app.py

# %%
import io
import json
import logging
from uuid import uuid4

import datasets
import gradio as gr
import matplotlib.pyplot as plt

from utils import (
    process_chat_file,
    transform_conversations_dataset_into_training_examples,
)
from validation import check_format_errors, estimate_cost, get_distributions

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)


def convert_to_dataset(
    files,
    do_spelling_correction,
    progress,
    whatsapp_name,
    datetime_dayfirst,
    message_line_format,
    minutes_threshold,
    min_messages_per_conversation,
):
    modified_dataset = None
    for file in progress.tqdm(files, desc="Processing files"):
        try:
            if modified_dataset is None:
                # First file
                modified_dataset = process_chat_file(
                    file,
                    do_spelling_correction=do_spelling_correction,
                    whatsapp_name=whatsapp_name,
                    datetime_dayfirst=datetime_dayfirst,
                    message_line_format=message_line_format,
                    minutes_threshold=minutes_threshold,
                    min_messages_per_conversation=min_messages_per_conversation,
                )
            else:
                # Concatenate the datasets
                this_file_dataset = process_chat_file(
                    file,
                    do_spelling_correction=do_spelling_correction,
                    whatsapp_name=whatsapp_name,
                    datetime_dayfirst=datetime_dayfirst,
                    message_line_format=message_line_format,
                    minutes_threshold=minutes_threshold,
                    min_messages_per_conversation=min_messages_per_conversation,
                )
                modified_dataset = datasets.concatenate_datasets(
                    [modified_dataset, this_file_dataset]
                )
        except Exception as e:
            logger.error(f"Error processing file {file}: {e}")
            raise gr.Error(f"Error processing file {file}: {e}")
    return modified_dataset


def file_upload_callback(
    files,
    system_prompt,
    do_spelling_correction,
    validation_split,
    user_role,
    model_role,
    whatsapp_name,
    datetime_dayfirst,
    message_line_format,
    minutes_threshold,
    min_messages_per_conversation,
    max_characters_per_message,
    split_conversation_threshold,
    progress=gr.Progress(),
):
    logger.info(f"Processing {files}")
    full_system_prompt = f"""# Task
You are a chatbot. Your goal is to simulate realistic, natural chat conversations as if you were me.
The {model_role} and the {user_role} can send multiple messages in a row, as a JSON list of strings. Your answer always needs to be JSON compliant. The strings are delimited by double quotes ("). The strings are separated by a comma (,). The list is delimited by square brackets ([, ]). Always start your answer with [", and close it with "]. Do not write anything else in your answer after "].
# Information about me
{system_prompt}"""
    # Example
    # [{{\"role\":\"user\",\"content\":\"[\"Hello!\",\"How are you?\"]\"}},{{\"role\":\"assistant\",\"content\":\"[\"Hi!\",\"I'm doing great.\",\"What about you?\"]\"}},{{\"role\":\"user\",\"content\":\"[\"I'm doing well.\",\"Have you been travelling?\"]\"}}]
    # Response:
    # [{{\"role\":\"assistant\",\"content\":\"[\"Yes, I've been to many places.\",\"I love travelling.\"]\"}}]"""

    # Check if the user has not chosen any files
    if not files or len(files) == 0:
        raise gr.Error("Please upload at least one file.")

    # Check if the user has not entered their whatsapp name
    if not whatsapp_name or len(whatsapp_name) == 0:
        raise gr.Error("Please enter your WhatsApp name.")

    # # Avoid using the full system prompt for now, as it is too long and increases the cost of the training
    # full_system_prompt = system_prompt
    dataset = convert_to_dataset(
        files=files,
        progress=progress,
        do_spelling_correction=do_spelling_correction,
        whatsapp_name=whatsapp_name,
        datetime_dayfirst=datetime_dayfirst,
        message_line_format=message_line_format,
        minutes_threshold=minutes_threshold,
        min_messages_per_conversation=min_messages_per_conversation,
    )
    logger.info(
        f"Number of conversations of dataset before being transformed: {len(dataset)}"
    )

    full_examples_ds = transform_conversations_dataset_into_training_examples(
        conversations_ds=dataset,
        system_prompt=full_system_prompt,
        user_role=user_role,
        model_role=model_role,
        whatsapp_name=whatsapp_name,
        minutes_threshold=minutes_threshold,
        min_messages_per_conversation=min_messages_per_conversation,
        split_conversation_threshold=split_conversation_threshold,
        max_characters_per_message=max_characters_per_message,
    )
    total_number_of_generated_examples = len(full_examples_ds)
    logger.info(
        f"Total number of generated examples: {total_number_of_generated_examples}"
    )

