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app.py

@@ -39,8 +39,8 @@ sets_un = np.unique(sets)
 print('unique sets:', sets_un)
 print(type(sets_un))
 
-
-for c in features:
+desired_order = ["Card", "Year", "Card Set", "Artwork Style", "Condition", "Market Value", "Set Number Eq"]
+for c in desired_order:
   input_names.append(c)
   if c=="Card":
     inputs.append(gr.Textbox(value="", label=c))
@@ -57,6 +57,30 @@ for c in features:
   elif c=="Set Number Eq":
     inputs.append(gr.Number(label=c, value = 0.0))
 
+examples_dicts = [
+    {
+        "Card": "Pikachu V",
+        "Year": 2021,
+        "Card Set": "Journey Together",
+        "Artwork Style": "Full Art",
+        "Condition": "Mint",
+        "Market Value": 50.0,
+        "Set Number Eq": 0.6
+    },
+    {
+        "Card": "Charizard",
+        "Year": 1999,
+        "Card Set": "Base Set",
+        "Artwork Style": "Holo",
+        "Condition": "Near Mint",
+        "Market Value": 12.0,
+        "Set Number Eq": 1.4
+    }
+]
+
+# Convert examples dicts into positional lists matching input_names
+examples = [[ex[name] for name in input_names] for ex in examples_dicts]
+
 def predict_record(*args):
   record = {name: val for name, val in zip(input_names, args)}
   df_in = pd.DataFrame([record])
@@ -67,14 +91,10 @@ iface = gr.Interface(
   inputs=inputs,
   outputs=gr.Textbox(label="Is this card a collector's item?"),
   title="Pokémon Card Collector's Item Predictor (AutoGluon)",
-  description="Predicts whether a Pokémon card is a collector's item."
+  description="Predicts whether a Pokémon card is a collector's item.",
+  examples=examples
   )
 
-examples = [
-    ["Pikachu V", 2021, "Journey Together", "Full Art", "Mint", 50.0, 0.6],
-    ["Charizard", 1999, "Base Set", "Holo", "Near Mint", 12.0, 1.4]
-]
-iface.examples = examples
 
 if __name__ == "__main__":
   iface.launch()