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README.md

@@ -1,14 +1,32 @@
 ---
-title: CHECKING
-emoji: 🦀
-colorFrom: gray
+title: PCA DimReduction 3D2D
+emoji: 📐
+colorFrom: blue
 colorTo: purple
 sdk: gradio
 sdk_version: 6.8.0
 app_file: app.py
 pinned: false
 license: mit
-short_description: 'dimensionality reduction using Principal Component Analysis '
+short_description: PCA tool for 3D to 2D dimensionality reduction.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# PCA Visualizer - 3D to 2D Dimensionality Reduction
+
+Interactive Principal Component Analysis tool with full step-by-step mathematical output.
+
+## Features
+- Enter 6+ 3D points and instantly see all PCA steps
+- Covariance matrix, eigenvalues, eigenvectors shown clearly
+- 3D scatter plot of original points
+- 2D projection plot
+- Variance explained bar and cumulative charts
+
+## Usage
+Enter points one per line as X Y Z, for example:
+```
+1 2 3
+4 5 6
+7 8 9
+```
+Click Run PCA to compute all steps.

app.py

@@ -0,0 +1,264 @@
+import gradio as gr
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+COLORS = ['#00d4ff','#7c3aed','#f59e0b','#10b981','#f43f5e',
+          '#a78bfa','#34d399','#fb923c','#60a5fa','#e879f9']
+
+def parse_points(text):
+    points, errors = [], []
+    for i, line in enumerate([l.strip() for l in text.strip().split('\n') if l.strip()]):
+        try:
+            vals = [float(x) for x in line.replace(',', ' ').split()]
+            if len(vals) != 3:
+                errors.append(f"Line {i+1}: need 3 values, got {len(vals)}")
+            else:
+                points.append(vals)
+        except:
+            errors.append(f"Line {i+1}: invalid numbers")
+    return (np.array(points) if points else None), errors
+
+def run_pca(points_text):
+    points, errors = parse_points(points_text)
+    if errors:
+        return "❌ Input Errors:\n" + "\n".join(errors), None, None, None
+    if points is None or len(points) < 6:
+        n = len(points) if points is not None else 0
+        return f"❌ Need at least 6 points (you entered {n})", None, None, None
+
+    n = len(points)
+    mean = np.mean(points, axis=0)
+    centered = points - mean
+    cov = np.cov(centered.T)
+    eigenvalues, eigenvectors = np.linalg.eigh(cov)
+    idx = np.argsort(eigenvalues)[::-1]
+    eigenvalues = eigenvalues[idx]
+    eigenvectors = eigenvectors[:, idx]
+    W = eigenvectors[:, :2]
+    projected = centered @ W
+    var_exp = eigenvalues / np.sum(eigenvalues) * 100
+
+    # ── Build output text
+    sep  = "═" * 58
+    thin = "─" * 58
+    L = []
+    L.append(sep)
+    L.append("   PCA  ·  3D → 2D  DIMENSIONALITY REDUCTION")
+    L.append(sep)
+
+    L.append("\n  ▸ STEP 1  |  INPUT POINTS")
+    L.append("  " + thin)
+    L.append(f"  {'#':<6} {'X':>10} {'Y':>10} {'Z':>10}")
+    L.append("  " + thin)
+    for i, p in enumerate(points):
+        L.append(f"  P{i+1:<5} {p[0]:>10.4f} {p[1]:>10.4f} {p[2]:>10.4f}")
+
+    L.append("\n  ▸ STEP 2  |  MEAN & CENTERED POINTS")
+    L.append("  " + thin)
