import os

import gradio as gr
import openai
from dotenv import load_dotenv

# Load environment variables
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

# System prompt for intent/entity extraction
SYSTEM_PROMPT = (
    "You are a customer support assistant. "
    "For every user message, reply with a JSON object containing: "
    "'intent' (the user's intent), 'entities' (dictionary of extracted entities), "
    "and 'response' (your natural reply). "
    'Example: {"intent": "order_status", "entities": {"order_number": "12345"}, "response": "Your order 12345 is being processed."} '
    "If no entities, use an empty dictionary."
)

REQUEST_LIMIT = 200
global_request_count = 0


# Chatbot function using OpenAI ChatGPT API (OpenAI Python v1.x)
def chatbot(user_message, history):
    global global_request_count
    if global_request_count >= REQUEST_LIMIT:
        display = "[Request limit reached. No further requests will be processed.]"
        return history + [[user_message, display]]
    global_request_count += 1
    if history is None:
        history = []
    # Track known entities across conversation
    known_entities = {}
    import json
    import re

    # Extract entities from previous bot replies
    for _, bot in history:
        match = re.search(r"\{[\s\S]*\}", bot)
        if match:
            try:
                parsed = json.loads(match.group(0))
                entities = parsed.get("entities", {})
                if isinstance(entities, dict):
                    known_entities.update(entities)
            except Exception:
                pass
    # Prepend known entities to user message if any
    if known_entities:
        entity_context = f"[Known entities: {json.dumps(known_entities)}] "
        user_message = entity_context + user_message
    # Prepare messages for OpenAI API
    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
    for user, bot in history:
        messages.append({"role": "user", "content": user})
        messages.append({"role": "assistant", "content": bot})
    messages.append({"role": "user", "content": user_message})
    try:
        response = openai.chat.completions.create(
            model="gpt-4.1-mini",
            messages=messages,
            max_completion_tokens=256,
            temperature=0.7,
        )
        raw_content = response.choices[0].message.content.strip()
        # Try to parse JSON
        try:
            # Extract JSON from code block or text
            match = re.search(r"\{[\s\S]*\}", raw_content)
            if match:
                json_str = match.group(0)
                parsed = json.loads(json_str)
                display = (
                    f"**Intent:** {parsed.get('intent','')}\n"
                    f"**Entities:** {parsed.get('entities','')}\n"
                    f"**Bot:** {parsed.get('response','')}"
                )
            else:
                display = f"[Could not parse intent/entities]\n{raw_content}"
        except Exception:
            display = f"[Could not parse intent/entities]\n{raw_content}"
    except Exception as e:
        display = f"[Error: {str(e)}]"
    return history + [[user_message, display]]


with gr.Blocks() as demo:
    gr.Markdown("# Customer Support Chatbot (MVP)")
    chatbot_ui = gr.Chatbot()
    msg = gr.Textbox(label="Your message")
    clear = gr.Button("Clear")

    def respond(user_message, chat_history):
        new_history = chatbot(user_message, chat_history)
        return "", new_history  # Clear textbox after submit

    msg.submit(respond, [msg, chatbot_ui], [msg, chatbot_ui])
    clear.click(lambda: ("", None), None, [msg, chatbot_ui], queue=False)

demo.launch()