import os
import csv
import streamlit as st
import polars as pl
from io import BytesIO, StringIO
from gliner import GLiNER
from gliner_file import run_ner
import time

st.set_page_config(
    page_title="GliNER", page_icon="🧊", layout="wide", initial_sidebar_state="expanded"
)


# Modified function to load data from either an Excel or CSV file
@st.cache_data
def load_data(file):
    _, file_ext = os.path.splitext(file.name)
    if file_ext.lower() in [".xls", ".xlsx"]:
        return pl.read_excel(file)
    elif file_ext.lower() == ".csv":
        file.seek(0)  # Go back to the beginning of the file
        try:
            sample = file.read(4096).decode(
                "utf-8"
            )  # Try to decode the sample in UTF-8
            encoding = "utf-8"
        except UnicodeDecodeError:
            encoding = "latin1"  # Switch to 'latin1' if UTF-8 fails
            file.seek(0)
            sample = file.read(4096).decode(encoding)

        file.seek(0)
        dialect = csv.Sniffer().sniff(sample)  # Detect the delimiter

        file.seek(0)
        if encoding != "utf-8":
            file_content = file.read().decode(encoding)
            file = StringIO(file_content)
        else:
            file_content = file.read().decode("utf-8")
            file = StringIO(file_content)

        return pl.read_csv(
            file,
            separator=dialect.delimiter,
            truncate_ragged_lines=True,
            ignore_errors=True,
        )
    else:
        raise ValueError("The uploaded file must be a CSV or Excel file.")


# Function to perform NER and update the UI
def perform_ner(filtered_df, selected_column, labels_list):
    ner_results_dict = {label: [] for label in labels_list}

    progress_bar = st.progress(0)
    progress_text = st.empty()

    start_time = time.time()  # Record start time for total runtime

    for index, row in enumerate(filtered_df.to_pandas().itertuples(), 1):
        iteration_start_time = time.time()  # Start time for this iteration

        if st.session_state.stop_processing:
            progress_text.text("Process stopped by the user.")
            break

        text_to_analyze = getattr(row, selected_column)
        ner_results = run_ner(
            st.session_state.gliner_model, text_to_analyze, labels_list
        )

        for label in labels_list:
            texts = ner_results.get(label, [])
            concatenated_texts = ", ".join(texts)
            ner_results_dict[label].append(concatenated_texts)

        progress = index / filtered_df.height
        progress_bar.progress(progress)

        iteration_time = (
            time.time() - iteration_start_time
        )  # Calculate runtime for this iteration
        total_time = time.time() - start_time  # Calculate total elapsed time so far

        progress_text.text(
            f"Progress: {index}/{filtered_df.height} - {progress * 100:.0f}% (Iteration: {iteration_time:.2f}s, Total: {total_time:.2f}s)"
        )

    end_time = time.time()  # Record end time
    total_execution_time = end_time - start_time  # Calculate total runtime

    progress_text.text(
        f"Processing complete! Total execution time: {total_execution_time:.2f}s"
    )

    for label, texts in ner_results_dict.items():
        filtered_df = filtered_df.with_columns(pl.Series(name=label, values=texts))

    return filtered_df


def main():
    st.title("Online NER with GliNER")
    st.markdown("Prototype v0.1")

    # Ensure the stop_processing flag is initialized
    if "stop_processing" not in st.session_state:
        st.session_state.stop_processing = False

    uploaded_file = st.sidebar.file_uploader("Choose a file")
    if uploaded_file is None:
        st.warning("Please upload a file.")
        return

    try:
        df = load_data(uploaded_file)
    except ValueError as e:
        st.error(str(e))
        return

    selected_column = st.selectbox("Select the column for NER:", df.columns, index=0)
    filter_text = st.text_input("Filter column by input text", "")
    ner_labels = st.text_input(
        "Enter all your different labels, separated by a comma", ""
    )

    filtered_df = (
        df.filter(pl.col(selected_column).str.contains(f"(?i).*{filter_text}.*"))
        if filter_text
        else df
    )
    st.dataframe(filtered_df)

    if st.button("Start NER"):
        if not ner_labels:
            st.warning("Please enter some labels for NER.")
        else:
            # Load GLiNER model if not already loaded
            if "gliner_model" not in st.session_state:
                with st.spinner("Loading GLiNER model... Please wait."):
                    st.session_state.gliner_model = GLiNER.from_pretrained(
                        "urchade/gliner_largev2"
                    )
                    st.session_state.gliner_model.eval()

            labels_list = ner_labels.split(",")
            updated_df = perform_ner(filtered_df, selected_column, labels_list)
            st.dataframe(updated_df)

            def to_excel(df):
                output = BytesIO()
                df.to_pandas().to_excel(output, index=False, engine="openpyxl")
                return output.getvalue()

            df_excel = to_excel(updated_df)
            st.download_button(
                label="📥 Download Excel",
                data=df_excel,
                file_name="ner_results.xlsx",
                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
            )

    st.button(
        "Stop Processing",
        on_click=lambda: setattr(st.session_state, "stop_processing", True),
    )


if __name__ == "__main__":
    main()