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import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
from peft import PeftModel, PeftConfig
import re
base_model = "cardiffnlp/twitter-roberta-base-sentiment-latest"
adapter_model = 'saideep-arikontham/twitter-roberta-base-sentiment-latest-trump-stance-1'
# define label maps
id2label = {0: "Anti-Trump", 1 : "Pro-Trump"}
label2id = {"Anti-Trump" : 0, "Pro-Trump" : 1}
# generate classification model from model_checkpoint
model = AutoModelForSequenceClassification.from_pretrained(base_model, num_labels=2, id2label = id2label, label2id = label2id, ignore_mismatched_sizes=True)
model = PeftModel.from_pretrained(model, adapter_model)
tokenizer = AutoTokenizer.from_pretrained(adapter_model)
def greet(text):
model.to('cpu')
inputs = tokenizer.encode(text, return_tensors="pt").to("cpu")
# compute logits
logits = model(inputs).logits
# convert logits to label
predictions = torch.argmax(logits)
return "This text is " + id2label[predictions.tolist()] + "!!"
demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch() |