"""
Medium MCP Server v3.0

A comprehensive MCP server for Medium article scraping with:
- Full MCP specification compliance (annotations, progress, logging)
- 12 Tools, 4 Resources, 9 Prompts
- ElevenLabs Creator API for premium audio
- HTTP transport support for remote deployment
"""

import sys
import os
import asyncio
import uuid
from typing import List, Dict, Any, Optional
from contextlib import asynccontextmanager

# No sys.path needed - src/ is in same project now

from mcp.server.fastmcp import FastMCP, Context, Image
import httpx

# Local imports
from src.config import MCPConfig, ELEVENLABS_CHAR_LIMITS, ELEVENLABS_OUTPUT_FORMATS
from elevenlabs_voices import ELEVENLABS_VOICES, get_voice_id, get_voices_info, VOICE_CATEGORIES

# Medium-Scraper imports
from src.service import ScraperService
from src.html_renderer import render_article_html, render_full_page

# LLM imports
from groq import Groq


# ============================================================================
# LIFESPAN MANAGEMENT
# ============================================================================

class AppContext:
    """Application-wide resources managed by lifespan."""
    def __init__(self, scraper: ScraperService, config: MCPConfig, elevenlabs):
        self.scraper = scraper
        self.config = config
        self.elevenlabs = elevenlabs


@asynccontextmanager
async def app_lifespan(server: FastMCP):
    """Manage scraper and API clients lifecycle."""
    global _app_context
    
    config = MCPConfig.from_env()
    scraper = ScraperService(max_workers=config.max_workers)
    
    # Check if ElevenLabs is available
    elevenlabs_available = bool(os.environ.get("ELEVENLABS_API_KEY"))
    if elevenlabs_available:
        print("[INFO] ElevenLabs API key found")
    else:
        print("[WARN] ELEVENLABS_API_KEY not set, TTS will use fallbacks")
    
    try:
        await scraper.ensure_initialized()
        app_ctx = AppContext(scraper=scraper, config=config, elevenlabs=elevenlabs_available)
        _app_context = app_ctx  # Set module-level reference for resources
        print("[INFO] Medium MCP Server v2.0 initialized")
        yield app_ctx
    finally:
        _app_context = None
        await scraper.close()
        print("[INFO] Medium MCP Server shutdown complete")


# Initialize FastMCP with lifespan and instructions
mcp = FastMCP(
    "Medium Scraper v3",
    lifespan=app_lifespan,
    instructions="""This MCP server provides comprehensive access to Medium articles.

**Key Capabilities:**
- Scrape any Medium article (35+ domains supported including TowardsDataScience)
- Search and discover trending content by topic or tag
- Generate audio podcasts from articles using ElevenLabs TTS
- Synthesize research reports using AI (Gemini/OpenAI)
- Export to markdown, HTML, or JSON

**Recommended Workflow:**
1. Use `medium_search(topic)` or `medium_fresh(tag)` to find articles
2. Use `medium_scrape(url)` to get full article content
3. Use `medium_synthesize(topic)` for AI-powered topic analysis
4. Use `medium_cast(url)` to generate audio versions

**Resources available:** Trending articles, tag feeds, search results
**Prompts available:** Article summarization, social media posts, research reports
"""
)

# Module-level reference for resources (set during lifespan)
_app_context: Optional[AppContext] = None


# ============================================================================
# HELPER FUNCTIONS
# ============================================================================

def get_app_context(ctx: Context) -> AppContext:
    """Get application context from request context."""
    return ctx.request_context.lifespan_context


def truncate_for_model(text: str, model: str) -> str:
    """Truncate text to model's character limit."""
    max_chars = ELEVENLABS_CHAR_LIMITS.get(model, 10000)
    if len(text) > max_chars:
        return text[:max_chars - 50] + "\n\n... End of audio preview."
    return text


def handle_paywall(article: Dict) -> Dict:
    """Add paywall warning if content appears truncated."""
    if not article:
        return {"error": "No article data"}
    
    content = article.get("markdownContent", "")
    is_locked = article.get("isLocked", False)
    
    if is_locked or (content and len(content) < 500):
        article["_paywall_warning"] = "Content may be behind a paywall"
    
