import questionary
from typing import List, Optional, Tuple, Dict
from rich.console import Console

from cli.models import AnalystType
from cli.api_keys import is_provider_available
from cli.model_fetcher import fetch_models_for_provider

console = Console()

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(
        "Enter the ticker symbol to analyze:",
        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 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."""

    # Static fallback options for each provider
    SHALLOW_AGENT_OPTIONS = {
        "openai": [
            ("GPT-4o-mini - Fast and efficient for quick tasks", "gpt-4o-mini"),
            ("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
            ("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
            ("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
        ],
        "anthropic": [
            ("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
            ("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
            ("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
            ("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
        ],
        "google": [
            ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
            ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
            ("Gemini 2.5 Flash-Lite - Lightweight and cost efficient", "gemini-2.5-flash-lite"),
            ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash"),
            ("Gemini 3 Flash Preview - Latest generation flash model", "gemini-3-flash-preview"),
        ],
        "openrouter": [
            ("Xiaomi MiMo V2 Flash - Fast and efficient multimodal model", "xiaomi/mimo-v2-flash:free"),
            ("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
            ("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"),
            ("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"),
        ],
        "ollama": [
            ("llama3.2:3b local", "llama3.2:3b"),
            ("phi3.5 local", "phi3.5:latest"),
        ],
        "lm studio": [
            ("Local Model (default)", "local-model"),
        ]
    }

    provider_lower = provider.lower()

    # Try dynamic fetch for supported providers (OpenAI, Anthropic, Google)
    model_options = None
    if provider_lower in ["openai", "anthropic", "google"]:
        dynamic_models = fetch_models_for_provider(provider_lower)
        if dynamic_models:
            model_options = dynamic_models

    # Fall back to static list if dynamic fetch failed or not supported
    if model_options is None:
        model_options = SHALLOW_AGENT_OPTIONS.get(provider_lower, [])

    choice = questionary.select(
        "Select Your [Quick-Thinking LLM Engine]:",
        choices=[
            questionary.Choice(display, value=value)
            for display, value in model_options
        ],
        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."""

    # Static fallback options for each provider
    DEEP_AGENT_OPTIONS = {
        "openai": [
            ("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
            ("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
            ("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
            ("o4-mini - Specialized reasoning model (compact)", "o4-mini"),
            ("o3-mini - Advanced reasoning model (lightweight)", "o3-mini"),
            ("o3 - Full advanced reasoning model", "o3"),
            ("o1 - Premier reasoning and problem-solving model", "o1"),
        ],
        "anthropic": [
            ("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
            ("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
            ("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
            ("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
            ("Claude Opus 4 - Most powerful Anthropic model", "claude-opus-4-0"),
        ],
        "google": [
            ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
            ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
            ("Gemini 2.5 Flash-Lite - Lightweight and cost efficient", "gemini-2.5-flash-lite"),
            ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash"),
            ("Gemini 3 Flash Preview - Latest generation flash model", "gemini-3-flash-preview"),
        ],
        "openrouter": [
            ("Xiaomi MiMo V2 Flash - Fast and efficient multimodal model", "xiaomi/mimo-v2-flash:free"),
            ("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),
        ],
        "ollama": [
            ("llama3.2:3b local", "llama3.2:3b"),
            ("phi3.5 local", "phi3.5:latest"),
        ],
        "lm studio": [
            ("Local Model (default)", "local-model"),
        ]
    }

    provider_lower = provider.lower()

    # Try dynamic fetch for supported providers (OpenAI, Anthropic, Google)
    model_options = None
    if provider_lower in ["openai", "anthropic", "google"]:
        dynamic_models = fetch_models_for_provider(provider_lower)
        if dynamic_models:
            model_options = dynamic_models

    # Fall back to static list if dynamic fetch failed or not supported
    if model_options is None:
        model_options = DEEP_AGENT_OPTIONS.get(provider_lower, [])

    choice = questionary.select(
        "Select Your [Deep-Thinking LLM Engine]:",
        choices=[
            questionary.Choice(display, value=value)
            for display, value in model_options
        ],
        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 LLM provider using interactive selection with availability checks."""
    # Define provider options with their corresponding endpoints
    BASE_URLS = [
        ("OpenAI", "https://api.openai.com/v1"),
        ("Anthropic", "https://api.anthropic.com/"),
        ("Google", "https://generativelanguage.googleapis.com/v1"),
        ("Openrouter", "https://openrouter.ai/api/v1"),
        ("Ollama", "http://localhost:11434/v1"),
        ("LM Studio", "http://localhost:1234/v1"),
    ]

    # Build choices with availability status
    choices = []
    for display, url in BASE_URLS:
        available, reason = is_provider_available(display)
        if available:
            choices.append(questionary.Choice(display, value=(display, url)))
        else:
            # Show disabled option with reason
            disabled_label = f"{display} ({reason})"
            choices.append(questionary.Choice(
                disabled_label,
                value=(display, url),
                disabled=reason
            ))

    choice = questionary.select(
        "Select your LLM Provider:",
        choices=choices,
        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 LLM provider selected. Exiting...[/red]")
        exit(1)

    display_name, url = choice
    print(f"You selected: {display_name}\tURL: {url}")

    return display_name, url