text_classify / app.py
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Update app.py
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import string
import gradio as gr
import requests
import torch
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
)
custom_labels = {0: "neg", 1: "pos"}
model_dir = r'model/sst-2-english'
# model = pipeline("sentiment-analysis",model=model_dir,device=0)
# print(model("you are bad boy."))
config = AutoConfig.from_pretrained(model_dir, num_labels=2, finetuning_task="text-classification")
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSequenceClassification.from_pretrained(model_dir, config=config)
model.config.id2label = custom_labels
model.config.label2id = {v: k for k, v in custom_labels.items()}
def inference(input_text):
inputs = tokenizer.batch_encode_plus(
[input_text],
max_length=512,
pad_to_max_length=True,
truncation=True,
padding="max_length",
return_tensors="pt",
)
with torch.no_grad():
logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
output = model.config.id2label[predicted_class_id]
return output
demo = gr.Interface(
fn=inference,
inputs=gr.Textbox(label="Input Text", scale=2, container=False),
outputs=gr.Textbox(label="Output Label"),
examples = [
["My last two weather pics from the storm on August 2nd. People packed up real fast after the temp dropped and winds picked up.", 1],
["Lying Clinton sinking! Donald Trump singing: Let's Make America Great Again!", 0],
],
title="Tutorial: BERT-based Text Classificatioin",
)
demo.launch(debug=True)