    return article


# ============================================================================
# RESOURCES (Structured JSON responses)
# Note: MCP resources have different signature requirements than tools.
# We use a module-level reference that gets set during lifespan.
# ============================================================================

import json


@mcp.resource(
    "medium://trending",
    name="Trending Articles",
    description="Top trending Medium articles updated hourly",
    mime_type="application/json"
)
async def get_trending() -> str:
    """Returns trending articles as JSON string."""
    if not _app_context:
        return '{"error": "Server not initialized"}'
    results = await _app_context.scraper.scrape_tag("trending", max_articles=10)
    return json.dumps([
        {
            "title": r.get("title"),
            "url": r.get("url"),
            "author": r.get("author", {}).get("name") if isinstance(r.get("author"), dict) else r.get("author"),
            "readingTime": r.get("readingTime"),
        }
        for r in results
    ], ensure_ascii=False)


@mcp.resource(
    "medium://tag/{tag}",
    name="Tag Feed",
    description="Latest articles for a specific topic tag",
    mime_type="application/json"
)
async def get_tag_feed(tag: str) -> str:
    """Returns articles for a specific tag as JSON string."""
    if not _app_context:
        return '{"error": "Server not initialized"}'
    results = await _app_context.scraper.scrape_tag(tag, max_articles=10)
    return json.dumps([
        {
            "title": r.get("title"),
            "url": r.get("url"),
            "author": r.get("author", {}).get("name") if isinstance(r.get("author"), dict) else r.get("author"),
            "readingTime": r.get("readingTime"),
        }
        for r in results
    ], ensure_ascii=False)


@mcp.resource(
    "medium://search/{query}",
    name="Search Results",
    description="Search Medium for articles matching a query",
    mime_type="application/json"
)
async def get_search_results(query: str) -> str:
    """Returns search results as JSON string."""
    if not _app_context:
        return '{"error": "Server not initialized"}'
    results = await _app_context.scraper.scrape_search(query, max_articles=10)
    return json.dumps([
        {
            "title": r.get("title"),
            "url": r.get("url"),
            "author": r.get("author", {}).get("name") if isinstance(r.get("author"), dict) else r.get("author"),
            "preview": (r.get("subtitle") or r.get("description", ""))[:200],
        }
        for r in results
    ], ensure_ascii=False)


@mcp.resource(
    "medium://stats",
    name="Server Statistics",
    description="Current server stats and capabilities",
    mime_type="application/json"
)
async def get_server_stats() -> str:
    """Returns server statistics and capabilities."""
    return json.dumps({
        "version": "3.0",
        "capabilities": {
            "tools": 10,
            "resources": 4,
            "prompts": 8,
            "features": [
                "article_scraping",
                "batch_processing", 
                "audio_generation",
                "ai_synthesis",
                "progress_notifications",
                "mcp_logging"
            ]
        },
        "supported_domains": [
            "medium.com",
            "towardsdatascience.com",
            "levelup.gitconnected.com",
            "betterprogramming.pub",
            "javascript.plainenglish.io",
            "35+ total domains"
        ],
        "tts_providers": ["elevenlabs", "edge-tts", "openai"],
        "ai_providers": ["groq", "gemini", "openai"]
    }, ensure_ascii=False)


# ============================================================================
# TOOLS - Core Scraping
# ============================================================================

@mcp.tool(annotations={
    "title": "Scrape Medium Article",
    "readOnlyHint": True,
    "openWorldHint": True,
    "idempotentHint": True
})
async def medium_scrape(
    url: str,
    output_format: str = "both",
    force_refresh: bool = False,
    enable_enhancements: bool = False,
    ctx: Context = None
) -> Dict[str, Any]:
    """
    Scrape a Medium article with full v3.0 capabilities.
    
