Add kya workshop

zkml-research/kya_face/.gitignore

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+.venv/
+workshop_*

zkml-research/kya_face/README.md

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+# KYA Workshop — Local Setup Guide
+
+## 1  Prerequisites (install once)
+
+| Tool | Windows | macOS | Linux (Ubuntu) |
+|------|---------|-------|----------------|
+| **Python 3.10 or 3.11 × 64-bit** | [python.org](https://www.python.org) installer | `brew install [email protected]` | `sudo apt install python3.11 python3.11-venv` |
+| **C++ build tools**<br>(needed for `dlib` → `face_recognition`) | *Visual Studio Build Tools 2022* → “Desktop C++” workload | `xcode-select --install` | `sudo apt install build-essential` |
+| **CMake ≥ 3.22** | [cmake.org](https://cmake.org) installer | `brew install cmake` | `sudo apt install cmake` |
+| **Leo CLI** | Follow steps at <https://github.com/ProvableHQ/leo> | Follow steps at <https://github.com/ProvableHQ/leo> | Follow steps at <https://github.com/ProvableHQ/leo> |
+
+---
+
+## 2  Clone the repo & create a virtual env
+
+```bash
+git clone https://github.com/ProvableHQ/python-sdk.git
+cd zkml-research/kya_face
+
+# Create & activate a venv named .venv
+python -m venv .venv
+# Windows
+.venv\Scripts\activate
+# macOS/Linux
+source .venv/bin/activate
+```
+
+---
+
+## 3  Install Python dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+---
+
+## 4  Open the Jupyter notebook
+
+Open `kya.ipynb`, e.g., through VS Code (choosing the `venv`).

zkml-research/kya_face/face_images/positive_user/.gitignore

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+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore

zkml-research/kya_face/helper.py

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+from sklearn.utils.multiclass import unique_labels
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+import matplotlib.pyplot as plt
+
+def plot_mlp_architecture(clf, ax=None):
+    """
+    Plot the architecture of a Multi-layer Perceptron (MLP) classifier or regressor.
+    """
+    import matplotlib.patches as mpatches
+    import matplotlib.pyplot as plt
+    n_layers = clf.n_layers_
+    layer_sizes = [clf.coefs_[0].shape[0]] + [w.shape[1] for w in clf.coefs_]
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(8, 4))
+    # Draw nodes
+    y_offset = 0
+    for i, n in enumerate(layer_sizes):
+        x = i * 2
+        for j in range(n):
+            ax.add_patch(mpatches.Circle((x, j - n/2), 0.2, color='skyblue', ec='k'))
+        if i == 0:
+            ax.text(x, n/2 + 0.5, "Input\n({})".format(n), ha='center')
+        elif i == len(layer_sizes) - 1:
+            ax.text(x, n/2 + 0.5, "Output\n({})".format(n), ha='center')
+        else:
+            ax.text(x, n/2 + 0.5, "Hidden\n({})".format(n), ha='center')
+    # Draw connections
+    for i in range(len(layer_sizes) - 1):
+        x0, x1 = i * 2, (i + 1) * 2
+        for j in range(layer_sizes[i]):
+            for k in range(layer_sizes[i+1]):
+                ax.plot([x0, x1], [j - layer_sizes[i]/2, k - layer_sizes[i+1]/2], color='gray', lw=0.5, alpha=0.5)
+    ax.axis('off')
+    ax.set_title("MLP Architecture")
+    plt.show()
+
+def summarize_mlp(clf):
+    """
+    Print the architecture (layer sizes) and total number of parameters
+    for an sklearn MLPClassifier or MLPRegressor.
+
+    Parameters
+    ----------
+    clf : object
+        A fitted sklearn.neural_network.MLPClassifier or MLPRegressor.
+    """
+    # Reconstruct full layer sizes (input, hidden…, output)
+    layer_sizes = (
+        [clf.coefs_[0].shape[0]]
+        + list(clf.hidden_layer_sizes)
+        + [clf.coefs_[-1].shape[1]]
+    )
+
+    # Print architecture
+    print("Layer sizes (including input and output):", layer_sizes)
+    print(f" ➔ Total layers (including input layer): {len(layer_sizes)}")
+    print(f" ➔ Hidden layers: {len(clf.hidden_layer_sizes)}")
+    print(" ➔ Output layer: 1")
+
+    # Compute total parameters (weights + biases)
+    total_params = sum(
+        w.size + b.size for w, b in zip(clf.coefs_, clf.intercepts_)
+    )
+    print(f"Total parameters: {total_params:,}")
+
+
+
+def plot_confusion_matrix(y_true, y_pred, *,
+                          labels=None,
+                          normalize=None,
+                          ax=None,
+                          title="Confusion matrix",
+                          cmap="Blues"):
+    """
+    Plot a confusion matrix for classification results.
+
+    Parameters
+    ----------
+    y_true : array-like
+        Ground-truth labels.
+    y_pred : array-like
+        Predicted labels.
+    labels : list, optional
+        Class labels (order on both axes). If None, uses the union of labels
+        present in y_true and y_pred.
+    normalize : {'true', 'pred', 'all'}, default None
+        Normalization mode passed to sklearn.metrics.confusion_matrix.
+    ax : matplotlib.axes.Axes, optional
+        Existing axes to draw on. If None, a new figure/axes is created.
+    title : str, default "Confusion matrix"
+        Title shown above the plot.
+    cmap : str or matplotlib Colormap, default "Blues"
+        Colormap used for the heat-map.
+
+    Returns
+    -------
+    numpy.ndarray
+        The underlying confusion-matrix array (for further inspection if needed).
+    """
+    if labels is None:
+        labels = unique_labels(y_true, y_pred)
+
+    cm = confusion_matrix(y_true, y_pred, labels=labels, normalize=normalize)
+
+    if ax is None:
+        _, ax = plt.subplots(figsize=(4, 4))
+
+    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)
+    disp.plot(ax=ax, cmap=cmap, colorbar=False)
+    ax.set_title(title)
+    plt.tight_layout()
+    plt.show()

zkml-research/kya_face/requirements.txt

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+# -------------------------------------------------
+# KYA Face ID demo – pinned, known-good versions
+# -------------------------------------------------
+numpy>=1.26,<2.0
+pandas>=2.2,<3.0
+matplotlib>=3.9,<4.0
+scikit-learn>=1.5,<1.6
+Pillow>=10.3,<11.0
+face_recognition==1.3.0        # needs CMake & compiler
+face_recognition_models>=0.3.0
+zkml>=0.0.2b2
+jupyterlab>=4.2,<5.0
+ipykernel>=6.29,<7.0
+pyobjc-framework-AVFoundation; sys_platform == "darwin"