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Running
on
Zero
Running
on
Zero
File size: 1,472 Bytes
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import gradio
import json
import torch
from transformers import AutoTokenizer
from transformers import pipeline
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from optimum.onnxruntime import ORTModelForQuestionAnswering
# CORS Config
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["https://jhuhman.com"], #["https://statosphere-3704059fdd7e.c5v4v4jx6pq5.win"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
model_name = "xenova/mobilebert-uncased-mnli"
model = ORTModelForQuestionAnswering.from_pretrained(model_name, file_name="onnx/model.onnx")
tokenizer = AutoTokenizer.from_pretrained("typeform/mobilebert-uncased-mnli", model_max_length=512)
# file = cached_download("https://huggingface.co/" + model_name + "")
# sess = InferenceSession(file)
classifier = pipeline(task="zero-shot-classification", model=model, tokenizer=tokenizer)
def zero_shot_classification(data_string):
print(data_string)
data = json.loads(data_string)
print(data)
results = classifier(data['sequence'], candidate_labels=data['candidate_labels'], hypothesis_template=data['hypothesis_template'], multi_label=data['multi_label'])
response_string = json.dumps(results)
return response_string
gradio_interface = gradio.Interface(
fn = zero_shot_classification,
inputs = gradio.Textbox(label="JSON Input"),
outputs = gradio.Textbox()
)
gradio_interface.launch() |