+    L.append(f"  Mean μ = ({mean[0]:.4f},  {mean[1]:.4f},  {mean[2]:.4f})\n")
+    L.append(f"  {'#':<6} {'X-μx':>10} {'Y-μy':>10} {'Z-μz':>10}")
+    L.append("  " + thin)
+    for i, p in enumerate(centered):
+        L.append(f"  P{i+1:<5} {p[0]:>10.4f} {p[1]:>10.4f} {p[2]:>10.4f}")
+
+    L.append("\n  ▸ STEP 3  |  COVARIANCE MATRIX (3×3)")
+    L.append("  " + thin)
+    L.append(f"  {'':>6} {'X':>10} {'Y':>10} {'Z':>10}")
+    for i, lbl in enumerate(['X','Y','Z']):
+        L.append(f"  {lbl:<6}" + "".join(f"{cov[i,j]:>10.4f}" for j in range(3)))
+
+    L.append("\n  ▸ STEP 4  |  EIGENVALUES & EIGENVECTORS")
+    L.append("  " + thin)
+    for i in range(3):
+        ev = eigenvectors[:, i]
+        L.append(f"  λ{i+1} = {eigenvalues[i]:>10.6f}   |  v{i+1} = [{ev[0]:>8.4f}, {ev[1]:>8.4f}, {ev[2]:>8.4f}]")
+
+    L.append("\n  ▸ STEP 5  |  TOP 2 PRINCIPAL COMPONENTS")
+    L.append("  " + thin)
+    L.append(f"  PC1  λ={eigenvalues[0]:.4f}  →  [{W[0,0]:>7.4f}, {W[1,0]:>7.4f}, {W[2,0]:>7.4f}]")
+    L.append(f"  PC2  λ={eigenvalues[1]:.4f}  →  [{W[0,1]:>7.4f}, {W[1,1]:>7.4f}, {W[2,1]:>7.4f}]")
+    L.append(f"\n  Variance: PC1={var_exp[0]:.2f}%  PC2={var_exp[1]:.2f}%  Total={var_exp[0]+var_exp[1]:.2f}%")
+
+    L.append("\n  ▸ STEP 6  |  PROJECTION MATRIX W (3×2)")
+    L.append("  " + thin)
+    L.append(f"  {'':>4} {'PC1':>10} {'PC2':>10}")
+    for i, lbl in enumerate(['x','y','z']):
+        L.append(f"  {lbl:<4} {W[i,0]:>10.4f} {W[i,1]:>10.4f}")
+
+    L.append("\n  ▸ STEP 7  |  REDUCED 2D COORDINATES")
+    L.append("  " + thin)
+    L.append(f"  {'#':<6} {'3D (X, Y, Z)':<32} 2D (PC1, PC2)")
+    L.append("  " + thin)
+    for i in range(n):
+        orig = f"({points[i,0]:.2f}, {points[i,1]:.2f}, {points[i,2]:.2f})"
+        red  = f"({projected[i,0]:.4f},  {projected[i,1]:.4f})"
+        L.append(f"  P{i+1:<5} {orig:<32} {red}")
+
+    L.append("\n" + sep)
+    output_text = "\n".join(L)
+
+    # ── Plots
+    fig1 = make_3d_plot(points, mean)
+    fig2 = make_2d_plot(projected, var_exp)
+    fig3 = make_var_plot(var_exp)
+
+    return output_text, fig1, fig2, fig3
+
+
+def _style_ax(ax):
+    ax.set_facecolor('#111827')
+    for sp in ax.spines.values():
+        sp.set_color('#1e293b')
+    ax.tick_params(colors='#94a3b8', labelsize=8)
+
+
+def make_3d_plot(points, mean):
+    fig = plt.figure(figsize=(6, 5), facecolor='#0a0e1a')
+    ax = fig.add_subplot(111, projection='3d')
+    ax.set_facecolor('#111827')
+    for pane in [ax.xaxis.pane, ax.yaxis.pane, ax.zaxis.pane]:
+        pane.fill = False
+        pane.set_edgecolor('#1e293b')
+    ax.tick_params(colors='#94a3b8', labelsize=7)
+    ax.xaxis.label.set_color('#94a3b8')
+    ax.yaxis.label.set_color('#94a3b8')
+    ax.zaxis.label.set_color('#94a3b8')
+    ax.set_xlabel('X'); ax.set_ylabel('Y'); ax.set_zlabel('Z')
+    for i, p in enumerate(points):
+        c = COLORS[i % len(COLORS)]
+        ax.scatter(*p, color=c, s=80, edgecolors='white', linewidths=0.5, zorder=5)
+        ax.text(p[0], p[1], p[2], f'  P{i+1}', color=c, fontsize=7.5, fontweight='bold')
