app.py

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

# --- Load your HASTE model ---
model_name = "theguywhosucks/haste"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",  # automatically uses GPU if available
    torch_dtype=torch.float16
)

# --- Chat function ---
def chat_with_haste(user_input, max_tokens, temperature, chat_history=[]):
    chat_history.append(f"User: {user_input}")
    
    # Prepare prompt
    prompt = "\n".join(chat_history) + "\nAI:"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    # Generate
    output = model.generate(
        **inputs,
        max_new_tokens=max_tokens,
        temperature=temperature,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    
    # Decode response
    response = tokenizer.decode(output[0][inputs['input_ids'].shape[-1]:], skip_special_tokens=True)
    chat_history.append(f"AI: {response}")
    
    return chat_history, chat_history

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown("## HASTE Chatbot with Adjustable Tokens & Temperature")
    
    chatbox = gr.Chatbot()
    with gr.Row():
        user_input = gr.Textbox(placeholder="Type your message...", label="Your Message")
        submit_btn = gr.Button("Send")
    with gr.Row():
        max_tokens_slider = gr.Slider(1, 500, value=100, step=1, label="Max Tokens")
        temp_slider = gr.Slider(0.1, 2.0, value=0.7, step=0.05, label="Temperature")
    
    state = gr.State([])  # chat history
    
    submit_btn.click(
        chat_with_haste,
        inputs=[user_input, max_tokens_slider, temp_slider, state],
        outputs=[chatbox, state]
    )

demo.launch()