import gradio as gr
import pandas as pd
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Constants for default values
DEFAULT_CHUNK_SIZE = 100
DEFAULT_CHUNK_OVERLAP = 0
DEFAULT_NUM_CHUNKS = 10

def tokenize_text(method, text, chunk_size, chunk_overlap, num_chunks):
    """
    Tokenizes the input text based on the selected method and provided parameters.
    """
    num_chunks = int(num_chunks)
    output = []

    # Ensure text is provided
    if not text.strip():
        return pd.DataFrame(columns=['Chunk #', 'Text Chunk', 'Character Count', 'Token Count'])

    if method == "RecursiveCharacterTextSplitter":
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap, length_function=len, is_separator_regex=False)
        tokenized_texts = text_splitter.split_text(text)[:num_chunks]
        for i, chunk in enumerate(tokenized_texts):
            output.append({
                'Chunk #': i,
                'Text Chunk': chunk,
                'Character Count': len(chunk),
                'Token Count': len(chunk.split())
            })

    df = pd.DataFrame(output)
    return df

iface = gr.Interface(
    fn=tokenize_text,
    inputs=[
        gr.Dropdown(label="Select Tokenization Method", choices=["RecursiveCharacterTextSplitter"]),
        gr.Textbox(label="Enter Text", lines=10, placeholder="Type or paste text here."),
        gr.Number(label="Chunk Size", value=DEFAULT_CHUNK_SIZE),
        gr.Number(label="Chunk Overlap", value=DEFAULT_CHUNK_OVERLAP),
        gr.Number(label="Number of Chunks to Display", value=DEFAULT_NUM_CHUNKS)
    ],
    outputs=gr.Dataframe(headers=["Chunk #", "Text Chunk", "Character Count", "Token Count"], height=900,),
    title="Text Tokenization Tool",
    description="A tool for tokenizing text using different methods."
)

iface.launch()