app.py

import gradio as gr
import os
import torch
import transformers

device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')

class Interactive:
    def __init__(self):
        self.tokenizer = transformers.AutoTokenizer.from_pretrained('liujch1998/cd-pi', use_auth_token=os.environ['HF_TOKEN_DOWNLOAD'])
        self.model = transformers.AutoModelForSeq2SeqLM.from_pretrained('liujch1998/cd-pi', use_auth_token=os.environ['HF_TOKEN_DOWNLOAD']).to(device)
        self.linear = torch.nn.Linear(self.model.shared.embedding_dim, 1).to(device)
        self.linear.weight = torch.nn.Parameter(self.model.shared.weight[32099, :].unsqueeze(0)) # (1, D)
        self.linear.bias = torch.nn.Parameter(self.model.shared.weight[32098, 0].unsqueeze(0)) # (1)
        self.model.eval()
        self.t = 2.2247

    def run(self, statement):
        input_ids = self.tokenizer.batch_encode_plus([statement], return_tensors='pt', padding='longest').input_ids.to(device)
        with torch.no_grad():
            output = self.model(input_ids)
            last_hidden_state = output.last_hidden_state.to(device) # (B=1, L, D)
            hidden = last_hidden_state[0, -1, :] # (D)
            logit = self.linear(hidden).squeeze(-1) # ()
            logit_calibrated = logit / self.t
            score = logit.sigmoid()
            score_calibrated = logit_calibrated.sigmoid()
        return {
            'logit': logit.item(),
            'logit_calibrated': logit_calibrated.item(),
            'score': score.item(),
            'score_calibrated': score_calibrated.item(),
        }

interactive = Interactive()

def predict(statement, model):
    result = interactive.run(statement)
    return {
        'True': result['score_calibrated'],
        'False': 1 - result['score_calibrated'],
    }

examples = [
    'If A sits next to B and B sits next to C, then A must sit next to C.',
    'If A sits next to B and B sits next to C, then A might not sit next to C.',
]

input_statement = gr.Dropdown(choices=examples, label='Statement:')
input_model = gr.Textbox(label='Commonsense statement verification model:', value='liujch1998/cd-pi', interactive=False)
output = gr.outputs.Label(num_top_classes=2)

description = '''This is a demo for a commonsense statement verification model. Under development.'''

gr.Interface(
    fn=predict,
    inputs=[input_statement, input_model],
    outputs=output,
    title="cd-pi Demo",
    description=description,
).launch()