import logging
from functools import partial
from typing import Callable, Optional

import pandas as pd
import streamlit as st
from bokeh.plotting import Figure
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 embed_text, load_model
from embedding_lenses.utils import encode_labels
from sentence_transformers import SentenceTransformer

from perplexity_lenses.data import documents_df_to_sentences_df, hub_dataset_to_dataframe
from perplexity_lenses.perplexity import KenlmModel
from perplexity_lenses.visualization import draw_interactive_scatter_plot

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
EMBEDDING_MODELS = ["distiluse-base-multilingual-cased-v1", "all-mpnet-base-v2", "flax-sentence-embeddings/all_datasets_v3_mpnet-base"]
DIMENSIONALITY_REDUCTION_ALGORITHMS = ["UMAP", "t-SNE"]
LANGUAGES = [
    "af",
    "ar",
    "az",
    "be",
    "bg",
    "bn",
    "ca",
    "cs",
    "da",
    "de",
    "el",
    "en",
    "es",
    "et",
    "fa",
    "fi",
    "fr",
    "gu",
    "he",
    "hi",
    "hr",
    "hu",
    "hy",
    "id",
    "is",
    "it",
    "ja",
    "ka",
    "kk",
    "km",
    "kn",
    "ko",
    "lt",
    "lv",
    "mk",
    "ml",
    "mn",
    "mr",
    "my",
    "ne",
    "nl",
    "no",
    "pl",
    "pt",
    "ro",
    "ru",
    "uk",
    "zh",
]
DOCUMENT_TYPES = ["Whole document", "Sentence"]
SEED = 0


def generate_plot(
    df: pd.DataFrame,
    text_column: str,
    label_column: str,
    sample: Optional[int],
    dimensionality_reduction_function: Callable,
    model: SentenceTransformer,
) -> Figure:
    if text_column not in df.columns:
        raise ValueError(f"The specified column name doesn't exist. Columns available: {df.columns.values}")
    if label_column not in df.columns:
        df[label_column] = 0
    df = df.dropna(subset=[text_column, label_column])
    if sample:
        df = df.sample(min(sample, df.shape[0]), random_state=SEED)
    with st.spinner(text="Embedding text..."):
        embeddings = embed_text(df[text_column].values.tolist(), model)
    logger.info("Encoding labels")
    encoded_labels = encode_labels(df[label_column])
    with st.spinner("Reducing dimensionality..."):
        embeddings_2d = dimensionality_reduction_function(embeddings)
    logger.info("Generating figure")
    plot = draw_interactive_scatter_plot(
        df[text_column].values, embeddings_2d[:, 0], embeddings_2d[:, 1], encoded_labels.values, df[label_column].values, text_column, label_column
    )
    return plot


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)
    logger.info("Displaying plot")
    st.bokeh_chart(plot)
    logger.info("Done")