app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
import numpy as np

tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', 'bert-base-cased-finetuned-mrpc')
model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', 'bert-base-cased-finetuned-mrpc')
model = model.from_pretrained("jacklindsai/is_it_elon_musk")

def preprocess_text(text):
    return tokenizer.encode_plus(text, truncation=True, padding='max_length', max_length=48, return_attention_mask=True)

device = torch.device('cpu')
def pred_is_elon_musk(text):
    encoded_text = preprocess_text(text)
    ids = encoded_text['input_ids']
    masks = encoded_text['attention_mask']
    ids = torch.Tensor([ids]).to(device, dtype=torch.int32)
    masks = torch.Tensor([masks]).to(device, dtype=torch.int32)
    results = model(input_ids=ids, token_type_ids=None,
                    attention_mask=masks)
    logis = results['logits'].detach()
    prob = F.softmax(logis, dim=1)[0][1]
    prediction = np.argmax(logis.numpy(), axis=1).flatten()
    output1 = f"The predicted probability is {prob*100: 0.2f}%.\n"
    if 0.4 <= prob <= 0.6:
        output2 = f"Therefore, maybe it's from Elon Musk or maybe not."
    elif prediction[0] == 1:
        output2 = f"Therefore, maybe it is from Elon Musk."
    else:
        output2 = f"Therefore, maybe it is Not from Elon Musk."
    return output1 + output2

iface = gr.Interface(pred_is_elon_musk, inputs="text", 
    outputs="text", title='“Is the tweet from Elon Musk?” Classifier', 
    theme = "huggingface", examples=["Now I'm going to buy McDonald's and fix all the ice cream machines...",
    '"Real magic is only a sip away."(Actual slogan of Coca-Cola!!) 🤣🤣',
    'Let’s make Twitter maximum fun!',
    'I hope that even my worst critics remain on Twitter, because that is what free speech means'], 
    description="This app predicts whether the tweet is from Elon Musk based on a fine-tuned BERT model. The model considers the first 48 words at most.")

iface.launch(inline=False)