import json
from pathlib import Path
from typing import List, NoReturn

import questionary
from rich.console import Console

from cli.models import AnalystType

console = Console()

CONFIG_PATH = Path(__file__).resolve().parents[1] / "config.json"

def _exit_with_config_error(message: str) -> NoReturn:
    """Exit with error message. This function never returns."""
    console.print(f"\n[red]{message}[/red]")
    raise SystemExit(1)


def _load_config() -> dict:
    try:
        with CONFIG_PATH.open("r", encoding="utf-8") as config_file:
            return json.load(config_file)
    except FileNotFoundError:
        _exit_with_config_error(f"Config file not found: {CONFIG_PATH}")
    except json.JSONDecodeError as exc:
        _exit_with_config_error(f"Invalid JSON in config file: {exc}")
    except OSError as exc:
        _exit_with_config_error(f"Unable to read config file: {exc}")


def _get_config_section(config: dict, key: str, expected_type: type):
    value = config.get(key)
    if not isinstance(value, expected_type):
        _exit_with_config_error(
            f"Invalid or missing '{key}' in config file: {CONFIG_PATH}"
        )
    return value


CONFIG = _load_config()
BASE_URLS = [
    (display, url)
    for display, url in _get_config_section(CONFIG, "BASE_URLS", list)
]
DEEP_AGENT_OPTIONS = {
    provider: [(display, model) for display, model in options]
    for provider, options in _get_config_section(
        CONFIG, "DEEP_AGENT_OPTIONS", dict
    ).items()
}
SHALLOW_AGENT_OPTIONS = {
    provider: [(display, model) for display, model in options]
    for provider, options in _get_config_section(
        CONFIG, "SHALLOW_AGENT_OPTIONS", dict
    ).items()
}

TICKER_INPUT_EXAMPLES = "Examples: SPY, CNC.TO, 7203.T, 0700.HK"

ANALYST_ORDER = [
    ("Market Analyst", AnalystType.MARKET),
    ("Social Media Analyst", AnalystType.SOCIAL),
    ("News Analyst", AnalystType.NEWS),
    ("Fundamentals Analyst", AnalystType.FUNDAMENTALS),
]


def get_ticker() -> str:
    """Prompt the user to enter a ticker symbol."""
    ticker = questionary.text(
        f"Enter the exact ticker symbol to analyze ({TICKER_INPUT_EXAMPLES}):",
        validate=lambda x: len(x.strip()) > 0 or "Please enter a valid ticker symbol.",
        style=questionary.Style(
            [
                ("text", "fg:green"),
                ("highlighted", "noinherit"),
            ]
        ),
    ).ask()

    if not ticker:
        console.print("\n[red]No ticker symbol provided. Exiting...[/red]")
        exit(1)

    return normalize_ticker_symbol(ticker)


def normalize_ticker_symbol(ticker: str) -> str:
    """Normalize ticker input while preserving exchange suffixes."""
    return ticker.strip().upper()


def get_analysis_date() -> str:
    """Prompt the user to enter a date in YYYY-MM-DD format."""
    import re
    from datetime import datetime

    def validate_date(date_str: str) -> bool:
        if not re.match(r"^\d{4}-\d{2}-\d{2}$", date_str):
            return False
        try:
            datetime.strptime(date_str, "%Y-%m-%d")
            return True
        except ValueError:
            return False

    date = questionary.text(
        "Enter the analysis date (YYYY-MM-DD):",
        validate=lambda x: validate_date(x.strip())
        or "Please enter a valid date in YYYY-MM-DD format.",
        style=questionary.Style(
            [
                ("text", "fg:green"),
                ("highlighted", "noinherit"),
            ]
        ),
    ).ask()

    if not date:
        console.print("\n[red]No date provided. Exiting...[/red]")
        exit(1)

    return date.strip()


def select_analysts() -> List[AnalystType]:
    """Select analysts using an interactive checkbox."""
    choices = questionary.checkbox(
        "Select Your [Analysts Team]:",
        choices=[
            questionary.Choice(display, value=value) for display, value in ANALYST_ORDER
        ],
        instruction="\n- Press Space to select/unselect analysts\n- Press 'a' to select/unselect all\n- Press Enter when done",
        validate=lambda x: len(x) > 0 or "You must select at least one analyst.",
        style=questionary.Style(
            [
                ("checkbox-selected", "fg:green"),
                ("selected", "fg:green noinherit"),
                ("highlighted", "noinherit"),
                ("pointer", "noinherit"),
            ]
        ),
    ).ask()

    if not choices:
        console.print("\n[red]No analysts selected. Exiting...[/red]")
        exit(1)

    return choices


def select_research_depth() -> int:
    """Select research depth using an interactive selection."""

