app.py

import gradio as gr
from transformers import BartForSequenceClassification, BartTokenizer
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")

te_tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-mnli')
te_model = BartForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
qa_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
qa_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small", device_map="auto")

def predict(context, intent, multi_class):
    input_text = "In one word, what is the opposite of: " + intent + "?"
    input_ids = qa_tokenizer(input_text, return_tensors="pt").input_ids.to(device)
    opposite_output = qa_tokenizer.decode(qa_model.generate(input_ids, max_length=2)[0])
    input_text = "In one word, what is the following describing: " + context
    input_ids = qa_tokenizer(input_text, return_tensors="pt").input_ids.to(device)
    object_output = qa_tokenizer.decode(qa_model.generate(input_ids, max_length=2)[0])
    batch = ['I think the ' + object_output + ' is ' + intent, 'I think the ' + object_output + ' is ' + opposite_output, 'I think the ' + object_output  + ' are neither ' + intent + ' nor ' + opposite_output]
    outputs = []
    for i, hypothesis in enumerate(batch):
        input_ids = te_tokenizer.encode(context, hypothesis, return_tensors='pt').to(device)
        # -> [contradiction, neutral, entailment]
        logits = te_model(input_ids)[0][0]

        if (i == 2):
            # -> [contradiction, entailment]
            probs = logits[[0,2]].softmax(dim=0)
        else:
            probs = logits.softmax(dim=0)
        outputs.append(probs)
        
    # -> [entailment, contradiction]
    outputs[2] = outputs[2].flip(dims=[0])
    # -> [entailment, neutral, contradiction]
    outputs[0] = outputs[0].flip(dims=[0])
    pn_tensor = (outputs[0] + outputs[1])/2
    pn_tensor[1] = pn_tensor[1] * outputs[2][0]
    pn_tensor[2] = pn_tensor[2] * outputs[2][1]
    pn_tensor[0] = pn_tensor[0] * outputs[2][1]
    
    pn_tensor = pn_tensor.exp() - 1

    if (multi_class):
        pn_tensor = torch.sigmoid(pn_tensor)
    else:
        pn_tensor = pn_tensor.softmax(dim=0)
    pn_tensor = pn_tensor.tolist()
    return {"agree": pn_tensor[0], "neutral": pn_tensor[1], "disagree": pn_tensor[2]}

gradio_app = gr.Interface(
    predict,
    inputs=[gr.Text(label="Sentence"), gr.Text(label="Class"), gr.Checkbox(label="Allow multiple true classes")],
    outputs=[gr.Label(num_top_classes=3)],
    title="Intent Analysis",
    description="This model predicts whether or not the **class** describes the **object described in the sentence.**"
)

gradio_app.launch()