app.py

import gradio as gr
import json
from datetime import datetime
from theme import TufteInspired

from transformers import pipeline

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

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",
)



# Placeholder LLM function
def generate_blurb():
    # This is where you'd call your LLM model
    return pipeline("Write a blurb for a made-up book")[0]["generated_text"]

# 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()