    # Split into training and validation datasets (80% and 20%)
    try:
        split_examples_ds = full_examples_ds.train_test_split(
            test_size=validation_split, seed=42
        )
        training_examples_ds, validation_examples_ds = (
            split_examples_ds["train"],
            split_examples_ds["test"],
        )
    except ValueError as e:
        # This happens when there's not enough data to split into training and validation datasets
        # In this case, we'll just use the whole dataset for training, nothing for validation
        training_examples_ds = full_examples_ds
        validation_examples_ds = datasets.Dataset.from_dict({})
    training_examples_ds = training_examples_ds  # .select(
    #    range(min(250, len(training_examples_ds)))
    # )
    validation_examples_ds = validation_examples_ds.select(
        range(min(200, len(validation_examples_ds)))
    )

    format_errors = check_format_errors(
        training_examples_ds, user_role=user_role, model_role=model_role
    )
    distributions = get_distributions(
        training_examples_ds, user_role=user_role, model_role=model_role
    )
    cost_stats = estimate_cost(
        training_examples_ds, user_role=user_role, model_role=model_role
    )

    stats = {
        "Total number of training examples": total_number_of_generated_examples,
        "Number of training examples": len(training_examples_ds),
        "Number of validation examples": len(validation_examples_ds),
        "Number of examples missing system message": distributions["n_missing_system"],
        "Number of examples missing user message": distributions["n_missing_user"],
        "Format Errors": format_errors,
        "Cost Statistics": cost_stats,
    }

    fig_num_messages_distribution_plot = plt.figure()
    num_messages_distribution_plot = plt.hist(distributions["n_messages"], bins=20)

    fig_num_total_tokens_per_example_plot = plt.figure()
    num_total_tokens_per_example_plot = plt.hist(distributions["convo_lens"], bins=20)

    fig_num_assistant_tokens_per_example_plot = plt.figure()
    num_assistant_tokens_per_example_plot = plt.hist(
        distributions["assistant_message_lens"], bins=20
    )

    # The DownloadFile component requires a path to the file, it can't accept a buffer to keep the file in memory.
    # Therefore, we need to save the buffer to a file and then pass the path to the DownloadFile component.
    # However, if different users are using the app at the same time, we need to make sure that the file is unique AND that no user can access the file of another user.
    # We can use a UUID generator to create a unique file name.
    uuid = str(uuid4())
    file_path = f"training_examples_{uuid}.jsonl"
    training_examples_ds.to_json(path_or_buf=file_path, force_ascii=False)

    file_path_validation = f"validation_examples_{uuid}.jsonl"
    validation_examples_ds.to_json(path_or_buf=file_path_validation, force_ascii=False)

    # If there's less than 50 training examples, show a warning message
    if len(training_examples_ds) < 50:
        gr.Warning(
            "There are less than 50 training examples. The model may not perform well with such a small dataset. Consider adding more chat files to increase the number of training examples."
        )

    system_prompt_to_use = full_system_prompt

    return (
        file_path,
        gr.update(visible=True),
        file_path_validation,
        gr.update(visible=True),
        stats,
        fig_num_messages_distribution_plot,
        fig_num_total_tokens_per_example_plot,
        fig_num_assistant_tokens_per_example_plot,
        system_prompt_to_use,
        gr.update(visible=True),
    )


def remove_file_and_hide_button(file_path):
    import os

    # try:
    #     os.remove(file_path)
    # except Exception as e:
    #     logger.info(f"Error removing file {file_path}: {e}")

    return gr.update(visible=False)


theme = gr.themes.Default(primary_hue="cyan", secondary_hue="fuchsia")

with gr.Blocks(theme=theme) as demo:
    gr.Markdown(
        """
        # WhatsApp Chat to Dataset Converter
        Upload your WhatsApp chat files and convert them into a Dataset.
        """
    )
    gr.Markdown(
        """
        ## Instructions
        1. Click on the "Upload WhatsApp Chat Files" button.
        2. Select the WhatsApp chat files you want to convert.
        3. Write a prompt about you to give context to the training examples.
        4. Click on the "Submit" button.
        5. Wait for the process to finish.
        6. Download the generated training examples as a JSONL file.
        7. Use the training examples to train your own model.
        """
    )

    input_files = gr.File(
        label="Upload WhatsApp Chat Files",
        type="filepath",
        file_count="multiple",
        file_types=["txt"],
    )

    system_prompt = gr.Textbox(
        label="System Prompt",
        placeholder="Background information about you.",
        lines=5,
        info="Enter the system prompt to be used for the training examples generation. This is the background information about you that will be used to generate the training examples.",
        value="""Aldan is an AI researcher who loves to play around with AI systems, travelling and learning new things.""",
    )

    whatsapp_name = gr.Textbox(
        label="Your WhatsApp Name",
        placeholder="Your WhatsApp Name",
        info="Enter your WhatsApp name as it appears in your profile. It needs to match exactly your name. If you're unsure, you can check the chat messages to see it.",
    )

