app.py

import gradio as gr
import json
from datetime import datetime
from theme import TufteInspired
import glob
import os
import uuid
from pathlib import Path
import spaces
import torch
import transformers
from huggingface_hub import CommitScheduler, hf_hub_download, login
from transformers import AutoTokenizer, AutoModelForCausalLM
from outlines import models, generate
from gradio import update

model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id, add_special_tokens=True)


@spaces.GPU(duration=120)
def generate_blurb(history):
    model = models.transformers(model_id)
    generator = generate.text(model)
    resp = generator("Write a blurb for a book")
    return resp

# Function to log blurb and vote
def log_blurb_and_vote(blurb, vote):
    log_entry = {"timestamp": datetime.now().isoformat(), "blurb": blurb, "vote": vote}
    with open("blurb_log.jsonl", "a") as f:
        f.write(json.dumps(log_entry) + "\n")
    return f"Logged: {vote}"


# 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 it?</h1>")
    gr.Markdown(
        "Click the button to generate a blurb for a made-up book, then vote on its quality."
    )
    with gr.Row():
        generate_btn = gr.Button("Write a Blurb", variant="primary")
    blurb_output = gr.Textbox(label="Generated Blurb", lines=5, interactive=False)
    with gr.Row():
        upvote_btn = gr.Button("👍 would read")
        downvote_btn = gr.Button("👎 wouldn't read")
    vote_output = gr.Textbox(label="Vote Status", interactive=False)

    generate_btn.click(generate_blurb, outputs=blurb_output)
    upvote_btn.click(
        lambda x: log_blurb_and_vote(x, "upvote"),
        inputs=blurb_output,
        outputs=vote_output,
    )
    downvote_btn.click(
        lambda x: log_blurb_and_vote(x, "downvote"),
        inputs=blurb_output,
        outputs=vote_output,
    )

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