app.py

import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr

checkpoint = "WillHeld/soft-raccoon"
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

@spaces.GPU(duration=120)
def predict(message, history, temperature, top_p):
    history.append({"role": "user", "content": message})
    input_text = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)  
    outputs = model.generate(
        inputs, 
        max_new_tokens=1024, 
        temperature=float(temperature), 
        top_p=float(top_p), 
        do_sample=True
    )
    decoded = tokenizer.decode(outputs[0])
    response = decoded.split("<|start_header_id|>assistant<|end_header_id|>\n\n")[-1]
    return response

with gr.Blocks() as demo:
    chatbot = gr.ChatInterface(
        predict,
        additional_inputs=[
            gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature"),
            gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-P")
        ],
        type="messages"
    )

demo.launch()