from dotenv import load_dotenv
from openai import OpenAI
from pypdf import PdfReader
import gradio as gr
import requests
import json
import os

from reflection_memory import init_db, save_reflection, fetch_recent


# ==================================================
# Environment
# ==================================================

load_dotenv(override=True)

PUSHOVER_TOKEN = os.getenv("PUSHOVER_TOKEN")
PUSHOVER_USER = os.getenv("PUSHOVER_USER")

MODEL_NAME = "gpt-4o-mini"
MAX_HISTORY_MESSAGES = 6
MAX_TURNS = 5


# ==================================================
# Conversation awareness helpers
# ==================================================

UNCERTAINTY_MARKERS = (
    "not sure",
    "confused",
    "stuck",
    "overwhelmed",
    "i don't know",
    "lost",
    "anxious",
)

def should_slow_down(text: str) -> bool:
    return any(marker in text.lower() for marker in UNCERTAINTY_MARKERS)


# ==================================================
# Notifications (Pushover)
# ==================================================

def push(message: str) -> None:
    if not PUSHOVER_TOKEN or not PUSHOVER_USER:
        return

    try:
        requests.post(
            "https://api.pushover.net/1/messages.json",
            data={
                "token": PUSHOVER_TOKEN,
                "user": PUSHOVER_USER,
                "message": message,
            },
            timeout=5,
        )
    except Exception as e:
        print("Pushover failed:", e)


# ==================================================
# Tool Implementations
# ==================================================

def record_user_details(
    email: str,
    name: str = "Name not provided",
    notes: str = "not provided",
):
    push(f"New contact: {name} | {email} | Notes: {notes}")
    return {"recorded": "ok"}


def record_unknown_question(question: str):
    push(f"Unknown question: {question}")
    return {"recorded": "ok"}


# ==================================================
# Tool Schemas
# ==================================================

TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "record_user_details",
            "description": "Record that a user is interested in being in touch",
            "parameters": {
                "type": "object",
                "properties": {
                    "email": {"type": "string"},
                    "name": {"type": "string"},
                    "notes": {"type": "string"},
                },
                "required": ["email"],
                "additionalProperties": False,
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "record_unknown_question",
            "description": "Record any question that could not be answered",
            "parameters": {
                "type": "object",
                "properties": {
                    "question": {"type": "string"},
                },
                "required": ["question"],
                "additionalProperties": False,
            },
        },
    },
]


# ==================================================
# Chatbot
# ==================================================

class Me:
    def __init__(self):
        self.client = OpenAI()
        self.name = "Urvashi Patel"

        init_db()

        self.linkedin = self._load_pdf("me/linkedin.pdf")
        self.summary = self._load_text("me/summary.txt")
        self.about_urva = self._load_text("me/about_urva.md")

    # -----------------------------
    # Loaders
    # -----------------------------

    def _load_text(self, path: str) -> str:
        try:
            with open(path, "r", encoding="utf-8") as f:
                return f.read()
        except Exception as e:
            print(f"Failed to load {path}:", e)
            return ""

    def _load_pdf(self, path: str) -> str:
        text = ""
        try:
            reader = PdfReader(path)
            for page in reader.pages:
                page_text = page.extract_text()
                if page_text:
                    text += page_text
        except Exception as e:
            print(f"Failed to load {path}:", e)
        return text

    # -----------------------------
    # Prompt
    # -----------------------------

    def system_prompt(self) -> str:
        return f"""
You are not a general AI assistant. You only answer as Urvashi Patel.

You are acting as {self.name} on her personal website.
Answer questions about career, background, skills, experience, and interests.

Be thoughtful, grounded, and professional.
Avoid generic advice. Prefer clarity and context.

If asked for medical, legal, or financial advice, politely decline.

When the user sounds uncertain:
- Ask ONE reflective question
- Do not rush to solutions
- Offer options, not instructions

## Personal Context
{self.about_urva}

## Summary
{self.summary}

## LinkedIn
{self.linkedin}

Stay in character at all times.
"""

    # -----------------------------
    # Tool handling
    # -----------------------------

    def handle_tool_calls(self, tool_calls):
        results = []

        for call in tool_calls:
            tool_name = call.function.name
            arguments = json.loads(call.function.arguments)

            tool_fn = globals().get(tool_name)
            result = tool_fn(**arguments) if tool_fn else {}

            results.append(
                {
                    "role": "tool",
                    "content": json.dumps(result),
                    "tool_call_id": call.id,
                }
            )

        return results

    # -----------------------------
    # Chat loop
    # -----------------------------

    def chat(self, message, history):
        trimmed_history = history[-MAX_HISTORY_MESSAGES:]

        # Fetch light reflection memory
        recent_reflections = fetch_recent()
        reflection_context = ""

        if recent_reflections:
            reflection_context = "\n".join(
                f"- {r['theme']}: {r['summary']}"
                for r in recent_reflections
            )

        messages = [
            {"role": "system", "content": self.system_prompt()}
        ]

        if reflection_context:
            messages.append(
                {
                    "role": "system",
                    "content": f"Recent reflections for continuity:\n{reflection_context}",
                }
            )

        messages.extend(trimmed_history)
        messages.append({"role": "user", "content": message})

        for _ in range(MAX_TURNS):
            response = self.client.chat.completions.create(
                model=MODEL_NAME,
                messages=messages,
                tools=TOOLS,
            )

            choice = response.choices[0]

            if choice.finish_reason == "tool_calls":
                messages.append(choice.message)
                messages.extend(self.handle_tool_calls(choice.message.tool_calls))
            else:
                final_answer = choice.message.content

                theme = "direction" if should_slow_down(message) else "general"

                save_reflection(
                    theme=theme,
                    user_question=message,
                    assistant_summary=final_answer[:300],
                )

                return final_answer

        return "Thanks for your question — feel free to rephrase or reach out directly."


# ==================================================
# UI
# ==================================================

if __name__ == "__main__":
    me = Me()

    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown(
        """
        ### About Urvashi Patel — Integration Developer  
        Ask about experience, skills, projects, and approach to integration and automation.
        """
        )

        gr.ChatInterface(
        me.chat,
        type="messages",
        submit_btn="Reflect",
        )

    demo.launch()