    Args:
        url: Medium article URL (supports 35+ domains including towardsdatascience.com)
        output_format: "markdown", "html", or "both" (default: both)
        force_refresh: Bypass cache and re-scrape (default: false)
        enable_enhancements: Enable KG extraction, embeddings (adds ~15s, default: false)
    
    Returns:
        Article with title, author, content, tags, and metadata
    """
    app = get_app_context(ctx)
    
    article = await app.scraper.scrape_article(
        url,
        force_refresh=force_refresh,
        enable_enhancements=enable_enhancements
    )
    
    if not article or article.get("error"):
        return article or {"error": "Failed to scrape article", "url": url}
    
    # Add HTML if requested
    if output_format in ["html", "both"]:
        try:
            article["htmlContent"] = render_article_html(article)
        except Exception as e:
            article["htmlContent"] = f"<p>Error rendering HTML: {e}</p>"
    
    # Remove markdown if only HTML requested
    if output_format == "html":
        article.pop("markdownContent", None)
    
    return handle_paywall(article)


@mcp.tool(annotations={
    "title": "Batch Scrape Articles",
    "readOnlyHint": True,
    "openWorldHint": True,
    "idempotentHint": True
})
async def medium_batch(
    urls: List[str],
    max_concurrency: int = 5,
    output_format: str = "both",
    ctx: Context = None
) -> Dict[str, Any]:
    """
    Scrape multiple Medium articles in parallel.
    
    Args:
        urls: List of Medium article URLs (max 20)
        max_concurrency: Number of parallel workers (1-10, default: 5)
        output_format: Output format for all articles (default: both)
    
    Returns:
        {success: [...], failed: [...], stats: {total, success, failed}}
    """
    app = get_app_context(ctx)
    
    if len(urls) > app.config.max_batch_size:
        return {"error": f"Maximum batch size is {app.config.max_batch_size} URLs"}
    
    max_concurrency = min(max(1, max_concurrency), 10)
    semaphore = asyncio.Semaphore(max_concurrency)
    
    success = []
    failed = []
    
    async def scrape_one(url: str, index: int):
        async with semaphore:
            try:
                article = await app.scraper.scrape_article(url)
                if article and not article.get("error"):
                    if output_format in ["html", "both"]:
                        article["htmlContent"] = render_article_html(article)
                    if output_format == "html":
                        article.pop("markdownContent", None)
                    success.append(article)
                else:
                    failed.append({"url": url, "error": article.get("error", "Unknown error")})
            except Exception as e:
                failed.append({"url": url, "error": str(e)})
            finally:
                # Report progress after each URL is processed
                if ctx:
                    await ctx.report_progress(
                        progress=len(success) + len(failed),
                        total=len(urls)
                    )
    
    await asyncio.gather(*[scrape_one(url, i) for i, url in enumerate(urls)])
    
    return {
        "success": success,
        "failed": failed,
        "stats": {
            "total": len(urls),
            "success": len(success),
            "failed": len(failed)
        }
    }


@mcp.tool(annotations={
    "title": "Search Medium",
    "readOnlyHint": True,
    "openWorldHint": True,
    "idempotentHint": False
})
async def medium_search(query: str, max_articles: int = 10, ctx: Context = None) -> List[Dict[str, Any]]:
    """
    Search Medium for articles.
    
    Args:
        query: Search query (e.g., "AI Agents", "Python Asyncio")
        max_articles: Maximum articles to return (default: 10)
    
    Returns:
        List of article previews with title, url, author
    """
    app = get_app_context(ctx)
    results = await app.scraper.scrape_search(query, max_articles=max_articles)
    return results


@mcp.tool(annotations={
    "title": "Get Fresh Articles by Tag",
    "readOnlyHint": True,
    "openWorldHint": True,
    "idempotentHint": False
})
async def medium_fresh(tag: str, max_articles: int = 10, ctx: Context = None) -> List[Dict[str, Any]]:
    """
    Get the latest articles for a specific tag.
    
    Args:
        tag: Topic tag (e.g., "artificial-intelligence", "python")
        max_articles: Maximum articles to return (default: 10)
    
    Returns:
        List of article previews
    """
    app = get_app_context(ctx)
    results = await app.scraper.scrape_tag(tag, max_articles=max_articles)
    return results


@mcp.tool(annotations={
    "title": "Render Article as HTML",
    "readOnlyHint": True,
    "openWorldHint": True,
    "idempotentHint": True
})
async def medium_render_html(url: str, standalone: bool = False, ctx: Context = None) -> str:
    """
    Render a Medium article as beautiful HTML.
    
