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, get_prompt_for_method, get_feedback_for_method

# ✅ 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)

# ✅ Chatbot Response Function with Full Debugging
def respond(user_message, history, selected_method):
    if not user_message:
        return "❌ No input received.", history, selected_method

    user_message = user_message.strip().lower()  # Normalize input

    valid_methods = ["bar model", "double number line", "equation"]

    # ✅ Ensure history is a list of tuples
    if not isinstance(history, list):
        history = []
    history = [(str(h[0]), str(h[1])) for h in history if isinstance(h, tuple) and len(h) == 2]

    # ✅ Debug Logs
    print("\nDEBUG: Incoming User Message:", user_message)
    print("DEBUG: Current History:", history)
    print("DEBUG: Selected Method Before Processing:", selected_method)

    # ✅ If user selects a method, store it and provide the method-specific prompt
    if user_message in valid_methods:
        selected_method = user_message  # Store the method
        method_prompt = get_prompt_for_method(user_message)
        history.append((user_message, method_prompt))  # Store correctly formatted tuple

        print("DEBUG: Method Selected:", selected_method)
        print("DEBUG: Sending Prompt for Method:", method_prompt)

        return method_prompt, history, selected_method

    # ✅ If a method has already been selected, provide feedback
    if selected_method:
        feedback = get_feedback_for_method(selected_method, user_message)
        history.append((user_message, feedback))  # Store correctly formatted tuple

        print("DEBUG: Feedback Given:", feedback)
        print("DEBUG: Updated History:", history)

        return feedback, history, selected_method

    # ✅ Ensure chatbot always responds with a valid tuple
    error_msg = "❌ Please select a method first (Bar Model, Double Number Line, or Equation)."
    history.append((user_message, error_msg))  # Store correctly formatted tuple

    print("DEBUG: Error Triggered, No Method Selected")

    return error_msg, history, selected_method

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

    chatbot = gr.Chatbot(value=[(INITIAL_PROMPT, "Hello! Please select a method to begin.")], height=500)
    state_history = gr.State([(INITIAL_PROMPT, "Hello! Please select a method to begin.")])
    state_selected_method = gr.State(None)  # ✅ New state to track selected method

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

    # ✅ Handling user input and response logic
    user_input.submit(
        respond,
        inputs=[user_input, state_history, state_selected_method],
        outputs=[chatbot, state_history, state_selected_method]
    )

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