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| import logging | |
| from functools import partial | |
| import streamlit as st | |
| from embedding_lenses.data import uploaded_file_to_dataframe | |
| from embedding_lenses.dimensionality_reduction import get_tsne_embeddings, get_umap_embeddings | |
| from embedding_lenses.embedding import load_model | |
| from perplexity_lenses.data import documents_df_to_sentences_df, hub_dataset_to_dataframe | |
| from perplexity_lenses.engine import DIMENSIONALITY_REDUCTION_ALGORITHMS, DOCUMENT_TYPES, EMBEDDING_MODELS, LANGUAGES, SEED, generate_plot | |
| from perplexity_lenses.perplexity import KenlmModel | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| st.title("Perplexity Lenses") | |
| st.write("Visualize text embeddings in 2D using colors to represent perplexity values.") | |
| uploaded_file = st.file_uploader("Choose an csv/tsv file...", type=["csv", "tsv"]) | |
| st.write("Alternatively, select a dataset from the [hub](https://huggingface.co/datasets)") | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| hub_dataset = st.text_input("Dataset name", "mc4") | |
| with col2: | |
| hub_dataset_config = st.text_input("Dataset configuration", "es") | |
| with col3: | |
| hub_dataset_split = st.text_input("Dataset split", "train") | |
| col4, col5 = st.columns(2) | |
| with col4: | |
| text_column = st.text_input("Text field name", "text") | |
| with col5: | |
| language = st.selectbox("Language", LANGUAGES, 12) | |
| col6, col7 = st.columns(2) | |
| with col6: | |
| doc_type = st.selectbox("Document type", DOCUMENT_TYPES, 1) | |
| with col7: | |
| sample = st.number_input("Maximum number of documents to use", 1, 100000, 1000) | |
| dimensionality_reduction = st.selectbox("Dimensionality Reduction algorithm", DIMENSIONALITY_REDUCTION_ALGORITHMS, 0) | |
| model_name = st.selectbox("Sentence embedding model", EMBEDDING_MODELS, 0) | |
| with st.spinner(text="Loading embedding model..."): | |
| model = load_model(model_name) | |
| dimensionality_reduction_function = ( | |
| partial(get_umap_embeddings, random_state=SEED) if dimensionality_reduction == "UMAP" else partial(get_tsne_embeddings, random_state=SEED) | |
| ) | |
| with st.spinner(text="Loading KenLM model..."): | |
| kenlm_model = KenlmModel.from_pretrained(language) | |
| if uploaded_file or hub_dataset: | |
| with st.spinner("Loading dataset..."): | |
| if uploaded_file: | |
| df = uploaded_file_to_dataframe(uploaded_file) | |
| if doc_type == "Sentence": | |
| df = documents_df_to_sentences_df(df, text_column, sample, seed=SEED) | |
| df["perplexity"] = df[text_column].map(kenlm_model.get_perplexity) | |
| else: | |
| df = hub_dataset_to_dataframe(hub_dataset, hub_dataset_config, hub_dataset_split, sample, text_column, kenlm_model, seed=SEED, doc_type=doc_type) | |
| # Round perplexity | |
| df["perplexity"] = df["perplexity"].round().astype(int) | |
| logger.info(f"Perplexity range: {df['perplexity'].min()} - {df['perplexity'].max()}") | |
| plot = generate_plot(df, text_column, "perplexity", None, dimensionality_reduction_function, model, seed=SEED, context_logger=st.spinner) | |
| logger.info("Displaying plot") | |
| st.bokeh_chart(plot) | |
| logger.info("Done") | |