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(Share=True, inline = False)