app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "khaled123/chess"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define the function to generate text
def generate_text(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(inputs["input_ids"], max_length=50)
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_text

# Create the Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs="text",
    outputs="text",
    title="Chess Model based on LLaMA 2",
    description="Type a prompt and the model will generate text based on it."
)

# Launch the interface
iface.launch()