    Args:
        url: Medium article URL
        standalone: If True, returns complete HTML page with <html>, <head>, etc.
    
    Returns:
        HTML string with Tailwind CSS styling
    """
    app = get_app_context(ctx)
    article = await app.scraper.scrape_article(url)
    
    if not article or article.get("error"):
        return f"<div class='error'>Failed to scrape: {article.get('error', 'Unknown')}</div>"
    
    if standalone:
        return render_full_page(article)
    else:
        return render_article_html(article)


@mcp.tool(annotations={
    "title": "Export Article",
    "readOnlyHint": True,
    "openWorldHint": True,
    "idempotentHint": True
})
async def medium_export(
    url: str,
    format: str = "markdown",
    ctx: Context = None
) -> Dict[str, Any]:
    """
    Export a Medium article to various formats.
    
    Args:
        url: Medium article URL
        format: Export format - "markdown", "html", "json"
    
    Returns:
        {content: ..., format: ..., title: ...}
    """
    app = get_app_context(ctx)
    article = await app.scraper.scrape_article(url)
    
    if not article or article.get("error"):
        return {"error": article.get("error", "Failed to scrape")}
    
    title = article.get("title", "article")
    
    if format == "markdown":
        return {
            "content": article.get("markdownContent", ""),
            "format": "markdown",
            "title": title
        }
    
    elif format == "html":
        html = render_full_page(article)
        return {
            "content": html,
            "format": "html",
            "title": title
        }
    
    elif format == "json":
        return {
            "content": article,
            "format": "json",
            "title": title
        }
    
    else:
        return {"error": f"Unsupported format: {format}. Use: markdown, html, json"}


# ============================================================================
# TOOLS - Audio (ElevenLabs)
# ============================================================================

@mcp.tool(annotations={
    "title": "Generate Audio Podcast",
    "readOnlyHint": False,
    "destructiveHint": False,
    "openWorldHint": True,
    "idempotentHint": False
})
async def medium_cast(
    url: str,
    voice: str = "george",
    model: str = "eleven_multilingual_v2",
    quality: str = "premium",
    summarize: str = "auto",
    max_chars: int = 250,
    ctx: Context = None
) -> Dict[str, Any]:
    """
    Convert a Medium article into premium audio podcast using ElevenLabs.
    
    Args:
        url: Medium article URL
        voice: Voice name or ID. Popular: "george" (British), "adam" (American), 
               "rachel" (calm female), "brian" (narrator), "alice" (British female)
        model: TTS model - "eleven_multilingual_v2" (10k chars, recommended),
               "eleven_flash_v2_5" (40k, fastest), "eleven_turbo_v2_5" (40k, balanced)
        quality: "standard", "high", or "premium" (Creator tier)
        summarize: "auto" (summarize if > max_chars), "always", or "none"
        max_chars: Target character limit for summarization (default: 250)
    
    Returns:
        {audio_path, title, voice, model, duration_estimate, provider}
    """
    app = get_app_context(ctx)
    
    # Scrape article (Phase 1)
    if ctx:
        await ctx.report_progress(progress=1, total=3)
    article = await app.scraper.scrape_article(url)
    if not article or not article.get("markdownContent"):
        return {"error": "Failed to scrape article or no content", "url": url}
    
    text = article["markdownContent"]
    title = article.get("title", "article")
    original_length = len(text)
    
    # Summarization logic
    should_summarize = (
        summarize == "always" or 
        (summarize == "auto" and len(text) > max_chars)
    )
    
    if should_summarize and summarize != "none":
        groq_key = os.environ.get("GROQ_API_KEY")
        gemini_key = os.environ.get("GEMINI_API_KEY")
        summarize_success = False
        
        prompt = f"""You are creating a quick audio summary for busy professionals. In EXACTLY {max_chars} characters or less, give the ONE most valuable insight or actionable takeaway from this article.

Format: Start with the key insight, then briefly explain why it matters.
Style: Conversational, engaging, like a smart friend sharing a tip.
Goal: The listener should feel they learned something useful in 15 seconds.

