"""
Report viewer component for displaying analysis results.
"""

from typing import Any, Dict, List, Optional, Union

import gradio as gr

from data.providers.base import DataProvider
from graph.state.trading_state import TechnicalWorkflowState
from graph.workflows.comprehensive_workflow import ComprehensiveState
from utils.cost_tracker import format_cost


def format_config_info(config: Dict[str, Any]) -> str:
    """
    Format configuration information for display.

    Args:
        config: Configuration dictionary

    Returns:
        Markdown formatted configuration info
    """
    if not config:
        return ""

    lines = []
    lines.append("### ⚙️ Analysis Configuration")
    lines.append("")

    # Indicator parameters
    if "indicator_parameters" in config:
        params = config["indicator_parameters"]
        lines.append("**Indicator Parameters:**")
        lines.append(f"- RSI Period: {params.get('rsi_period', 14)}")
        lines.append(
            f"- MACD: Fast={params.get('macd_fast', 12)}, Slow={params.get('macd_slow', 26)}, Signal={params.get('macd_signal', 9)}"
        )
        lines.append(
            f"- Stochastic: K={params.get('stoch_k_period', 14)}, D={params.get('stoch_d_period', 3)}"
        )
        lines.append("")

    # Data providers
    if "data_providers" in config:
        providers = config["data_providers"]
        lines.append("**Data Sources:**")
        lines.append(f"- OHLC: {providers.get('ohlc_primary', 'yfinance')}")
        lines.append(
            f"- Fundamentals: {providers.get('fundamentals_primary', 'alpha_vantage')}"
        )
        lines.append("")

    return "\n".join(lines)


def format_cost_summary(cost_summary: Optional[Dict[str, Any]]) -> str:
    """
    Format cost tracking summary for display.

    Args:
        cost_summary: Cost summary dictionary from CostTracker

    Returns:
        Markdown formatted cost summary
    """
    if not cost_summary:
        return ""

    lines = []
    lines.append("### 💰 Analysis Cost")
    lines.append("")

    total_cost = cost_summary.get("total_cost", 0.0)
    total_tokens = cost_summary.get("total_tokens", 0)
    call_count = cost_summary.get("call_count", 0)

    lines.append(f"**Total Cost:** {format_cost(total_cost)}")
    lines.append(f"**Total Tokens:** {total_tokens:,}")
    lines.append(f"**API Calls:** {call_count}")

    if call_count > 0:
        avg_cost = cost_summary.get("average_cost_per_call", 0.0)
        lines.append(f"**Average per Call:** {format_cost(avg_cost)}")

    # Show per-agent breakdown if available
    agent_costs = cost_summary.get("agent_costs", {})
    if agent_costs:
        lines.append("")
        lines.append("<details>")
        lines.append("<summary><b>Cost by Agent</b> (click to expand)</summary>")
        lines.append("")
        for agent_name, cost in sorted(
            agent_costs.items(), key=lambda x: x[1], reverse=True
        ):
            lines.append(f"- **{agent_name}:** {format_cost(cost)}")
        lines.append("")
        lines.append("</details>")

    lines.append("")
    lines.append("---")
    lines.append("")

    return "\n".join(lines)


def get_asset_guidance(ticker: str) -> str:
    """
    Get asset-specific guidance and warnings based on asset type.

    Args:
        ticker: Asset ticker symbol

    Returns:
        Markdown formatted guidance/warning string
    """
    asset_type = DataProvider.detect_asset_type(ticker)
    asset_characteristics = DataProvider.get_asset_characteristics(asset_type)

    guidance_parts = []

    if asset_type == "crypto":
        guidance_parts.append("## 🪙 Cryptocurrency Asset")
        guidance_parts.append("")
        guidance_parts.append("> **⚠️ HIGH VOLATILITY WARNING**")
        guidance_parts.append("> ")
        guidance_parts.append(
            "> - Cryptocurrencies are **extremely volatile** and can experience 10-20%+ swings in minutes"
        )
        guidance_parts.append(
            "> - Market operates **24/7 (365 days)** - no market close for risk management"
        )
        guidance_parts.append(
            "> - **Highly sentiment-driven** - news and social media have immediate impact"
        )
        guidance_parts.append(
            "> - **Regulatory risk** - government actions can cause instant severe moves"
        )
        guidance_parts.append(
            "> - **Liquidity varies** - wide spreads possible during volatility"
        )
        guidance_parts.append("> - Traditional fundamental analysis **does not apply**")
        guidance_parts.append("> ")
        guidance_parts.append(
            "> **Focus on:** Technical patterns, market sentiment, news, trading volume"
        )
        guidance_parts.append("")

    elif asset_type == "commodity":
        guidance_parts.append("## 🌾 Commodity Futures")
        guidance_parts.append("")
        guidance_parts.append("> **⚠️ FUTURES CONTRACT NOTICE**")
        guidance_parts.append("> ")
        guidance_parts.append(
            "> - Most commodity tickers represent **futures contracts** with expiration dates"
        )
        guidance_parts.append(
            "> - **Contract rollover** required - positions may need to be closed before expiry"
        )
        guidance_parts.append(
            "> - **Contango/backwardation** affects long-term holding costs"
        )
        guidance_parts.append(
            "> - **High leverage** inherent in futures - margin requirements apply"
        )
        guidance_parts.append(
            "> - **Gap risk** from overnight geopolitical/weather events"
        )
        guidance_parts.append("> - Traditional financial metrics **do not apply**")
        guidance_parts.append("> ")
        guidance_parts.append(
            "> **Focus on:** Supply/demand dynamics, inventory levels, weather, geopolitical events"
        )
        guidance_parts.append("")

