app.py

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

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

with open("tag_types_encoded.json", "r") as fp:
    encode_tag_types = json.load(fp)

tags = list(encode_tag_types.keys())

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

def classify_book_genre(quotes):
    input_ids = tokenizer(quotes)['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(tags, map(float, probs)))
    
label = gr.outputs.Label(num_top_classes=5)
iface = gr.Interface(fn=classify_book_genre, inputs="text", outputs=label)
iface.launch(inline=False)