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

# Load 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=1024,
             temperature=0.7,
             top_p=0.95):
    """
    Calls OpenAI Chat API to generate responses.
    - history: [(user_text, assistant_text), ...]
    - user_message: latest message from user
    """
    messages = [{"role": "system", "content": MAIN_PROMPT}]
    
    # Add 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})

    messages.append({"role": "user", "content": user_message})
    
    # OpenAI API Call
    completion = client.chat.completions.create(
        model=model,
        messages=messages,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p
    )
    
    response = completion.choices[0].message.content
    
    # Encourage teachers to explain their reasoning before providing guidance
    if "solve" in user_message.lower() or "explain" in user_message.lower():
        response = "Great! Before we move forward, can you explain your reasoning? Why do you think this is the right approach? Once you share your thoughts, I'll guide you further.\n\n" + response
    
    # Encourage problem posing
    if "pose a problem" in user_message.lower():
        response += "\n\nNow that you've explored this concept, try creating your own problem related to it. How would you challenge your students?"
    
    # Cover Common Core practice standards
    if "common core" in user_message.lower():
        response += "\n\nHow do you see this aligning with Common Core practice standards? Can you identify any specific standards this connects to?"
    
    # Encourage creativity-directed practices
    if "creativity" in user_message.lower():
        response += "\n\nHow did creativity play a role in this problem-solving process? Did you find any opportunities to think differently?"
    
    # Provide structured summary
    if "summary" in user_message.lower():
        response += "\n\nSummary: Today, we explored problem-solving strategies, reflected on reasoning, and connected ideas to teaching practices. We examined key characteristics of proportional and non-proportional relationships, explored their graphical representations, and considered pedagogical approaches. Keep thinking about how these concepts can be applied in your own classroom!"
    
    return response

def respond(user_message, history):
    """
    Handles user input and chatbot responses.
    """
    if not user_message:
        return "", history

    assistant_reply = gpt_call(history, user_message)
    history.append((user_message, assistant_reply))
    return "", history

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

    chatbot = gr.Chatbot(
        value=[("", INITIAL_PROMPT)],
        height=600
    )

    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)