app.py

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

# Load Hugging Face API token securely
api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")

if not api_token:
    raise ValueError("❌ ERROR: Hugging Face API token is not set. Please set it as an environment variable.")

# Define model names
base_model_name = "unsloth/qwen2.5-math-7b-bnb-4bit"
peft_model_name = "Hrushi02/Root_Math"

# Load base model with authentication
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.float16,
    device_map="auto",
    use_auth_token=api_token  # ✅ Correct
)

# Load fine-tuned model
model = PeftModel.from_pretrained(base_model, peft_model_name, token=api_token)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_name, token=api_token)

# Ensure pad_token is set
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # Build messages list
    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})

    # Apply chat template
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

    # Tokenize input
    inputs = tokenizer([prompt], return_tensors="pt").to(model.device)

    # Generate response with streaming
    with torch.no_grad():
        for new_token in model.generate(
            **inputs,
            max_new_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
            repetition_penalty=1.1,
            streamer=None,  # We'll handle streaming manually
        ):
            # Decode the new token
            new_token_decoded = tokenizer.decode(new_token[-1:], skip_special_tokens=True)
            yield new_token_decoded

# Note: For true token-by-token streaming in Gradio, the above yields per-token.
# If you want full sentence streaming, accumulate and yield periodically, but this matches the original's per-token yield.

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a helpful math assistant specialized in solving equations and finding roots.", 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)",
        ),
    ],
    chatbot=gr.Chatbot(type="messages"),  # Modern format to avoid deprecation
    title="Root Math Chatbot",
    description="A fine-tuned Qwen2.5-Math model for solving roots and math problems."
)

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