Article Title: "{title}"

Article Content:
{text[:8000]}

Your {max_chars}-character summary (make every word count):"""

        # Try Groq first (PRIMARY - fastest)
        if groq_key and not summarize_success:
            try:
                client = Groq(api_key=groq_key)
                response = client.chat.completions.create(
                    model="llama-3.1-8b-instant",  # Fast model for summarization
                    messages=[{"role": "user", "content": prompt}],
                    max_tokens=500,
                    temperature=0.7
                )
                text = response.choices[0].message.content.strip()[:max_chars]
                summarize_success = True
                if ctx:
                    await ctx.info(f"Summarized with Groq: {original_length} -> {len(text)} chars")
            except Exception as e:
                if ctx:
                    await ctx.warning(f"Groq failed: {e}, trying Gemini...")
        
        # Fallback to Gemini (BACKUP) - Using new google.genai SDK
        if gemini_key and not summarize_success:
            try:
                from google import genai
                client = genai.Client(api_key=gemini_key)
                
                response = client.models.generate_content(
                    model='gemini-2.0-flash-exp',
                    contents=prompt
                )
                text = response.text.strip()[:max_chars]
                summarize_success = True
                if ctx:
                    await ctx.info(f"Summarized with Gemini: {original_length} -> {len(text)} chars")
            except Exception as e:
                if ctx:
                    await ctx.warning(f"Gemini also failed: {e}, using truncation")
        
        # Final fallback: truncation
        if not summarize_success:
            text = text[:max_chars]
    else:
        # Just truncate to model limit
        text = truncate_for_model(text, model)
    
    # Resolve voice ID
    voice_id = get_voice_id(voice)
    
    # Output format
    output_format = ELEVENLABS_OUTPUT_FORMATS.get(quality, "mp3_44100_192")
    
    # Output path
    outputs_dir = app.config.audio_output_dir
    os.makedirs(outputs_dir, exist_ok=True)
    safe_title = "".join(c if c.isalnum() else "_" for c in title)[:40]
    output_path = os.path.join(outputs_dir, f"{safe_title}_{voice}_{uuid.uuid4().hex[:6]}.mp3")
    
    # Try ElevenLabs (PRIMARY)
    elevenlabs_key = os.environ.get("ELEVENLABS_API_KEY")
    if elevenlabs_key:
        try:
            from elevenlabs.client import ElevenLabs
            
            def _generate_audio():
                client = ElevenLabs(api_key=elevenlabs_key)
                audio = client.text_to_speech.convert(
                    text=text,
                    voice_id=voice_id,
                    model_id=model,
                    output_format=output_format,
                )
                with open(output_path, "wb") as f:
                    for chunk in audio:
                        f.write(chunk)
            
            await asyncio.to_thread(_generate_audio)
            
            if os.path.exists(output_path) and os.path.getsize(output_path) > 0:
                return {
                    "audio_path": os.path.abspath(output_path),
                    "title": title,
                    "voice": voice,
                    "voice_id": voice_id,
                    "model": model,
                    "quality": quality,
                    "duration_estimate": f"{len(text) // 150} min",
                    "characters_used": len(text),
                    "provider": "elevenlabs"
                }
        except Exception as e:
            if ctx:
                await ctx.warning(f"ElevenLabs failed: {e}, trying fallback...")
    
    # Fallback: Edge-TTS (Free)
    try:
        import edge_tts
        text_truncated = text[:4000]  # Edge-TTS limit
        communicate = edge_tts.Communicate(text_truncated, "en-US-ChristopherNeural")
        await communicate.save(output_path)
        
        if os.path.exists(output_path) and os.path.getsize(output_path) > 0:
            return {
                "audio_path": os.path.abspath(output_path),
                "title": title,
                "voice": "en-US-ChristopherNeural",
                "duration_estimate": f"{len(text_truncated) // 150} min",
                "characters_used": len(text_truncated),
                "provider": "edge-tts",
                "note": "Free fallback, limited to 4000 chars"
            }
    except Exception as e:
        if ctx:
            await ctx.warning(f"Edge-TTS failed: {e}")
    
