app.py

from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr

tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-Instruct-124M',
    use_auth_token="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")
model = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-Instruct-124M',
    use_auth_token="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") 

disclaimer = """**`Disclaimer`:** This demo should be used for research purposes only. Commercial use is strictly **prohibited**. The model output is not censored and the authors do not endorse the opinions in the generated content. **Use at your own risk.**"""

with gr.Blocks(theme='freddyaboulton/dracula_revamped') as demo:
    
    gr.Markdown("""<h1><center>🔥Aira Demo 🤓🚀</h1></center>""")
    
    with gr.Row(scale=1, equal_height=True):
        
        with gr.Column(scale=5):
            chatbot = gr.Chatbot(label="Aira").style(height=300)
        
        with gr.Column(scale=2):
           
            with gr.Tab(label="Parameters ⚙️"):
                top_k = gr.Slider( minimum=10, maximum=100, value=50, step=5, interactive=True, label="Top-k",)
                top_p = gr.Slider( minimum=0.1, maximum=1.0, value=0.70, step=0.05, interactive=True, label="Top-p",)
                temperature = gr.Slider( minimum=0.001, maximum=2.0, value=0.1, step=0.1, interactive=True, label="Temperature",)
                max_length = gr.Slider( minimum=10, maximum=500, value=100, step=10, interactive=True, label="Max Length",)

    msg = gr.Textbox(label="Write a question or comment to Aira", placeholder="Hi Aira, how are you?")
    clear = gr.Button("Clear Conversation 🧹")
    gr.Markdown(disclaimer)

    def generate_response(message, chat_history, top_k, top_p, temperature, max_length):
        inputs = tokenizer(tokenizer.bos_token + message + tokenizer.eos_token, return_tensors="pt")

        response = model.generate(**inputs,
            bos_token_id=tokenizer.bos_token_id,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
            do_sample=True,
            early_stopping=True,   
            top_k=top_k, 
            max_length=max_length,
            top_p=top_p,
            temperature=temperature, 
            num_return_sequences=1)
        
        chat_history.append((f"👤 {message}", f"""🤖 {tokenizer.decode(response[0], skip_special_tokens=True).replace(message, "")}"""))

        return "", chat_history
    
    msg.submit(generate_response, [msg, chatbot, top_k, top_p, temperature, max_length], [msg, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

demo.launch()