Feat: initial demo publish

app.py

@@ -0,0 +1,167 @@
+import gradio as gr
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import plotly.graph_objects as go
+
+torch.manual_seed(42)
+np.random.seed(42)
+
+# Intercepts encode baseline preferences when no rivals are encountered.
+# Luke pushes asparagus & milk; Rey pushes steak & milk.
+B_true = np.array([
+# intercepts: asp milk appl steak
+[ 0.6, 0.3, 0.0, -0.2],
+# Luke effect:
+[ 0.8, 0.5, -0.2, -0.6],
+# Rey effect:
+[-0.6, 0.5, -0.3, 0.9],
+])
+
+FOODS = ['Asparagus', 'Milk', 'Apples', 'Steak']
+COLORS = ['#00CC00', '#0066FF', '#FF0000', '#FF9900']
+
+# ============================================================================
+# DATA & MODEL
+# ============================================================================
+def softmax(z):
+    z = np.atleast_2d(z)
+    z = z - np.max(z, axis=1, keepdims=True)
+    return np.exp(z) / np.sum(np.exp(z), axis=1, keepdims=True)
+
+def generate_logs(n=100):
+    """Simulate Kylo's ~100 weeks of logs."""
+    luke = np.random.randint(0, 6, n)
+    rey = np.random.randint(0, 6, n)
+    X = np.column_stack([np.ones(n), luke, rey])
+    probs = softmax(X @ B_true)
+    choices = np.array([np.random.choice(4, p=p) for p in probs])
+    return X, choices
+
+class SoftmaxModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(3, 4, bias=False)
+
+    def forward(self, x):
+        return self.linear(x)
+
+def train_model(X, y):
+    """Fit multinomial logistic regression in PyTorch."""
+    model = SoftmaxModel()
+    opt = optim.Adam(model.parameters(), lr=0.5)
+    loss_fn = nn.CrossEntropyLoss()
+    X_t = torch.tensor(X, dtype=torch.float32)
+    y_t = torch.tensor(y, dtype=torch.long)
+
+    for _ in range(500):
+        opt.zero_grad()
+        loss_fn(model(X_t), y_t).backward()
+        opt.step()
+
+    return model.linear.weight.detach().numpy().T
+
+# Train model once on startup
+X_train, y_train = generate_logs(100)
+B_learned = train_model(X_train, y_train)
+
+# ============================================================================
+# SIMPLEX VISUALIZATION
+# ============================================================================
+def make_simplex(luke, rey):
+    """Show predicted probabilities on tetrahedron for given encounters."""
+    # Predict using learned model
+    x = np.array([[1, luke, rey]])
+    probs = softmax(x @ B_learned)[0]
+
+    # Tetrahedron vertices
+    v = np.array([
+        [1, 0, -1/np.sqrt(2)],
+        [-1, 0, -1/np.sqrt(2)],
+        [0, 1, 1/np.sqrt(2)],
+        [0, -1, 1/np.sqrt(2)],
+    ])
+
+    # Predicted point location
+    pt = probs @ v
+
+    fig = go.Figure()
+
+    # Draw tetrahedron edges
+    for i, j in [(0,1), (0,2), (0,3), (1,2), (1,3), (2,3)]:
+        fig.add_trace(go.Scatter3d(
+            x=[v[i,0], v[j,0]], y=[v[i,1], v[j,1]], z=[v[i,2], v[j,2]],
+            mode='lines', line=dict(color='gray', width=2),
+            showlegend=False, hoverinfo='skip'
+        ))
+
+    # Draw predicted point
+    fig.add_trace(go.Scatter3d(
+        x=[pt[0]], y=[pt[1]], z=[pt[2]],
+        mode='markers', marker=dict(size=12, color='white', symbol='diamond'),
+        name='Prediction'
+    ))
+
+    # Vertex labels with probabilities
+    for i, (food, color) in enumerate(zip(FOODS, COLORS)):
+        label = f"{food}\n({probs[i]:.1%})"
+        fig.add_trace(go.Scatter3d(
+            x=[v[i,0]*1.3], y=[v[i,1]*1.3], z=[v[i,2]*1.3],
+            mode='text', text=[label], textfont=dict(size=14, color=color),
+            showlegend=False, hoverinfo='skip'
+        ))
+
+    fig.update_layout(
+        title=f"Luke: {luke}, Rey: {rey}",
+        scene=dict(
+            xaxis=dict(visible=False),
+            yaxis=dict(visible=False),
+            zaxis=dict(visible=False),
+            bgcolor='#1a1a1a'
+        ),
+        paper_bgcolor='#1a1a1a',
+        font=dict(color='white'),
+        margin=dict(l=0, r=0, t=40, b=0),
+        height=500
+    )
+    return fig
+
+def predict(luke, rey):
+    x = np.array([[1, luke, rey]])
+    probs = softmax(x @ B_learned)[0]
+    fig = make_simplex(int(luke), int(rey))
+
+    text = f"**Predicted probabilities for {int(luke)} Luke, {int(rey)} Rey encounters:**\n\n"
+    for food, p, color in zip(FOODS, probs, COLORS):
+        text += f"- {food}: {p:.1%}\n"
+
+    return fig, text
+
+# ============================================================================
+# GRADIO APP
+# ============================================================================
+with gr.Blocks(title="Kylo's Cravings") as demo:
+    gr.Markdown("# Kylo Ren's Craving Predictor")
+    gr.Markdown("Model trained on ~100 weeks of logs. Adjust sliders to predict cravings.")
+
+    with gr.Row():
+        luke_slider = gr.Slider(0, 10, value=0, step=1, label="Luke encounters this week")
+        rey_slider = gr.Slider(0, 10, value=0, step=1, label="Rey encounters this week")
+
+    plot = gr.Plot()
+    output = gr.Markdown()
+
+    luke_slider.change(predict, [luke_slider, rey_slider], [plot, output])
+    rey_slider.change(predict, [luke_slider, rey_slider], [plot, output])
+
+    demo.load(predict, [luke_slider, rey_slider], [plot, output])
+
+    gr.Markdown(f"""---
+**Learned B:**
+```
+{np.array2string(B_learned, precision=2)}
+```
+""")
+
+demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+numpy<2
+torch
+plotly