import os
import gradio as gr
from dotenv import load_dotenv
from openai import OpenAI
from prompts.main_prompt import MAIN_PROMPT, get_prompt_for_method, get_feedback_for_method
from prompts.initial_prompt import INITIAL_PROMPT

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

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

# ✅ Ensure API Key is available
if not OPENAI_API_KEY:
    raise ValueError("🚨 OpenAI API key is missing! Set it in the .env file.")

client = OpenAI(api_key=OPENAI_API_KEY)

# ✅ Track the selected method
selected_method = None  # This will store which method the teacher selects

# ✅ Chatbot Response Function
def respond(user_message, history):
    """
    Handles user input for the chatbot.
    - Step 1: Teacher selects a method (Bar Model, Double Number Line, or Equation).
    - Step 2: AI asks teacher to explain their reasoning before giving guidance.
    - Step 3: AI listens to teacher's explanation and provides feedback.
    """
    global selected_method  # Store the selected method so AI remembers it

    user_message = user_message.strip().lower()

    # ✅ Step 1: Check if user selected a method (but hasn't explained yet)
    if user_message in ["bar model", "double number line", "equation"]:
        selected_method = user_message  # Store selected method
        response = get_prompt_for_method(user_message)  # Ask for reasoning
        history.append((user_message, response))
        return "", history

    # ✅ Step 2: If a method was selected, process teacher's explanation
    if selected_method:
        response = get_feedback_for_method(selected_method, user_message)  # Give feedback
        history.append((user_message, response))
        return "", history

    # ✅ If no method is selected yet, ask the user to select one
    if not selected_method:
        return "I didn’t understand that. Please select a method first (Bar Model, Double Number Line, or Equation).", history

    return "", history  # ✅ Maintain conversation flow

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

    chatbot = gr.Chatbot(value=[(INITIAL_PROMPT, "")], height=500)  
    state_history = gr.State([(INITIAL_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]
    )

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