from dotenv import load_dotenv
from openai import OpenAI
from pypdf import PdfReader
import gradio as gr
import datetime
from collections import defaultdict
import os

# Load environment variables from .env (includes OPENAI_API_KEY)
load_dotenv(override=True)

# In-memory daily question tracker
user_question_counter = defaultdict(lambda: {"date": None, "count": 0})


class Me:
    def __init__(self):
        self.openai = OpenAI()
        self.name = "Narendra"

        # Load LinkedIn profile text from PDF
        reader = PdfReader("me/linkedin.pdf")
        self.linkedin = ""
        for page in reader.pages:
            text = page.extract_text()
            if text:
                self.linkedin += text

        # Load summary text
        with open("me/summary.txt", "r", encoding="utf-8") as f:
            self.summary = f.read()

    def system_prompt(self):
        return (
            f"You are acting as {self.name}, an experienced Python technical interviewer. "
            f"You are helping users by asking or answering Python-related technical questions. "
            f"Always stay professional, helpful, and concise. Do not generate responses over 100 tokens. "
            f"The user can only ask 3 questions per day—enforce this limit politely. "
            f"\n\n## About {self.name} (your interviewer):\n"
            f"{self.summary}\n\n"
            f"## LinkedIn Profile:\n{self.linkedin}\n\n"
            f"Use this background to answer in character as {self.name}."
        )

    def chat(self, message, history):
        user_id = "user"  # Replace with session-based ID for real tracking
        today = datetime.date.today()
        record = user_question_counter[user_id]

        # Reset question count if date changed
        if record["date"] != today:
            record["date"] = today
            record["count"] = 0

        # Check daily question limit
        if record["count"] >= 3:
            return "🚫 You've reached your daily limit of 3 questions. Please try again tomorrow."

        # Prepare conversation
        messages = [{"role": "system", "content": self.system_prompt()}] + history + [{"role": "user", "content": message}]
        
        response = self.openai.chat.completions.create(
            model="gpt-4o-mini",
            messages=messages,
            max_tokens=100
        )

        record["count"] += 1
        return f"👋 Narendra is your Python interviewer. Let's begin!\n\n{response.choices[0].message.content}"


if __name__ == "__main__":
    me = Me()
    gr.ChatInterface(me.chat, type="messages").launch(share=True)