app.py

# %%
from uuid import uuid4
import gradio as gr
import datasets
import json
import io
from utils import (
    process_chat_file,
    transform_conversations_dataset_into_training_examples,
)
from validation import (
    check_format_errors,
    check_token_counts,
    estimate_cost,
    get_distributions,
)
import matplotlib.pyplot as plt


def convert_to_dataset(files, do_spelling_correction, progress):
    modified_dataset = None
    for file in progress.tqdm(files, desc="Processing files"):
        if modified_dataset is None:
            # First file
            modified_dataset = process_chat_file(file, do_spelling_correction=do_spelling_correction)
        else:
            # Concatenate the datasets
            this_file_dataset = process_chat_file(file, do_spelling_correction=do_spelling_correction)
            modified_dataset = datasets.concatenate_datasets(
                [modified_dataset, this_file_dataset]
            )
    return modified_dataset


def file_upload_callback(files, system_prompt, do_spelling_correction, validation_split, progress=gr.Progress()):
    print(f"Processing {files}")
    full_system_prompt = f"""You are a chatbot. Your goal is to simulate realistic, natural chat conversations as if you were me.
# Task
A participant can send multiple messages in a row, delimited by '\"', in the following schema:
{{string}}[]. Your answer always needs to be JSON compliant. Always start your answer with [\"
# Information about me
You should use the following information about me to answer:
{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.\"]\"}}]"""

    # 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)
    training_examples_ds = transform_conversations_dataset_into_training_examples(
        conversations_ds=dataset, system_prompt=full_system_prompt
    )

    # Split into training and validation datasets (80% and 20%)
    training_examples_ds = training_examples_ds.train_test_split(test_size=validation_split, seed=42)
    training_examples_ds, validation_examples_ds = training_examples_ds["train"], training_examples_ds["test"]

    format_errors = check_format_errors(training_examples_ds)
    distributions = get_distributions(training_examples_ds)
    cost_stats = estimate_cost(training_examples_ds)

    stats = {
        "Format Errors": format_errors,
        "Number of examples missing system message": distributions["n_missing_system"],
        "Number of examples missing user message": distributions["n_missing_user"],
        "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)

    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
    )


def remove_file_and_hide_button(file_path):
    import os

    try:
        os.remove(file_path)
    except Exception as e:
        print(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.""",
    )

    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")
    # 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],
        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,
        ]
    )

    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()