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| import numpy as np | |
| import gradio as gr | |
| from sentence_transformers import SentenceTransformer | |
| minilm = SentenceTransformer('all-MiniLM-L12-v2') | |
| roberta = SentenceTransformer('all-distilroberta-v1') | |
| glove = SentenceTransformer('average_word_embeddings_glove.840B.300d') | |
| labels = ["contradiction", "entailment", "neutral"] | |
| def predict(sentence1, sentence2): | |
| sentence_pairs = np.array([[str(sentence1), str(sentence2)]]) | |
| print(sentence1) | |
| print(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="Semantic Song Search", | |
| description = "Search for songs based on the meaning in the song's lyrics using a variety of embeddings", | |
| inputs=["text", "text"], | |
| examples=examples, | |
| #outputs=gr.Textbox(label='Prediction'), | |
| outputs=gr.outputs.Label(num_top_classes=3, label='Semantic similarity'), | |
| cache_examples=True, | |
| article = "Author: @sheacon", | |
| ).launch(debug=True, enable_queue=True) |