import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

# Replace 'YOUR_ACCESS_TOKEN' with your actual Hugging Face access token
model_name = "sambanovasystems/SambaNova-Qwen2.5-Coder-Artifacts"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token='YOUR_ACCESS_TOKEN')
model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token='YOUR_ACCESS_TOKEN')

def generate_text(prompt):
    # Generate text using the model
    inputs = tokenizer.encode(prompt, return_tensors="pt")
    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return text

# Build the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("<h1 style='text-align: center;'>AI Text Generator</h1>")
    with gr.Row():
        with gr.Column():
            user_input = gr.Textbox(label="Input Prompt", placeholder="Enter your prompt here...")
            generate_btn = gr.Button("Generate Text")
        with gr.Column():
            output_text = gr.Textbox(label="Generated Text", readonly=True)
    generate_btn.click(generate_text, inputs=user_input, outputs=output_text)

# Launch the app
demo.launch(share=True, theme="macos", css=".gradio-container {background-color: #f0f0f0;}")