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Running
Shea
commited on
Commit
•
58a182b
1
Parent(s):
542351c
try new interface
Browse files
app.py
CHANGED
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import gradio as gr
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from sentence_transformers import SentenceTransformer
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minilm = SentenceTransformer('all-MiniLM-L12-v2')
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#roberta = SentenceTransformer('all-distilroberta-v1')
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#glove = SentenceTransformer('average_word_embeddings_glove.840B.300d')
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def predict(sentence1, sentence2):
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sentence_pairs = np.array([[str(sentence1), str(sentence2)]])
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print(sentence1)
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print(sentence2)
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# test_data = BertSemanticDataGenerator(
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# sentence_pairs, labels=None, batch_size=1, shuffle=False, include_targets=False,
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# )
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# probs = model.predict(test_data[0])[0]
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# labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
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# return labels_probs
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examples = [["Two women are observing something together.", "Two women are standing with their eyes closed."],
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["A smiling costumed woman is holding an umbrella", "A happy woman in a fairy costume holds an umbrella"],
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["A soccer game with multiple males playing", "Some men are playing a sport"],
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]
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gr.Interface(
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fn=predict,
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title="Semantic Song Search",
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description = "Search for songs based on the meaning in the song's lyrics using a variety of embeddings",
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inputs=["text", "text"],
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examples=examples,
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#outputs=gr.Textbox(label='Prediction'),
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outputs=gr.outputs.Label(num_top_classes=3, label='Semantic similarity'),
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cache_examples=True,
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article = "Author: @sheacon",
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).launch(debug=True, enable_queue=True)
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import gradio as gr
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import pandas as pd
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import numpy as np
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from sklearn.metrics.pairwise import cosine_similarity
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from sentence_transformers import SentenceTransformer
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from datasets import load_dataset
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dataset = load_dataset(
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"sheacon/song_lyrics",
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revision="main" # tag name, or branch name, or commit hash
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)
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df = dataset.to_pandas()
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minilm = SentenceTransformer('all-MiniLM-L12-v2')
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#roberta = SentenceTransformer('all-distilroberta-v1')
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#glove = SentenceTransformer('average_word_embeddings_glove.840B.300d')
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# Tokenize and encode the song lyrics using the embedding model
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song_embeddings = df["embedding"].tolist()
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def search_songs(text, top_n=5):
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# Tokenize and encode the text entry using the same embedding model
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text_embedding = minilm([text])[0]
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# Calculate the cosine similarity between the text entry embedding and each song embedding
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similarities = cosine_similarity([text_embedding], song_embeddings)[0]
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# Sort the songs by similarity score and return the top N songs with their titles and lyrics
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top_indices = similarities.argsort()[::-1][:top_n]
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results = [{"title": df.iloc[i]["title"], "lyrics": df.iloc[i]["lyrics"]} for i in top_indices]
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return results
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# Define the Gradio interface
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iface = gr.Interface(search_songs, "textbox", "text", examples=[["I'm feeling lonely tonight"]])
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# Launch the interface
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iface.launch()
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