    # Define research depth options with their corresponding values
    DEPTH_OPTIONS = [
        ("Shallow - Quick research, few debate and strategy discussion rounds", 1),
        ("Medium - Middle ground, moderate debate rounds and strategy discussion", 3),
        ("Deep - Comprehensive research, in depth debate and strategy discussion", 5),
    ]

    choice = questionary.select(
        "Select Your [Research Depth]:",
        choices=[
            questionary.Choice(display, value=value) for display, value in DEPTH_OPTIONS
        ],
        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
        style=questionary.Style(
            [
                ("selected", "fg:yellow noinherit"),
                ("highlighted", "fg:yellow noinherit"),
                ("pointer", "fg:yellow noinherit"),
            ]
        ),
    ).ask()

    if choice is None:
        console.print("\n[red]No research depth selected. Exiting...[/red]")
        exit(1)

    return choice


def select_shallow_thinking_agent(provider) -> str:
    """Select shallow thinking llm engine using an interactive selection."""

    choice = questionary.select(
        "Select Your [Quick-Thinking LLM Engine]:",
        choices=[
            questionary.Choice(display, value=value)
            for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
        ],
        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
        style=questionary.Style(
            [
                ("selected", "fg:magenta noinherit"),
                ("highlighted", "fg:magenta noinherit"),
                ("pointer", "fg:magenta noinherit"),
            ]
        ),
    ).ask()

    if choice is None:
        console.print(
            "\n[red]No shallow thinking llm engine selected. Exiting...[/red]"
        )
        exit(1)

    return choice


def select_deep_thinking_agent(provider) -> str:
    """Select deep thinking llm engine using an interactive selection."""

    choice = questionary.select(
        "Select Your [Deep-Thinking LLM Engine]:",
        choices=[
            questionary.Choice(display, value=value)
            for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
        ],
        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
        style=questionary.Style(
            [
                ("selected", "fg:magenta noinherit"),
                ("highlighted", "fg:magenta noinherit"),
                ("pointer", "fg:magenta noinherit"),
            ]
        ),
    ).ask()

    if choice is None:
        console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]")
        exit(1)

    return choice

def select_llm_provider() -> tuple[str, str]:
    """Select the OpenAI api url using interactive selection."""
    choice = questionary.select(
        "Select your LLM Provider:",
        choices=[
            questionary.Choice(display, value=(display, value))
            for display, value in BASE_URLS
        ],
        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
        style=questionary.Style(
            [
                ("selected", "fg:magenta noinherit"),
                ("highlighted", "fg:magenta noinherit"),
                ("pointer", "fg:magenta noinherit"),
            ]
        ),
    ).ask()
    
    if choice is None:
        console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
        exit(1)
    
    display_name, url = choice
    print(f"You selected: {display_name}\tURL: {url}")

    return display_name, url


def ask_openai_reasoning_effort() -> str:
    """Ask for OpenAI reasoning effort level."""
    choices = [
        questionary.Choice("Medium (Default)", "medium"),
        questionary.Choice("High (More thorough)", "high"),
        questionary.Choice("Low (Faster)", "low"),
    ]
    return questionary.select(
        "Select Reasoning Effort:",
        choices=choices,
        style=questionary.Style([
            ("selected", "fg:cyan noinherit"),
            ("highlighted", "fg:cyan noinherit"),
            ("pointer", "fg:cyan noinherit"),
        ]),
    ).ask()


def ask_anthropic_effort() -> str | None:
    """Ask for Anthropic effort level.

    Controls token usage and response thoroughness on Claude 4.5+ and 4.6 models.
    """
    return questionary.select(
        "Select Effort Level:",
        choices=[
            questionary.Choice("High (recommended)", "high"),
            questionary.Choice("Medium (balanced)", "medium"),
            questionary.Choice("Low (faster, cheaper)", "low"),
        ],
        style=questionary.Style([
            ("selected", "fg:cyan noinherit"),
            ("highlighted", "fg:cyan noinherit"),
            ("pointer", "fg:cyan noinherit"),
        ]),
    ).ask()


def ask_gemini_thinking_config() -> str | None:
    """Ask for Gemini thinking configuration.

    Returns thinking_level: "high" or "minimal".
    Client maps to appropriate API param based on model series.
    """
    return questionary.select(
        "Select Thinking Mode:",
        choices=[
            questionary.Choice("Enable Thinking (recommended)", "high"),
            questionary.Choice("Minimal/Disable Thinking", "minimal"),
        ],
        style=questionary.Style([
            ("selected", "fg:green noinherit"),
            ("highlighted", "fg:green noinherit"),
            ("pointer", "fg:green noinherit"),
        ]),
    ).ask()