    # Fallback: OpenAI TTS
    openai_key = os.environ.get("OPENAI_API_KEY")
    if openai_key:
        try:
            from openai import AsyncOpenAI
            client = AsyncOpenAI(api_key=openai_key)
            response = await client.audio.speech.create(
                model="tts-1-hd" if quality == "premium" else "tts-1",
                voice="onyx",
                input=text[:4096]
            )
            response.stream_to_file(output_path)
            
            return {
                "audio_path": os.path.abspath(output_path),
                "title": title,
                "voice": "onyx",
                "duration_estimate": f"{min(len(text), 4096) // 150} min",
                "provider": "openai"
            }
        except Exception as e:
            print(f"[WARN] OpenAI TTS failed: {e}")
    
    return {"error": "All TTS providers failed", "url": url}


@mcp.tool(annotations={
    "title": "List Available Voices",
    "readOnlyHint": True,
    "openWorldHint": True,
    "idempotentHint": True
})
async def medium_voices(ctx: Context = None) -> Dict[str, Any]:
    """
    List available ElevenLabs voices for medium_cast.
    
    Returns:
        Voice categories, recommendations, and model info
    """
    app = get_app_context(ctx)
    
    # Try to fetch live voices
    live_voices = []
    if app.elevenlabs:
        try:
            result = await app.elevenlabs.voices.search()
            live_voices = [
                {"name": v.name, "id": v.voice_id, "category": getattr(v, 'category', 'unknown')}
                for v in result.voices[:20]
            ]
        except Exception:
            pass
    
    return {
        "recommended": {
            "george": {"id": "JBFqnCBsd6RMkjVDRZzb", "desc": "British, warm narrator (DEFAULT)"},
            "adam": {"id": "pNInz6obpgDQGcFmaJgB", "desc": "American, deep narrator"},
            "rachel": {"id": "21m00Tcm4TlvDq8ikWAM", "desc": "American, calm female"},
            "brian": {"id": "nPczCjzI2devNBz1zQrb", "desc": "American, narrator"},
            "alice": {"id": "Xb7hH8MSUJpSbSDYk0k2", "desc": "British, confident female"},
        },
        "categories": VOICE_CATEGORIES,
        "models": {
            "eleven_multilingual_v2": "Recommended, 10k chars, 29 languages",
            "eleven_flash_v2_5": "Fastest (~75ms), 40k chars, 32 languages",
            "eleven_turbo_v2_5": "Balanced, 40k chars, 32 languages",
            "eleven_v3": "Most expressive, 5k chars, 70+ languages",
        },
        "quality_options": ELEVENLABS_OUTPUT_FORMATS,
        "live_voices": live_voices,
        "total_premade_voices": len(ELEVENLABS_VOICES),
    }


# ============================================================================
# TOOLS - Synthesis
# ============================================================================

@mcp.tool(annotations={
    "title": "Synthesize Research Report",
    "readOnlyHint": True,
    "openWorldHint": True,
    "idempotentHint": False
})
async def medium_synthesize(topic: str, max_articles: int = 5, ctx: Context = None) -> str:
    """
    Synthesize a 'State of the Union' report on a topic using top Medium articles.
    
    Args:
        topic: Topic to analyze (e.g., "Generative AI", "Web Development 2024")
        max_articles: Number of articles to analyze (default: 5)
    
    Returns:
        Synthesized research report
    """
    app = get_app_context(ctx)
    
    groq_key = os.environ.get("GROQ_API_KEY")
    gemini_key = os.environ.get("GEMINI_API_KEY")
    openai_key = os.environ.get("OPENAI_API_KEY")
    
    if not groq_key and not gemini_key and not openai_key:
        return "Error: No AI API keys set (GROQ_API_KEY, GEMINI_API_KEY, or OPENAI_API_KEY)."
    
    # Scrape articles
    if ctx:
        await ctx.report_progress(progress=1, total=3)  # Phase 1: Search
    articles = await app.scraper.scrape_search(topic, max_articles=max_articles)
    if not articles:
        return "No articles found to synthesize."
    
