File size: 828 Bytes
bb22eb8 9d79f7e 8d027cb 2244b0c 8d027cb 71828e8 8d027cb 259344b 8d027cb 5f09d1f 374cec5 8a0cad7 1b99eaa 260fdda 1b99eaa bdd14b5 9d79f7e bdd14b5 5f09d1f |
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 |
import gradio as gr
import json
from transformers import AutoTokenizer, pipeline
from optimum.onnxruntime import ORTModelForSequenceClassification
model_id = "SamLowe/roberta-base-go_emotions-onnx"
file_name = "onnx/model_quantized.onnx"
model = ORTModelForSequenceClassification.from_pretrained(model_id, file_name=file_name)
tokenizer = AutoTokenizer.from_pretrained(model_id)
classifier = pipeline(
task="text-classification",
model=model,
tokenizer=tokenizer,
top_k=None,
function_to_apply="sigmoid",
binary_output=True,
)
def predict(param_0):
results = classifier(param_0)
response = {
"label": results[0][0]["label"],
"confidences": results[0]
}
return response
demo = gr.Interface(
fn = predict,
inputs = 'text',
outputs = 'json',
)
demo.launch() |