app.py

import gradio as gr
import onnxruntime as rt
from transformers import AutoTokenizer
import torch, json


tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")

with open("label_types_encoded.json", "r") as fp:
    encode_genre_types = json.load(fp)

inf_session = rt.InferenceSession('food-classifier-quantized.onnx')
input_name = inf_session.get_inputs()[0].name
output_name = inf_session.get_outputs()[0].name


def classify_news_label(article):
    input_ids = tokenizer(article)['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(label, map(float, probs)))


label = gr.outputs.Label(num_top_classes=6)
iface = gr.Interface(fn=classify_news_label, inputs="text", outputs=label)
iface.launch(inline=False)