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")