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 TASK_PROMPT


# Load the OpenAI API key from .env file
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-mini",
             max_tokens=512,
             temperature=0.7,
             top_p=0.95):
    """
    Calls OpenAI's ChatCompletion API to generate responses.
    - history: [(user_text, assistant_text), ...]
    - user_message: User's latest input
    """
    # System message (TASK_PROMPT) at the beginning
    messages = [{"role": "system", "content": TASK_PROMPT}]
    
    # Convert history into OpenAI format
    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})

    # Add the latest user input
    messages.append({"role": "user", "content": user_message})

    # AI-controlled gradual guidance
    if "bar model" in user_message.lower():
        return "Great! You've started using a bar model. Can you explain how you divided it? What does each section represent?"
    
    elif "double number line" in user_message.lower():
        return "Nice! How does your number line show the relationship between time and distance? Did you mark the correct intervals?"
    
    elif "ratio table" in user_message.lower():
        return "Good choice! Before I check, how did you determine the ratio for 1 hour?"
    
    elif "graph" in user_message.lower():
        return "Graphs are powerful! What key points did you plot, and why?"
    
    else:
        # OpenAI API call (fallback response)
        completion = client.chat.completions.create(
            model=model,
            messages=messages,
            max_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p
        )
        return completion.choices[0].message.content


def respond(user_message, history):
    """
    Handles user input and chatbot response in Gradio.
    - user_message: The latest input from the user.
    - history: A list of (user, assistant) message pairs.
    """
    if not user_message:
        return "", history

    # Generate AI response
    assistant_reply = gpt_call(history, user_message)

    # Append to history
    history.append((user_message, assistant_reply))

    # Return the updated history and clear the input box
    return "", history


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

    # Initial chatbot message (starts with the task)
    chatbot = gr.Chatbot(
        value=[("", INITIAL_PROMPT)],
        height=500
    )

    # Chat history state
    state_history = gr.State([("", INITIAL_PROMPT)])

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

    # When user submits input → respond() updates chatbot
    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]
    )

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