example

app.py

@@ -4,16 +4,13 @@ import torch
 import onnxruntime as rt
 from transformers import AutoTokenizer
 
-
 tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
 
 with open("genre_types_encoded_kaggle_onnx.json", "r") as file:
     encode_genre_types = json.load(file)
 
-
 genres = list(encode_genre_types.keys())
 
-
 inf_session = rt.InferenceSession("game-classifier-quantized-kaggle.onnx")
 input_name = inf_session.get_inputs()[0].name
 output_name = inf_session.get_outputs()[0].name
@@ -27,6 +24,11 @@ def classify_game_genre(summary):
     return dict(zip(genres, map(float, probs)))
 
 
+examples = [
+    ["March Of Soldiers is a real time strategy single player,"
+     "It is a military game based on the player's skill and the strength of his financial economy"]
+]
+
 labels = gr.outputs.Label(num_top_classes=5)
-iface = gr.Interface(fn=classify_game_genre, inputs="text", outputs=labels)
+iface = gr.Interface(fn=classify_game_genre, inputs="text", outputs=labels, examples=examples)
 iface.launch(inline=False)