app.py

import gradio as gr
import json
from datetime import datetime
from theme import TufteInspired
import uuid
from huggingface_hub import InferenceClient, CommitScheduler, hf_hub_download
from openai import OpenAI
from huggingface_hub import get_token, login
from prompts import detailed_genre_description_prompt, basic_prompt
import random
import os
from pathlib import Path

# Ensure you're logged in to Hugging Face
login(get_token())
# Define available models
MODELS = [
    "meta-llama/Meta-Llama-3-70B-Instruct",
    "mistralai/Mixtral-8x7B-Instruct-v0.1",
    "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
]

CHOSEN_MODEL = None

# Set up dataset storage
dataset_folder = Path("dataset")
dataset_folder.mkdir(exist_ok=True)


# Function to get the latest dataset file
def get_latest_dataset_file():
    files = list(dataset_folder.glob("data_*.jsonl"))
    return max(files, key=os.path.getctime) if files else None


# Check for existing dataset and create or append to it
latest_file = get_latest_dataset_file()
if latest_file:
    dataset_file = latest_file
    print(f"Appending to existing dataset file: {dataset_file}")
else:
    dataset_file = dataset_folder / f"data_{uuid.uuid4()}.jsonl"
    print(f"Creating new dataset file: {dataset_file}")

# Set up CommitScheduler for dataset uploads
repo_id = (
    "davanstrien/summer-reading-preference"  # Replace with your desired dataset repo
)
scheduler = CommitScheduler(
    repo_id=repo_id,
    repo_type="dataset",
    folder_path=dataset_folder,
    path_in_repo="data",
    every=5,  # Upload every 5 minutes
)


# Function to download existing dataset files
def download_existing_dataset():
    try:
        files = hf_hub_download(
            repo_id=repo_id, filename="data", repo_type="dataset", recursive=True
        )
        for file in Path(files).glob("*.jsonl"):
            dest_file = dataset_folder / file.name
            if not dest_file.exists():
                dest_file.write_bytes(file.read_bytes())
                print(f"Downloaded existing dataset file: {dest_file}")
    except Exception as e:
        print(f"Error downloading existing dataset: {e}")


# Download existing dataset files at startup
download_existing_dataset()


def get_random_model():
    global CHOSEN_MODEL
    model = random.choice(MODELS)
    CHOSEN_MODEL = model
    return model


def create_client(model_id):
    return OpenAI(
        base_url=f"https://api-inference.huggingface.co/models/{model_id}/v1",
        api_key=get_token(),
    )


client = OpenAI(
    base_url="https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct/v1",
    api_key=get_token(),
)


def generate_prompt():
    if random.choice([True, False]):
        return detailed_genre_description_prompt()
    else:
        return basic_prompt()


def get_and_store_prompt():
    prompt = generate_prompt()
    print(prompt)  # Keep this for debugging
    return prompt


def generate_blurb(prompt):
    model_id = get_random_model()
    client = create_client(model_id)
    max_tokens = random.randint(100, 1000)
    chat_completion = client.chat.completions.create(
        model="tgi",
        messages=[
            {"role": "user", "content": prompt},
        ],
        stream=True,
        max_tokens=max_tokens,
    )
    full_text = ""
    for message in chat_completion:
        full_text += message.choices[0].delta.content
        yield full_text


# Function to log blurb and vote
def log_blurb_and_vote(prompt, blurb, vote, user_info: gr.OAuthProfile | None, *args):
    user_id = user_info.username if user_info is not None else str(uuid.uuid4())
    log_entry = {
        "timestamp": datetime.now().isoformat(),
        "prompt": prompt,
        "blurb": blurb,
        "vote": vote,
        "user_id": user_id,
        "model": CHOSEN_MODEL,
    }
    with scheduler.lock:
        with dataset_file.open("a") as f:
            f.write(json.dumps(log_entry) + "\n")
    gr.Info("Thank you for voting!")
    return f"Logged: {vote} by user {user_id}"


# Create custom theme
tufte_theme = TufteInspired()

# Create Gradio interface
with gr.Blocks(theme=tufte_theme) as demo:
    gr.Markdown("<h1 style='text-align: center;'>Would you read this book?</h1>")
    gr.Markdown(
        """<p style='text-align: center;'>Looking for your next summer read? 
    Would you read a book based on this LLM generated blurb? <br> Your vote will be added to <a href="https://huggingface.co/datasets/your-username/your-dataset-repo">this</a> Hugging Face dataset</p>"""
    )

    # Add the login button
    login_btn = gr.LoginButton()

    with gr.Row():
        generate_btn = gr.Button("Create a book", variant="primary")

    prompt_state = gr.State()
    blurb_output = gr.Markdown(label="Book blurb")

    with gr.Row(visible=False) as voting_row:
        upvote_btn = gr.Button("👍 would read")
        downvote_btn = gr.Button("👎 wouldn't read")

    vote_output = gr.Textbox(label="Vote Status", interactive=False, visible=False)

    def generate_and_show(prompt):
        return "Generating...", gr.Row.update(visible=False)

    def show_voting_buttons(blurb):
        return blurb, gr.Row.update(visible=True)

    generate_btn.click(get_and_store_prompt, outputs=prompt_state).then(
        generate_and_show, inputs=prompt_state, outputs=[blurb_output, voting_row]
    ).then(generate_blurb, inputs=prompt_state, outputs=blurb_output).then(
        show_voting_buttons, inputs=blurb_output, outputs=[blurb_output, voting_row]
    )

    upvote_btn.click(
        log_blurb_and_vote,
        inputs=[
            prompt_state,
            blurb_output,
            gr.Textbox(value="upvote", visible=False),
            login_btn,
        ],
        outputs=vote_output,
    )
    downvote_btn.click(
        log_blurb_and_vote,
        inputs=[
            prompt_state,
            blurb_output,
            gr.Textbox(value="downvote", visible=False),
            login_btn,
        ],
        outputs=vote_output,
    )

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