from dotenv import load_dotenv
from openai import OpenAI
import json
import os
import requests
from PyPDF2 import PdfReader
import gradio as gr
import gdown
from datetime import datetime
from pathlib import Path
import zipfile

load_dotenv(override=True)

def push(text):
    try:
        Path("chat_logs").mkdir(exist_ok=True)
        keep_path = Path("chat_logs/.keep")
        if not keep_path.exists():
            keep_path.touch()
        requests.post(
            "https://api.pushover.net/1/messages.json",
            data={
                "token": os.getenv("PUSHOVER_TOKEN"),
                "user": os.getenv("PUSHOVER_USER"),
                "message": text,
            }
        )
    except Exception as e:
        print(f"Pushover error: {e}")

def record_user_details(email, name="Name not provided", notes="not provided"):
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"chat_logs/session_{timestamp}.json"
    latest_log = "\n".join([
        f"{entry['role'].capitalize()}: {entry['content'][:200]}"
        for entry in me.session_log[-6:]
    ])
    with open(filename, "w", encoding="utf-8") as f:
        json.dump(me.session_log, f, indent=2)
    msg = f"[New Contact]\nName: {name}\nEmail: {email}\nNotes: {notes}\n\n🔗 View log: {filename}\n\nLast messages:\n{latest_log}"
    push(msg)
    return {"recorded": "ok"}

def record_unknown_question(question):
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"chat_logs/session_{timestamp}.json"
    latest_log = "\n".join([
        f"{entry['role'].capitalize()}: {entry['content'][:200]}"
        for entry in me.session_log[-6:]
    ])
    with open(filename, "w", encoding="utf-8") as f:
        json.dump(me.session_log, f, indent=2)
    msg = f"[Unknown Question]\nQ: {question}\n\n🔗 View log: {filename}\n\nLast messages:\n{latest_log}"
    push(msg)
    return {"recorded": "ok"}

record_user_details_json = {
    "name": "record_user_details",
    "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
    "parameters": {
        "type": "object",
        "properties": {
            "email": {"type": "string"},
            "name": {"type": "string"},
            "notes": {"type": "string"}
        },
        "required": ["email"],
        "additionalProperties": False
    }
}

record_unknown_question_json = {
    "name": "record_unknown_question",
    "description": "Record a question that couldn't be answered",
    "parameters": {
        "type": "object",
        "properties": {
            "question": {"type": "string"}
        },
        "required": ["question"],
        "additionalProperties": False
    }
}

tools = [
    {"type": "function", "function": record_user_details_json},
    {"type": "function", "function": record_unknown_question_json}
]

class Me:
    def __init__(self):
        self.openai = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
        self.name = "Jacob Isaacson"
        self.session_log = []
        Path("chat_logs").mkdir(exist_ok=True)
        keep_path = Path("chat_logs/.keep")
        if not keep_path.exists():
            keep_path.touch()

        gdown.download("https://drive.google.com/uc?id=1xz2RowkImpI8odYv8zvKdlRHaKfILn40", "linkedin.pdf", quiet=False)
        reader = PdfReader("linkedin.pdf")
        self.linkedin = "".join(page.extract_text() or "" for page in reader.pages)

        gdown.download("https://drive.google.com/uc?id=1hjJz082YFSVjFtpO0pwT6Tyy3eLYYj6-", "summary.txt", quiet=False)
        with open("summary.txt", "r", encoding="utf-8") as f:
            self.summary = f.read()

        self.archive_logs()

    def system_prompt(self):
        return f"""You are acting as {self.name}. You're answering questions on {self.name}'s website about his career, experience, and skills.
Be professional and conversational, as if talking to a potential employer or client.

If you can't answer something, call `record_unknown_question`. If a user seems interested, ask for their email and use `record_user_details`.

## Summary:
{self.summary}

## LinkedIn Profile:
{self.linkedin}
"""

    def handle_tool_call(self, tool_calls):
        results = []
        for tool_call in tool_calls:
            tool_name = tool_call.function.name
            arguments = json.loads(tool_call.function.arguments)
            tool = globals().get(tool_name)
            result = tool(**arguments) if tool else {}
            results.append({"role": "tool", "tool_call_id": tool_call.id, "content": json.dumps(result)})
        return results

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

        if not history:
            self.session_log.append({"role": "assistant", "content": "Hello, my name is Jacob Isaacson. Please ask me any questions about my professional career and I will do my best to respond."})
            yield self.session_log[-1]["content"]

        for msg in history:
            if isinstance(msg, dict) and msg.get("role") in ["user", "assistant"]:
                messages.append(msg)

        messages.append({"role": "user", "content": message})
        self.session_log.append({"role": "user", "content": message})

        response = self.openai.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=tools,
            stream=True
        )

        full_response = ""
        for chunk in response:
            delta = chunk.choices[0].delta
            if hasattr(delta, "content") and delta.content:
                full_response += delta.content
                yield full_response

        full_response += "\n\n💬 Let me know if you’d like to follow up or need help connecting with Jacob."
        self.session_log.append({"role": "assistant", "content": full_response})
        self.save_session_log()

    def save_session_log(self):
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"chat_logs/session_{timestamp}.json"
        with open(filename, "w", encoding="utf-8") as f:
            json.dump(self.session_log, f, indent=2)

    def archive_logs(self):
        zip_path = "chat_logs/weekly_archive.zip"
        with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as archive:
            for log_file in Path("chat_logs").glob("session_*.json"):
                archive.write(log_file, arcname=log_file.name)

me = Me()

with gr.Blocks(title="Jacob Isaacson Chatbot") as iface:
    with gr.Row():
        gr.Image("jacob.png", width=100, show_label=False)
        gr.Markdown("### Chat with Jacob Isaacson\nAsk about Jacob's background, skills, or career. \n🛡️ *All chats are logged for improvement purposes.*")

    gr.ChatInterface(
        fn=me.chat_stream,
        chatbot=gr.Chatbot(show_copy_button=True),
        type="messages",
        additional_inputs=[],
    )

if __name__ == "__main__":
    iface.launch()