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(1).json", "r") as fp:
    encode_genre_types = json.load(fp)

genres = list(encode_genre_types.keys())

inf_session = rt.InferenceSession('Quote-classifier-quantized (3).onnx')
input_name = inf_session.get_inputs()[0].name
output_name = inf_session.get_outputs()[0].name

def classify_Quote_tag(Quote):
    input_ids = tokenizer(Quote)['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))) 

iface = gr.Interface(
    fn=classify_Quote_tag,
    inputs="text",
    outputs=gr.Label(num_top_classes=5)  # Use gr.Label for the label output
)
iface.launch(inline=False)