app.py

import os
import gradio as gr
from dotenv import load_dotenv
from openai import OpenAI
from prompts.initial_prompt import INITIAL_PROMPT
from prompts.main_prompt import MAIN_PROMPT, PROBLEM_SOLUTIONS_PROMPT  # Ensure both are imported

# Load the API key from the .env file if available
if os.path.exists(".env"):
    load_dotenv(".env")

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

client = OpenAI(api_key=OPENAI_API_KEY)


def gpt_call(history, user_message,
             model="gpt-4o",
             max_tokens=512,
             temperature=0.7,
             top_p=0.95):
    """
    Calls the OpenAI API to generate a response.
    - history: [(user_text, assistant_text), ...]
    - user_message: The latest user message
    """
    # 1) Start with the system message (MAIN_PROMPT) for context
    messages = [{"role": "system", "content": MAIN_PROMPT}]

    # 2) Append conversation history
    for user_text, assistant_text in history:
        if user_text:
            messages.append({"role": "user", "content": user_text})
        if assistant_text:
            messages.append({"role": "assistant", "content": assistant_text})

    # 3) Add the user's new message
    messages.append({"role": "user", "content": user_message})

    # 4) Call OpenAI API
    completion = client.chat.completions.create(
        model=model,
        messages=messages,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p
    )

    # 5) Ensure LaTeX equations are properly wrapped in double $$ for rendering
    response_text = completion.choices[0].message.content
    formatted_response = response_text.replace("\\[", "$$").replace("\\]", "$$")
    formatted_response = formatted_response.replace("\\(", "$").replace("\\)", "$")

    return formatted_response


def respond(user_message, history):
    """
    Handles user input and gets GPT-generated response.
    - user_message: The message from the user
    - history: List of (user, assistant) conversation history
    """
    if not user_message:
        return "", history

    # If the user asks for a solution, inject PROBLEM_SOLUTIONS_PROMPT
    if "solution" in user_message.lower():
        assistant_reply = gpt_call(history, PROBLEM_SOLUTIONS_PROMPT)
    else:
        assistant_reply = gpt_call(history, user_message)

    # Add conversation turn to history
    history.append((user_message, assistant_reply))

    return "", history


##############################
#  Gradio Blocks UI
##############################
with gr.Blocks() as demo:
    gr.Markdown("## AI-Guided Math PD Chatbot")

    # Chatbot initialization with the first AI message
    chatbot = gr.Chatbot(
        value=[("", INITIAL_PROMPT)],  # Initial system prompt
        height=500
    )

    # Stores the chat history
    state_history = gr.State([("", INITIAL_PROMPT)])

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

    # Submit action
    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]
    )

# Run the Gradio app
if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)