app.py

import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoModel, AutoTokenizer
import torch
from peft import PeftModel, PeftConfig

base_model = "cardiffnlp/twitter-roberta-base-sentiment-latest"
adapter_model = 'saideep-arikontham/twitter-roberta-base-sentiment-latest-trump-stance'

model = PeftModel.from_pretrained(model, adapter_model)
tokenizer = AutoTokenizer.from_pretrained(adapter_model)


def greet(text):
    
    model.to('mps')
    inputs = tokenizer.encode(text, return_tensors="pt").to("mps")
    # 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()