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import gradio as gr | |
import json | |
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 | |
def classify_game_genre(summary): | |
input_ids = tokenizer(summary)["input_ids"][:512] | |
logits = inf_session.run([output_name], {input_name: [input_ids]})[0] | |
logits = torch.FloatTensor(logits) | |
probs = torch.sigmoid(logits)[0] | |
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, examples=examples) | |
iface.launch(inline=False) | |