    elif asset_type == "index":
        guidance_parts.append("## 📈 Market Index")
        guidance_parts.append("")
        guidance_parts.append("> **ℹ️ INDEX TRACKING NOTICE**")
        guidance_parts.append("> ")
        guidance_parts.append(
            "> - Indices **cannot be traded directly** - use ETFs, futures, or options"
        )
        guidance_parts.append(
            "> - Represents **weighted basket** of underlying securities"
        )
        guidance_parts.append(
            "> - **Sector composition** affects behavior - tech-heavy indices more volatile"
        )
        guidance_parts.append(
            "> - **Diversification** reduces single-stock risk but limits upside"
        )
        guidance_parts.append("> - Traditional company fundamentals **do not apply**")
        guidance_parts.append("> ")
        guidance_parts.append(
            "> **Focus on:** Macro trends, sector rotation, interest rates, earnings season"
        )
        guidance_parts.append("")

    elif asset_type == "forex":
        guidance_parts.append("## 💱 Foreign Exchange")
        guidance_parts.append("")
        guidance_parts.append("> **⚠️ CURRENCY PAIR NOTICE**")
        guidance_parts.append("> ")
        guidance_parts.append(
            "> - Forex markets are **highly leveraged** (50:1 to 500:1 typical)"
        )
        guidance_parts.append(
            "> - Market operates **24/5** (Sunday evening to Friday evening)"
        )
        guidance_parts.append(
            "> - **Macro-driven** - central bank policy, interest rates, geopolitical events"
        )
        guidance_parts.append(
            "> - **Bid-ask spreads** vary by broker and market conditions"
        )
        guidance_parts.append("> - Traditional equity fundamentals **do not apply**")
        guidance_parts.append("> ")
        guidance_parts.append(
            "> **Focus on:** Interest rate differentials, economic data, central bank policy"
        )
        guidance_parts.append("")

    elif asset_type == "stock":
        # For stocks, just add a brief note about traditional analysis
        guidance_parts.append("## 📊 Equity Stock")
        guidance_parts.append("")
        guidance_parts.append("> **ℹ️ Traditional equity analysis applies**")
        guidance_parts.append("> ")
        guidance_parts.append("> This is a publicly traded stock. Analysis includes:")
        guidance_parts.append("> - Financial fundamentals (earnings, revenue, margins)")
        guidance_parts.append("> - Technical indicators and chart patterns")
        guidance_parts.append("> - News and market sentiment")
        guidance_parts.append("> - Company-specific events and announcements")
        guidance_parts.append("")

    return "\n".join(guidance_parts)


def create_timeframe_alignment_indicator(
    alignment_score: float, trend_direction: str = "unknown"
) -> str:
    """
    Create a visual indicator for timeframe alignment.

    Args:
        alignment_score: Alignment score (0-1)
        trend_direction: Overall trend direction (bullish, bearish, mixed)

    Returns:
        Visual indicator string with emoji and description
    """
    # Determine alignment strength
    if alignment_score >= 0.75:
        strength = "Strong"
        emoji = "🟢🟢🟢"
    elif alignment_score >= 0.5:
        strength = "Moderate"
        emoji = "🟢🟢"
    elif alignment_score >= 0.25:
        strength = "Weak"
        emoji = "🟡"
    else:
        strength = "Conflicting"
        emoji = "🔴"

    # Add trend direction
    if trend_direction.lower() == "bullish":
        direction_emoji = "📈"
    elif trend_direction.lower() == "bearish":
        direction_emoji = "📉"
    else:
        direction_emoji = "↔️"

    return f"{emoji} {direction_emoji} {strength} Alignment ({alignment_score:.0%})"


def format_timeframe_context(timeframe_context: Dict[str, Any]) -> str:
    """
    Format timeframe context information for display.

    Args:
        timeframe_context: Timeframe context dict

    Returns:
        Formatted string
    """
    if not timeframe_context:
        return ""

    label = timeframe_context.get("label", "unknown")
    scope = timeframe_context.get("scope", "trading")
    hold_duration = timeframe_context.get("hold_duration", "varies")
    weight = timeframe_context.get("weight", 0.5)

    return f"**Timeframe:** {label} | **Scope:** {scope.title()} | **Expected Hold:** {hold_duration} | **Weight:** {weight:.1f}"


def create_report_viewer() -> gr.Markdown:
    """
    Create report viewer component.

    Returns:
        Gradio Markdown component for displaying reports
    """
    return gr.Markdown(
        label="Analysis Report",
        value="Analysis report will appear here...",
    )


def format_error_report(error_message: str) -> str:
    """
    Format error message as report.

    Args:
        error_message: Error message

    Returns:
        Markdown formatted error report
    """
    return f"""# ⚠️ Analysis Error

An error occurred during analysis:

```
{error_message}
```

Please check:
- Ticker symbol is valid
- Internet connection is working
- API keys are configured (if using Alpha Vantage)
- Try a different timeframe
"""


def format_progress_message(current_agent: str) -> str:
    """
    Format progress message during analysis.

    Args:
        current_agent: Currently executing agent

    Returns:
        Progress message
    """
    agent_display = current_agent.replace("_", " ").title()

    return f"""# 🔄 Analysis in Progress

Currently running: **{agent_display}**

Please wait while the multi-agent system analyzes the market data...

**Workflow Steps:**
1. ✅ Fetch Market Data
2. 🔄 Indicator Agent - Calculate technical indicators
3. ⏳ Pattern Agent - Identify chart patterns
4. ⏳ Trend Agent - Analyze trends
5. ⏳ Decision Agent - Generate recommendation
6. ⏳ Generate Charts
"""