File size: 1,506 Bytes
7e13741 ced12f5 7e13741 ced12f5 7e13741 ced12f5 7e13741 ced12f5 7e13741 ced12f5 7e13741 ced12f5 7e13741 ced12f5 7e13741 ced12f5 71ec65b ced12f5 71ec65b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
import gradio as gr
import onnxruntime as rt
from transformers import AutoTokenizer
import torch
import json
# Initialize the tokenizer
tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
# Load genre types from a JSON file
try:
with open("genre_types_encoded.json", "r") as fp:
encode_genre_types = json.load(fp)
except FileNotFoundError:
print("Error: 'genre_types_encoded.json' not found. Make sure the file exists.")
exit(1)
# Extract genres from the loaded data
genres = list(encode_genre_types.keys())
# Load the ONNX inference session
try:
inf_session = rt.InferenceSession('udemy-classifier-quantized.onnx')
input_name = inf_session.get_inputs()[0].name
output_name = inf_session.get_outputs()[0].name
except FileNotFoundError:
print("Error: 'udemy-classifier-quantized.onnx' not found. Make sure the file exists.")
exit(1)
# Define the function for classifying courses' genres
def classify_courses_genre(description):
input_ids = tokenizer(description, truncation=True, padding=True, return_tensors="pt")['input_ids'][:,:512]
logits = inf_session.run([output_name], {input_name: input_ids.cpu().numpy()})[0]
logits = torch.FloatTensor(logits)
probs = torch.sigmoid(logits)[0]
return dict(zip(genres, map(float, probs)))
# Create the Gradio interface
iface = gr.Interface(fn=classify_courses_genre, inputs="text", outputs=gr.components.Label(num_top_classes=5))
# Launch the Gradio interface
iface.launch(inline=False)
|