    # Prepare context
    async def get_article_content(art):
        url = art.get('url')
        title = art.get('title', 'Untitled')
        author = art.get('author', {}).get('name') if isinstance(art.get('author'), dict) else art.get('author', 'Unknown')
        
        try:
            full_art = await app.scraper.scrape_article(url)
            content = full_art.get("markdownContent", "")[:2000]
        except:
            content = f"(Content unavailable)"
        
        return f"\nTitle: {title}\nAuthor: {author}\nURL: {url}\nContent:\n{content}\n"
    
    results = await asyncio.gather(*[get_article_content(art) for art in articles])
    context_text = "".join(results)
    if ctx:
        await ctx.report_progress(progress=2, total=3)  # Phase 2: Scraped articles
    
    prompt = f"""You are a tech analyst. Synthesize the following Medium articles into a 'State of the Union' report.
    
Topic: {topic}

Structure your report:
1. Executive Summary (2-3 sentences)
2. Key Trends
3. Notable Insights
4. Contrarian Views (if any)
5. Recommended Reading

Articles:
{context_text}
"""
    
    # Try Groq first (PRIMARY - fastest)
    if groq_key:
        try:
            client = Groq(api_key=groq_key)
            response = client.chat.completions.create(
                model="llama-3.3-70b-versatile",  # Best model for synthesis
                messages=[{"role": "user", "content": prompt}],
                max_tokens=2000,
                temperature=0.7
            )
            return response.choices[0].message.content
        except Exception as e:
            if ctx:
                await ctx.warning(f"Groq failed: {e}")
    
    # Fallback to Gemini - Using new google.genai SDK
    if gemini_key:
        try:
            from google import genai
            client = genai.Client(api_key=gemini_key)
            response = client.models.generate_content(
                model='gemini-2.0-flash-exp',
                contents=prompt
            )
            return response.text
        except Exception as e:
            if ctx:
                await ctx.warning(f"Gemini failed: {e}")
    
    # Fallback to OpenAI
    if openai_key:
        try:
            from openai import AsyncOpenAI
            client = AsyncOpenAI(api_key=openai_key)
            response = await client.chat.completions.create(
                model="gpt-4o",
                messages=[{"role": "user", "content": prompt}]
            )
            return response.choices[0].message.content
        except Exception as e:
            return f"Error: All providers failed. Last error: {e}"
    
    return "Error: No AI service available."


# ============================================================================
# TOOLS - Utility
# ============================================================================

@mcp.tool(annotations={
    "title": "Fetch Image Thumbnail",
    "readOnlyHint": True,
    "openWorldHint": True,
    "idempotentHint": True
})
async def get_thumbnail(image_url: str) -> Image:
    """
    Fetch an image from a URL and return it as an MCP Image.
    
    Args:
        image_url: The URL of the image to fetch
    
    Returns:
        Image object for display
    """
    async with httpx.AsyncClient() as client:
        response = await client.get(image_url)
        response.raise_for_status()
        return Image(data=response.content, format="png")


@mcp.tool(annotations={
    "title": "Find Related Articles",
    "readOnlyHint": True,
    "openWorldHint": True,
    "idempotentHint": False
})
async def medium_related(
    url: str,
    max_articles: int = 5,
    ctx: Context = None
) -> List[Dict[str, Any]]:
    """
    Find articles related to a given Medium article.
    
    Args:
        url: URL of the source article
        max_articles: Maximum related articles to return (default: 5)
    
    Returns:
        List of related articles with similarity scores
    """
    app = get_app_context(ctx)
    
    # Scrape the source article to get its tags and topic
    article = await app.scraper.scrape_article(url)
    if not article or article.get("error"):
        return [{"error": "Failed to scrape source article"}]
    
    # Get tags from the article
    tags = article.get("tags", [])
    if not tags:
        # Try to infer from title
        title = article.get("title", "")
        tags = [title.split()[0]] if title else ["technology"]
    
    # Search for related articles using the first tag
    primary_tag = tags[0] if isinstance(tags, list) and tags else "technology"
    related = await app.scraper.scrape_tag(primary_tag, max_articles=max_articles + 2)
    
