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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=5)
iface = gr.Interface(fn=classify_movie_genre, inputs="text", outputs=label)
iface.launch(inline=False) |