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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() |