Add Gradio image demo without binary calibration PNGs

.gitattributes

@@ -1,3 +1,2 @@
 *.pth filter=lfs diff=lfs merge=lfs -text
 *.tar.gz filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text

assignments/assignment-1/app/shared/artifact_utils.py

@@ -9,6 +9,9 @@ import os
 from pathlib import Path
 from typing import Any, Dict, Optional
 
+import numpy as np
+from PIL import Image
+
 from .model_registry import CalibrationResult
 
 
@@ -18,6 +21,110 @@ ASSIGNMENT_ROOT = Path(
 ARTIFACTS_DIR = ASSIGNMENT_ROOT / "image" / "artifacts"
 
 
+def _render_reliability_diagram_from_metrics(metrics: Dict[str, Any]) -> np.ndarray:
+    """Render a reliability diagram directly from saved calibration metrics."""
+    import matplotlib
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    bin_accuracies = [float(x) for x in metrics["bin_accuracies"]]
+    bin_confidences = [float(x) for x in metrics["bin_confidences"]]
+    bin_counts = [int(x) for x in metrics["bin_counts"]]
+    ece = float(metrics["ece"])
+
+    n_bins = len(bin_accuracies)
+    bin_boundaries = np.linspace(0, 1, n_bins + 1)
+    bin_centers = [
+        (bin_boundaries[i] + bin_boundaries[i + 1]) / 2 for i in range(n_bins)
+    ]
+    total = max(sum(bin_counts), 1)
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))
+    fig.patch.set_facecolor("#0d1117")
+
+    ax1.set_facecolor("#161b22")
+    width = 0.08
+    ax1.bar(
+        [c - width / 2 for c in bin_centers],
+        bin_accuracies,
+        width,
+        label="Accuracy",
+        color="#58a6ff",
+        alpha=0.9,
+        edgecolor="#58a6ff",
+    )
+    ax1.bar(
+        [c + width / 2 for c in bin_centers],
+        bin_confidences,
+        width,
+        label="Avg Confidence",
+        color="#f97583",
+        alpha=0.9,
+        edgecolor="#f97583",
+    )
+    ax1.plot(
+        [0, 1],
+        [0, 1],
+        "--",
+        color="#8b949e",
+        linewidth=2,
+        label="Perfect Calibration",
+    )
+    ax1.set_xlim(0, 1)
+    ax1.set_ylim(0, 1)
+    ax1.set_xlabel("Confidence", color="white", fontsize=12)
+    ax1.set_ylabel("Accuracy / Confidence", color="white", fontsize=12)
+    ax1.set_title(
+        f"Reliability Diagram (ECE: {ece:.4f})",
+        color="white",
+        fontsize=14,
+        fontweight="bold",
+        pad=15,
+    )
+    ax1.legend(
+        facecolor="#161b22",
+        edgecolor="#30363d",
+        labelcolor="white",
+        fontsize=10,
+    )
+    ax1.tick_params(colors="white")
+    for spine in ax1.spines.values():
+        spine.set_edgecolor("#30363d")
+    ax1.grid(True, alpha=0.1, color="white")
+
+    ax2.set_facecolor("#161b22")
+    ax2.bar(
+        bin_centers,
+        [count / total for count in bin_counts],
+        0.08,
+        color="#56d364",
+        alpha=0.9,
+        edgecolor="#56d364",
+    )
+    ax2.set_xlim(0, 1)
+    ax2.set_xlabel("Confidence", color="white", fontsize=12)
+    ax2.set_ylabel("Fraction of Samples", color="white", fontsize=12)
+    ax2.set_title(
+        "Confidence Distribution",
+        color="white",
+        fontsize=14,
+        fontweight="bold",
+        pad=15,
+    )
+    ax2.tick_params(colors="white")
+    for spine in ax2.spines.values():
+        spine.set_edgecolor("#30363d")
+    ax2.grid(True, alpha=0.1, color="white")
+
+    plt.tight_layout(pad=3)
+    fig.canvas.draw()
+    rgba_buffer = fig.canvas.buffer_rgba()
+    diagram = np.array(rgba_buffer)[:, :, :3]
+    plt.close(fig)
+    return diagram
+
+
 def get_best_accuracy_from_history(history: Optional[Dict[str, Any]]) -> Optional[float]:
     """Return the best validation accuracy found in a checkpoint history."""
     if not history:
@@ -44,20 +151,24 @@ def load_precomputed_calibration_result(
         return None
 
     metrics_name = f"{model_tag}_calibration_metrics_{sample_tag}.json"
-    image_name = f"{model_tag}_calibration_{sample_tag}.png"
-
     metrics_path = next(ARTIFACTS_DIR.rglob(metrics_name), None)
+    image_name = f"{model_tag}_calibration_{sample_tag}.png"
     image_path = next(ARTIFACTS_DIR.rglob(image_name), None)
 
-    if metrics_path is None or image_path is None:
+    if metrics_path is None:
         return None
 
     metrics = json.loads(metrics_path.read_text(encoding="utf-8"))
+    if image_path is not None:
+        reliability_diagram = np.array(Image.open(image_path).convert("RGB"))
+    else:
+        reliability_diagram = _render_reliability_diagram_from_metrics(metrics)
+
     return CalibrationResult(
         ece=float(metrics["ece"]),
         bin_accuracies=[float(x) for x in metrics["bin_accuracies"]],
         bin_confidences=[float(x) for x in metrics["bin_confidences"]],
         bin_counts=[int(x) for x in metrics["bin_counts"]],
-        reliability_diagram=str(image_path),
+        reliability_diagram=reliability_diagram,
         source=f"Notebook artifact ({metrics_path.parent.name})",
     )