app.py

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

# Define the base and LoRA model IDs
base_model_id = "unsloth/gemma-2-9b-bnb-4bit"
lora_model_id = "Futuresony/future_12_10_2024"

# Load the base model on CPU with float16
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float16,
    device_map="cpu",  # Load the model on CPU, no GPU
)

# Load the PEFT LoRA model
model = PeftModel.from_pretrained(base_model, lora_model_id)

# Tokenizer for the model
tokenizer = AutoTokenizer.from_pretrained(base_model_id)

# Function to respond to the user's input
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
    # Prepare the message history for chat completion
    messages = [{"role": "system", "content": system_message}]
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    # Generate a response
    response = ""
    for message in model.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content
        response += token
        yield response

# Gradio interface setup
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)

if __name__ == "__main__":
    demo.launch()