+    ax.scatter(*mean, color='#f59e0b', s=160, marker='*', edgecolors='white', linewidths=0.8, zorder=10)
+    ax.set_title('Original 3D Points', color='#e2e8f0', fontsize=11, fontweight='bold', pad=10)
+    fig.tight_layout()
+    return fig
+
+
+def make_2d_plot(projected, var_exp):
+    fig, ax = plt.subplots(figsize=(6, 5), facecolor='#0a0e1a')
+    _style_ax(ax)
+    ax.axhline(0, color='#1e293b', lw=1); ax.axvline(0, color='#1e293b', lw=1)
+    ax.grid(True, color='#1e293b', lw=0.5, alpha=0.7)
+    ax.set_xlabel(f'PC1  ({var_exp[0]:.1f}%)', color='#00d4ff', fontsize=10)
+    ax.set_ylabel(f'PC2  ({var_exp[1]:.1f}%)', color='#7c3aed', fontsize=10)
+    for i, p in enumerate(projected):
+        c = COLORS[i % len(COLORS)]
+        ax.scatter(*p, color=c, s=100, edgecolors='white', linewidths=0.6, zorder=5)
+        ax.annotate(f'P{i+1}', p, xytext=(7,4), textcoords='offset points',
+                    color=c, fontsize=8.5, fontweight='bold')
+    ax.set_title('2D Projection (PCA)', color='#e2e8f0', fontsize=11, fontweight='bold', pad=10)
+    fig.tight_layout()
+    return fig
+
+
+def make_var_plot(var_exp):
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), facecolor='#0a0e1a')
+    labels = ['PC1','PC2','PC3']
+    bar_colors = ['#00d4ff','#7c3aed','#f59e0b']
+
+    _style_ax(ax1)
+    bars = ax1.bar(labels, var_exp, color=bar_colors, edgecolor='#0a0e1a', linewidth=1.5, width=0.5)
+    for bar, val in zip(bars, var_exp):
+        ax1.text(bar.get_x()+bar.get_width()/2, bar.get_height()+0.8,
+                 f'{val:.1f}%', ha='center', color='#e2e8f0', fontsize=9, fontweight='bold')
+    ax1.set_title('Variance per PC', color='#e2e8f0', fontsize=10, fontweight='bold', pad=8)
+    ax1.set_ylabel('Variance (%)', color='#64748b', fontsize=9)
+    ax1.set_ylim(0, max(var_exp)*1.2)
+
+    _style_ax(ax2)
+    cum = np.cumsum(var_exp)
+    ax2.plot(labels, cum, 'o-', color='#10b981', lw=2.5, markersize=8,
+             markerfacecolor='#0a0e1a', markeredgecolor='#10b981', markeredgewidth=2.5)
+    ax2.fill_between(labels, cum, alpha=0.12, color='#10b981')
+    ax2.axhline(100, color='#f43f5e', lw=1, ls='--', alpha=0.5)
+    for x, y in zip(labels, cum):
+        ax2.text(x, y+2, f'{y:.1f}%', ha='center', color='#10b981', fontsize=9, fontweight='bold')
+    ax2.set_title('Cumulative Variance', color='#e2e8f0', fontsize=10, fontweight='bold', pad=8)
+    ax2.set_ylabel('Cumulative (%)', color='#64748b', fontsize=9)
+    ax2.set_ylim(0, 115)
+    ax2.grid(True, color='#1e293b', lw=0.5)
+
+    fig.tight_layout(pad=2)
+    return fig
+
+
+# ── CSS
+css = """
+@import url('https://fonts.googleapis.com/css2?family=Space+Mono:wght@400;700&family=Syne:wght@400;600;800&display=swap');
+
+body, .gradio-container { background: #0a0e1a !important; color: #e2e8f0 !important; font-family: 'Syne', sans-serif !important; }
+.gradio-container { max-width: 1200px !important; margin: 0 auto !important; }
+
+.header { background: linear-gradient(135deg,#0a0e1a,#111827); border: 1px solid #1e293b;
+    border-top: 3px solid #00d4ff; border-radius: 12px; padding: 28px 36px; margin-bottom: 20px; }
