
+    border-bottom: none;
+}
+
+.model-info {
+    background: var(--bg-card);
+    border: 1px solid var(--glass-border);
+    border-radius: var(--radius-md);
+    padding: 1.5rem;
+}
+
+.model-info h4 {
+    color: var(--accent-primary);
+    margin-bottom: 1rem;
+    font-size: 1.1rem;
+}
+
+.model-info p {
+    color: var(--text-muted);
+    margin-bottom: 0.5rem;
+}
+
+.model-info ul {
+    list-style: none;
+    color: var(--text-muted);
+    font-size: 0.9rem;
+}
+
+.model-info li {
+    margin-bottom: 0.3rem;
+}
+
+.model-info li strong {
+    color: var(--text-main);
+}
+
 /* Notification System */
 #notification-container {
     position: fixed;