app.py

import gradio as gr
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import spaces
from huggingface_hub import login
import os
login(os.environ['HF_KEY'])

# Define a function that takes user input and returns the model's output
@spaces.GPU(duration=120)
def generate_text(prompt):
    config = PeftConfig.from_pretrained("diabolic6045/gemma-2-2b-chess-adapter")
    base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b")
    model = PeftModel.from_pretrained(base_model, "diabolic6045/gemma-2-2b-chess-adapter")
    model.tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b")
    input_ids = model.tokenizer.encode(prompt, return_tensors="pt")
    print("generation")
    output = model.generate(input_ids, max_length=20)
    return model.tokenizer.decode(output[0], skip_special_tokens=True)

# Create a Gradio interface
demo = gr.Interface(
    fn=generate_text,
    inputs=gr.Textbox(label="Input prompt"),
    outputs=gr.Textbox(label="Generated text"),
    title="Chess Text Generation with Gemma-2-2B",
    description="Enter a prompt and the model will generate a response.",
    examples=[
        ["What is the best opening move in chess?"],
        ["What is the Ruy Lopez opening?"],
        ["What is the Sicilian Defense?"],
    ],
)

# Launch the Gradio app
demo.launch()