+.header h1 { font-family:'Syne',sans-serif; font-size:2rem; font-weight:800;
+    background:linear-gradient(90deg,#00d4ff,#7c3aed); -webkit-background-clip:text;
+    -webkit-text-fill-color:transparent; margin:0 0 6px 0; }
+.header p { color:#64748b; font-family:'Space Mono',monospace; font-size:0.82rem; margin:0; }
+
+.hint { background:rgba(0,212,255,0.04); border:1px solid rgba(0,212,255,0.15);
+    border-radius:8px; padding:12px 16px; font-family:'Space Mono',monospace;
+    font-size:0.76rem; color:#64748b; line-height:1.8; margin-top:10px; }
+.hint strong { color:#00d4ff; }
+
+textarea { background:#060a12 !important; border:1px solid #1e293b !important;
+    color:#94a3b8 !important; font-family:'Space Mono',monospace !important;
+    font-size:0.8rem !important; border-radius:8px !important; }
+textarea:focus { border-color:#00d4ff !important; outline:none !important; }
+
+button { font-family:'Syne',sans-serif !important; font-weight:700 !important; border-radius:8px !important; }
+"""
+
+EXAMPLE = """1 2 3
+4 5 6
+7 8 9
+2 4 1
+5 1 8
+3 6 2
+9 3 7
+1 8 4"""
+
+with gr.Blocks(css=css, title="PCA · 3D→2D") as demo:
+    gr.HTML("""
+    <div class="header">
+        <h1>&#x2B21; PCA Visualizer</h1>
+        <p>Principal Component Analysis &nbsp;·&nbsp; 3D &#8594; 2D Dimensionality Reduction</p>
+    </div>""")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            points_input = gr.Textbox(
+                label="3D Points  (one per line:  X  Y  Z)",
+                placeholder="1 2 3\n4 5 6\n...",
+                lines=12, value=EXAMPLE)
+
+            gr.HTML("""<div class="hint">
+                <strong>Format:</strong> X Y Z &nbsp;(space or comma)<br>
+                <strong>Minimum:</strong> 6 points required<br>
+                <strong>Example:</strong> &nbsp;1 2 3 &nbsp;or&nbsp; 1,2,3
+            </div>""")
+
+            with gr.Row():
+                run_btn   = gr.Button("▶  Run PCA",  variant="primary")
+                clear_btn = gr.Button("✕  Clear",    variant="secondary")
+
+        with gr.Column(scale=2):
+            with gr.Tabs():
+                with gr.Tab("📋 Steps & Results"):
+                    steps_out = gr.Textbox(label="Full PCA Computation",
+                                           interactive=False, lines=34)
+                with gr.Tab("🔵 3D Input Plot"):
+                    plot_3d = gr.Plot(label="Original 3D Points")
+                with gr.Tab("🟣 2D Projection"):
+                    plot_2d = gr.Plot(label="PCA 2D Projection")
+                with gr.Tab("📊 Variance Analysis"):
+                    plot_var = gr.Plot(label="Explained Variance")
+
+    run_btn.click(fn=run_pca, inputs=points_input,
+                  outputs=[steps_out, plot_3d, plot_2d, plot_var])
+    clear_btn.click(fn=lambda: ("", None, None, None),
+                    outputs=[points_input, plot_3d, plot_2d, plot_var])
+
+demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+gradio>=4.0.0
+numpy>=1.24.0
+matplotlib>=3.7.0
+Pillow>=10.0.0