app.py

from transformers import AutoModelForCausalLM, AutoTokenizer
from latex2mathml.converter import convert
import gradio as gr
import torch
import re
import spaces

# Initialize the model and tokenizer
model_name = "Qwen/Qwen2.5-Math-1.5B-Instruct"
device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
    device_map="auto" if device == "cuda" else None
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# System instruction
SYSTEM_INSTRUCTION = (
    "You are a helpful and patient math tutor tasked with providing step-by-step hints and guidance for solving math problems."
    "Your primary role is to assist learners in understanding how to approach and solve problems without revealing the final answer, even if explicitly requested."
    "Always encourage the learner to solve the problem themselves by offering incremental hints and explanations."
    "Under no circumstances should you provide the complete solution or final answer."
)


def render_latex_to_mathml(text):
    """
    Converts LaTeX expressions in the text to MathML.
    """
    try:
        mathml = convert(text)  # Converts LaTeX to MathML
        return f"<math xmlns='http://www.w3.org/1998/Math/MathML'>{mathml}</math>"
    except Exception as e:
        return f"<span>Error rendering LaTeX: {str(e)}</span>"

def preprocess_response(response):
    """
    Preprocess the response to convert LaTeX expressions in the text to MathML.
    Only parts of the text that contain LaTeX are converted.
    """
    # Regex patterns to detect LaTeX expressions
    inline_latex_pattern = r"\$([^\$]+)\$"  # Matches inline LaTeX between single $
    block_latex_pattern = r"\$\$([^\$]+)\$\$"  # Matches block LaTeX between $$

    # Replace block LaTeX
    def replace_block(match):
        latex_code = match.group(1)
        try:
            return render_latex_to_mathml(latex_code)
        except Exception as e:
            return f"<span>Error rendering block LaTeX: {str(e)}</span>"

    # Replace inline LaTeX
    def replace_inline(match):
        latex_code = match.group(1)
        try:
            return render_latex_to_mathml(latex_code)
        except Exception as e:
            return f"<span>Error rendering inline LaTeX: {str(e)}</span>"

    # First process block LaTeX
    response = re.sub(block_latex_pattern, replace_block, response)

    # Then process inline LaTeX
    response = re.sub(inline_latex_pattern, replace_inline, response)

    return response

def apply_chat_template(messages):
    """
    Prepares the messages for the model using the tokenizer's chat template.
    """
    return tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )

@spaces.GPU
def generate_response(chat_history, user_input):
    """
    Generates a response from the model based on the chat history and user input.
    """
    # Append user input to chat history
    chat_history.append(("User", user_input + "\n\n strinctly prohibited to reveal answer only provide hints and guidelines to solve this"))

    # Prepare messages for the model
    messages = [{"role": "system", "content": SYSTEM_INSTRUCTION}] + [
        {"role": "user", "content": msg[1]} if msg[0] == "User" else {"role": "assistant", "content": msg[1]}
        for msg in chat_history
    ]

    # Tokenize the input for the model
    text = apply_chat_template(messages)
    model_inputs = tokenizer([text], return_tensors="pt").to(device)

    # Generate the model's response
    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=512
    )
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

    rendered_response = preprocess_response(response)

    # Append AI response to chat history
    chat_history.append(("MathTutor", rendered_response))

    

    # Return updated chat history
    return chat_history

def format_chat_history(history):
    """
    Formats the conversation history for a user-friendly chat display.
    """
    chat_display = ""
    for message in history:
        if message["role"] == "user":
            chat_display += f"**User:** {message['content']}\n\n"
        elif message["role"] == "assistant":
            chat_display += f"**MathTutor:** {message['content']}\n\n"
    return chat_display

# Gradio chat interface
def create_chat_interface():
    """
    Creates the Gradio interface for the chat application.
    """
    with gr.Blocks() as chat_app:
        gr.Markdown("## Math Hint Chat")
        gr.Markdown(
            "This chatbot provides hints and step-by-step guidance for solving math problems. "
        )

        chatbot = gr.Chatbot(label="Math Tutor Chat", elem_id="chat-container")
        user_input = gr.Textbox(
            placeholder="Ask your math question here (e.g., Solve for x: 4x + 5 = 6x + 7)",
            label="Your Query"
        )
        send_button = gr.Button("Send")

        # Hidden state for managing chat history
        chat_history = gr.State([])

        # Button interaction for chat
        send_button.click(
            fn=generate_response,
            inputs=[chat_history, user_input],
            outputs=[chatbot]
        )

    return chat_app



app = create_chat_interface()
app.launch(debug=True)