app.py

import os
import gradio as gr
from unsloth import FastLanguageModel
import torch

# Disable CUDA explicitly by setting the environment variable
os.environ["CUDA_VISIBLE_DEVICES"] = ""  # Disabling CUDA

# Set device to CPU
device = torch.device("cpu")

model_name_or_path = "michailroussos/model_llama_8d"
max_seq_length = 2048
dtype = None

# Load the model on CPU
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_name_or_path,  # Your model path
    max_seq_length=max_seq_length,
    dtype=dtype,
    load_in_4bit=True,
).to(device)  # Ensure the model is on CPU

# Enable native faster inference if possible
FastLanguageModel.for_inference(model)

# Define the inference function
def respond(message, history, system_message, max_tokens, temperature, top_p):
    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})

    response = ""

    # Perform inference on CPU
    inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(device)
    for message in model.generate(input_ids=inputs['input_ids'], streamer=None, max_new_tokens=max_tokens, temperature=temperature, top_p=top_p):
        token = message.choices[0].delta.content
        response += token
        yield response

# Create Gradio interface
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)"),
    ],
)

# Launch Gradio app
if __name__ == "__main__":
    demo.launch()