import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
te_tokenizer = AutoTokenizer.from_pretrained('MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli')
te_model = AutoModelForSequenceClassification.from_pretrained('MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli').to(device)
qa_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
qa_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto")

def predict(context, intent, multi_class):
    input_text = "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], skip_special_tokens=True)
    input_text = "What object 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], skip_special_tokens=True)
    batch = ['The ' + object_output + ' is ' + intent, 'The ' + object_output + ' is ' + opposite_output, 'The ' + object_output + ' is neither ' + intent + ' nor ' + opposite_output]
    outputs = []
    normal = 0
    
    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 == 0):
            normal = logits
        if (i >= 2):
            # -> [contradiction, entailment]
            probs = logits[[0,2]].softmax(dim=0)
        else:
            probs = torch.exp(logits)
        outputs.append(probs)
    # calculate the stochastic vector for it being neither the positive or negative class
    perfect_prob = outputs[2]
    # -> [entailment, contradiction] for perfect
    
    # -> [entailment, neutral, contradiction] for positive
    outputs[1] = outputs[1].flip(dims=[0])
    
    # combine the negative and positive class by summing by the opposite of the negative class
    aggregated = (outputs[0] + outputs[1])/2
    
    # multiplying vectors
    aggregated[1] = aggregated[1] * perfect_prob[0]
    aggregated[0] = aggregated[0] * perfect_prob[1]
    aggregated[2] = aggregated[2] * perfect_prob[1]
    
    aggregated = torch.sqrt(aggregated)

    # multiple true classes
    if (multi_class):
        aggregated = torch.sigmoid(aggregated)
        normal = torch.sigmoid(normal)
    # only one true class
    else:
        aggregated = aggregated.softmax(dim=0)
        normal = normal.softmax(dim=0)
    aggregated = aggregated.tolist()
    return {"agree": aggregated[0], "neutral": aggregated[1], "disagree": aggregated[2]}, {"agree": normal[0], "neutral": normal[1], "disagree": normal[2]}
examples = [["These are my absolute favorite cargos in my closet. I’m 5’7 and they’re actually long enough for me. I’m 165lbs and ordered an M & it fits nice and loose just how I wanted it. The adjustable waist band is awesome!", "long"], ["I feel strongly about politics in the US", "long"], ["The pants are long", "long"], ["The pants are slightly long", "long"]]

gradio_app = gr.Interface(
    predict,
    examples=examples,
    inputs=[gr.Text(label="Statement"), gr.Text(label="Class"), gr.Checkbox(label="Allow multiple true classes")],
    outputs=[gr.Label(num_top_classes=3, label="With Postprocessing"), gr.Label(num_top_classes=3, label="Without Postprocessing")],
    title="Intent Analysis",
    description="This model predicts whether or not the **_class_** describes the **_object described in the sentence_**. <br /> The two outputs shows what TE would predict with and without the postprocessing. An example edge case for normal TE is shown below. <br /> **_It is recommended that you clone the repository to speed up processing time_**. <br /> Additionally, note the difference between the strength of the probability when going between the last two examples, the former representing a strong opinion and the latter a weaker opinion",
    cache_examples=True
)

gradio_app.launch()