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app.py

@@ -51,7 +51,24 @@ ADAPTER_TASKS = [t for t in TASKS if not t.is_base]
 ADAPTER_CHOICES = [(t.display_name, t.key) for t in ADAPTER_TASKS]
 
 EXAMPLES_DIR = Path(__file__).parent / "examples"
-EXAMPLE_IMAGES = sorted(str(p) for p in EXAMPLES_DIR.glob("*.jpg")) if EXAMPLES_DIR.exists() else []
+
+FOOD_NAMES = {"baklava", "donut", "dumplings", "hotdog"}
+FOOD_TASK_KEY = "food101" if any(t.key == "food101" for t in TASKS) else BASE_TASK_NAME
+
+
+def _collect_examples() -> list[tuple[str, str]]:
+    """Return (path, default_task_key) pairs for each example image."""
+    if not EXAMPLES_DIR.exists():
+        return []
+    pairs = []
+    for p in sorted(EXAMPLES_DIR.glob("*.jpg")):
+        task_key = FOOD_TASK_KEY if p.stem.lower() in FOOD_NAMES else BASE_TASK_NAME
+        pairs.append((str(p), task_key))
+    return pairs
+
+
+EXAMPLE_PAIRS = _collect_examples()
+EXAMPLE_IMAGES = [p for p, _ in EXAMPLE_PAIRS]
 
 
 def _to_inputs(image):
@@ -132,14 +149,14 @@ with gr.Blocks(title="ViT + LoRA image classifier", theme=gr.themes.Soft()) as d
                 with gr.Column(scale=1):
                     output = gr.Label(num_top_classes=10, label="Predictions")
 
-            if EXAMPLE_IMAGES:
+            if EXAMPLE_PAIRS:
                 gr.Examples(
-                    examples=[[p, BASE_TASK_NAME, 5, 0.0] for p in EXAMPLE_IMAGES],
+                    examples=[[p, tk, 5, 0.0] for p, tk in EXAMPLE_PAIRS],
                     inputs=[image, task, top_k, threshold],
                     outputs=output,
                     fn=classify,
                     cache_examples=False,
-                    label="Example images",
+                    label="Example images (animals default to ImageNet, foods to Food-101)",
                 )
 
             classify_args = dict(fn=classify, inputs=[image, task, top_k, threshold], outputs=output)