| import gradio as gr | |
| import onnxruntime as rt | |
| from transformers import AutoTokenizer | |
| import torch | |
| import json | |
| tokenizer = AutoTokenizer.from_pretrained("distilroberta-base") | |
| with open("genre_types_encoded.json", "r") as fp: | |
| encode_genre_types = json.load(fp) | |
| genres = list(encode_genre_types.keys()) | |
| inf_session = rt.InferenceSession('genre-classifier-quantized.onnx') | |
| input_name = inf_session.get_inputs()[0].name | |
| output_name = inf_session.get_outputs()[0].name | |
| def classify_movie_genre(summary): | |
| tokens = tokenizer(summary, padding='max_length', truncation=True, return_tensors="pt") | |
| input_ids = tokens['input_ids'][0].tolist()[:512] | |
| print("Input summary:", summary) | |
| print("Tokenized input:", input_ids) | |
| logits = inf_session.run([output_name], {input_name: [input_ids]})[0] | |
| logits = torch.FloatTensor(logits) | |
| probs = torch.sigmoid(logits)[0] | |
| print("Logits:", logits) | |
| print("Probabilities:", probs) | |
| return dict(zip(genres, map(float, probs))) | |
| label = gr.Label(num_top_classes=10) | |
| iface = gr.Interface(fn=classify_movie_genre, inputs="text", outputs=label) | |
| iface.launch(inline=False) |