app.py

import os
import gradio as gr
from dotenv import load_dotenv
from openai import OpenAI
from prompts.main_prompt import MAIN_PROMPT

# Load API key
if os.path.exists(".env"):
    load_dotenv(".env")

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

client = OpenAI(api_key=OPENAI_API_KEY)

def respond(user_message, history):
    if not user_message:
        return "", history

    assistant_reply = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": MAIN_PROMPT},
            *[
                {"role": "user", "content": u} if i % 2 == 0 else {"role": "assistant", "content": a}
                for i, (u, a) in enumerate(history)
            ],
            {"role": "user", "content": user_message}
        ],
        max_tokens=512,
        temperature=0.7,
    ).choices[0].message.content

    history.append((user_message, assistant_reply))

    return "", history

# ✅ **Explicitly tell Gradio to use MathJax for LaTeX rendering**
with gr.Blocks() as demo:
    gr.Markdown("## AI-Guided Math PD Chatbot")
    chatbot = gr.Chatbot(height=500)

    state_history = gr.State([("", MAIN_PROMPT)])

    user_input = gr.Textbox(placeholder="Type your message here...", label="Your Input")

    user_input.submit(
        respond,
        inputs=[user_input, state_history],
        outputs=[user_input, chatbot]
    ).then(
        fn=lambda _, h: h,
        inputs=[user_input, chatbot],
        outputs=[state_history]
    )

    gr.Markdown(
        r"""
        <script type="text/javascript">
            MathJax = {
                tex: { inlineMath: [['$', '$'], ['\\(', '\\)']] }
            };
        </script>
        """
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)