    # Advanced parameters section, collapsed by default
    with gr.Accordion(label="Advanced Parameters", open=False):
        gr.Markdown(
            """
            These are advanced parameters that you can change if you know what you're doing. If you're unsure, you can leave them as they are.
            """
        )

        user_role = gr.Textbox(
            label="Role for User",
            info="This is a technical parameter. If you don't know what to write, just type 'user'.",
            value="user",
        )

        model_role = gr.Textbox(
            label="Role for Model",
            info="This is a technical parameter. Usual values are 'model' (e.g. Vertex AI) or 'assistant' (e.g. OpenAI).",
            value="model",
        )

        minutes_threshold = gr.Number(
            label="Minutes Threshold",
            info="Threshold in minutes to consider that a new message is a new conversation. The default value should work for most cases.",
            value=180,
        )

        min_messages_per_conversation = gr.Number(
            label="Minimum Messages per Conversation",
            info="Minimum number of messages per conversation to consider it as a valid conversation. The default value should work for most cases.",
            value=5,
        )

        max_characters_per_message = gr.Number(
            label="Max Characters per Message",
            info="One token is around 3 characters. The default value should work for most cases. For example, on Vertex AI, the maximum number of tokens per example is [32,000](https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini-supervised-tuning-prepare#sample-datasets), so keeping the default value will ensure that the examples are well within the limit.",
            value=10000,
        )

        split_conversation_threshold = gr.Number(
            label="Split Conversation Threshold",
            info="Number of messages in a conversation to split it into multiple ones. The default value should work for most cases.",
            value=40,
        )

        message_line_format = gr.Textbox(
            label="Message Line Format",
            info="Format of each message line in the chat file, as a regular expression. The default value should work for most cases.",
            value=r"\[?(?P<msg_datetime>\S+,\s\S+?(?:\s[APap][Mm])?)\]? (?:- )?(?P<contact_name>.+?): (?P<message>.+)",
        )

        datetime_dayfirst = gr.Checkbox(
            label="Date format: Day first",
            info="Check this box if the date time format in the chat messages is in the format 'DD/MM/YYYY'. You can check your phone settings to see the date format. Otherwise, it will be assumed that the date time format is 'MM/DD/YYYY'.",
            value=True,
        )

        do_spelling_correction = gr.Checkbox(
            label="Do Spelling Correction (English)",
            info="Check this box if you want to perform spelling correction on the chat messages before generating the training examples.",
        )

        # Allow the user to choose the validation split size
        validation_split = gr.Slider(
            minimum=0.0,
            maximum=0.5,
            value=0.2,
            interactive=True,
            label="Validation Split",
            info="Choose the percentage of the dataset to be used for validation. For example, if you choose 0.2, 20% of the dataset will be used for validation and 80% for training.",
        )

    submit = gr.Button(value="Submit", variant="primary")

    output_file = gr.DownloadButton(
        label="Download Generated Training Examples", visible=False, variant="primary"
    )
    output_file_validation = gr.DownloadButton(
        label="Download Generated Validation Examples",
        visible=False,
        variant="secondary",
    )

    system_prompt_to_use = gr.Textbox(
        label="System Prompt that you can use",
        visible=False,
        interactive=False,
        show_copy_button=True,
        info="When using the model, if you're asked for a system prompt, you can use this text.",
    )
    # output_example = gr.JSON(label="Example Training Example")

    with gr.Group():
        # Statistics about the dataset
        gr.Markdown("## Statistics")
        written_stats = gr.JSON()
        num_messages_distribution_plot = gr.Plot(
            label="Number of Messages Distribution"
        )
        num_total_tokens_per_example_plot = gr.Plot(
            label="Total Number of Tokens per Example"
        )
        num_assistant_tokens_per_example_plot = gr.Plot(
            label="Number of Assistant Tokens per Example"
        )

    submit.click(
        file_upload_callback,
        inputs=[
            input_files,
            system_prompt,
            do_spelling_correction,
            validation_split,
            user_role,
            model_role,
            whatsapp_name,
            datetime_dayfirst,
            message_line_format,
            minutes_threshold,
            min_messages_per_conversation,
            max_characters_per_message,
            split_conversation_threshold,
        ],
        outputs=[
            output_file,
            output_file,
            output_file_validation,
            output_file_validation,
            written_stats,
            num_messages_distribution_plot,
            num_total_tokens_per_example_plot,
            num_assistant_tokens_per_example_plot,
            system_prompt_to_use,
            system_prompt_to_use,
        ],
    )

    output_file.click(
        remove_file_and_hide_button, inputs=[output_file], outputs=[output_file]
    )
    output_file_validation.click(
        remove_file_and_hide_button,
        inputs=[output_file_validation],
        outputs=[output_file_validation],
    )

if __name__ == "__main__":
    demo.launch()