import gradio as gr
import numpy as np

gr.Interface.load("models/openai-gpt")

def predict(sentence1, sentence2):
    sentence_pairs = np.array([[str(sentence1), str(sentence2)]])
    test_data = BertSemanticDataGenerator(
        sentence_pairs, labels=None, batch_size=1, shuffle=False, include_targets=False,
    )
    probs = model.predict(test_data[0])[0]
    
    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
    return labels_probs

examples = [["Two women are observing something together.", "Two women are standing with their eyes closed."],
            ["A smiling costumed woman is holding an umbrella", "A happy woman in a fairy costume holds an umbrella"],
            ["A soccer game with multiple males playing", "Some men are playing a sport"],            
]

gr.Interface(
    fn=predict,
    title="basic with GPT",
    description = "Natural Language Inference by fine-tuning GPT model",
    inputs=["text", "text"],
    examples=examples,
    #outputs=gr.Textbox(label='Prediction'),
    outputs=gr.outputs.Label(num_top_classes=3, label='Semantic similarity'),
    cache_examples=True
).launch(debug=True, enable_queue=True)

# gr.Interface.load("models/openai-gpt").launch()