import gradio as gr
from transformers import BartForSequenceClassification, BartTokenizer
import torch.nn.functional as F
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
from transformers import Pipeline

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-base")
qa_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto")

def predict(context, intent):
    input_text = "In one word, what is the opposite of: " + intent + "?"
    input_ids = qa_tokenizer(input_text, return_tensors="pt")
    encoded_input = qa_tokenizer(input_ids, return_tensors="pt")
    opposite_output = qa_tokenizer.decode(qa_model.generate(encoded_input)[0])
    input_text = "In one word, what is the following describing: " + context
    input_ids = qa_tokenizer(input_text, return_tensors="pt")
    encoded_input = qa_tokenizer(input_ids, return_tensors="pt")
    object_output = qa_tokenizer.decode(qa_model.generate(encoded_input)[0])
    batch = ['I think the ' + object_output + ' are long.', 'I think the ' + object_output + ' are ' + opposite_output, 'I think the ' + object_output + ' are the perfect']
    outputs = []
    for i, hypothesis in enumerate(batch):
        input_ids = te_tokenizer.encode(context, hypothesis, return_tensors='pt')
        # -> [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]).softmax(dim=0)
    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 = F.normalize(pn_tensor, p=1, dim=0)

    pn_tensor = pn_tensor.softmax(dim=0)
    return {"entailment": pn_tensor[0].item(), "neutral": pn_tensor[1].item(), "contradiction": pn_tensor[2].item()}

gradio_app = gr.Interface(
    predict,
    inputs=gr.Text(label="Input sentence"),
    outputs=[gr.Label(num_top_classes=3)],
    title="Hot Dog? Or Not?",
)

if __name__ == "__main__":
    gradio_app.launch()