    # Filter out the source article
    source_url = url.rstrip("/")
    related = [r for r in related if r.get("url", "").rstrip("/") != source_url][:max_articles]
    
    return [{
        "title": r.get("title"),
        "url": r.get("url"),
        "author": r.get("author", {}).get("name") if isinstance(r.get("author"), dict) else r.get("author"),
        "readingTime": r.get("readingTime"),
        "relevance": "tag_match"
    } for r in related]


@mcp.tool(annotations={
    "title": "Get Personalized Recommendations",
    "readOnlyHint": True,
    "openWorldHint": True,
    "idempotentHint": False
})
async def medium_recommend(
    interests: List[str],
    reading_time: int = 30,
    ctx: Context = None
) -> Dict[str, Any]:
    """
    Get personalized article recommendations based on interests.
    
    Args:
        interests: List of topics you're interested in (e.g., ["AI", "Python", "startups"])
        reading_time: Target total reading time in minutes (default: 30)
    
    Returns:
        Curated reading list with estimated total time
    """
    app = get_app_context(ctx)
    
    all_articles = []
    for interest in interests[:3]:  # Limit to 3 interests
        articles = await app.scraper.scrape_search(interest, max_articles=5)
        for art in articles:
            art["interest"] = interest
        all_articles.extend(articles)
    
    # Deduplicate by URL
    seen_urls = set()
    unique_articles = []
    for art in all_articles:
        url = art.get("url", "")
        if url not in seen_urls:
            seen_urls.add(url)
            unique_articles.append(art)
    
    # Estimate reading times and filter
    reading_list = []
    total_time = 0
    for art in unique_articles:
        est_time = art.get("readingTime", 5)
        if isinstance(est_time, str):
            est_time = int(est_time.split()[0]) if est_time.split()[0].isdigit() else 5
        
        if total_time + est_time <= reading_time:
            reading_list.append({
                "title": art.get("title"),
                "url": art.get("url"),
                "author": art.get("author", {}).get("name") if isinstance(art.get("author"), dict) else art.get("author"),
                "readingTime": est_time,
                "interest": art.get("interest")
            })
            total_time += est_time
    
    return {
        "reading_list": reading_list,
        "total_articles": len(reading_list),
        "total_reading_time": total_time,
        "interests_covered": list(set(a.get("interest") for a in reading_list))
    }


# ============================================================================
# PROMPTS
# ============================================================================

@mcp.prompt()
def summarize_article(url: str) -> str:
    """Create a prompt to summarize a Medium article."""
    return f"""Read and summarize this Medium article: {url}

Structure your summary:
1. **Main Thesis**: One sentence summary
2. **Key Points**: 3-5 bullet points
3. **Novel Insights**: What's new or surprising
4. **Actionable Takeaways**: What can the reader do"""


@mcp.prompt()
def tweet_thread(url: str) -> str:
    """Create a prompt to turn an article into a Twitter thread."""
    return f"""Convert this article into a viral Twitter thread: {url}

Guidelines:
- 5-7 tweets maximum
- First tweet must be a hook
- Use emojis strategically (not excessively)
- End with a call to action
- Include relevant hashtags in final tweet"""


@mcp.prompt()
def linkedin_post(url: str) -> str:
    """Create a prompt to turn an article into a LinkedIn post."""
    return f"""Transform this article into an engaging LinkedIn post: {url}

Guidelines:
- Start with a hook (question or bold statement)
- Keep it under 1300 characters
- Use line breaks for readability
- Include 3-5 relevant hashtags at the end
- End with a question to drive engagement"""


@mcp.prompt()
def newsletter(topic: str, article_count: int = 5) -> str:
    """Create a prompt for a newsletter digest on a topic."""
    return f"""Create a newsletter digest on "{topic}" using the top {article_count} Medium articles.

Format:
- Catchy subject line
- Brief intro paragraph (2-3 sentences)
- For each article:
  • Title with brief summary
  • Why it matters
- Closing with call to action"""


@mcp.prompt()
def research_report(topic: str) -> str:
    """Create a prompt for a comprehensive research report."""
    return f"""Create a comprehensive research report on "{topic}" using Medium articles.

Structure:
1. **Executive Summary** (2-3 sentences)
2. **Current Trends** (What's hot in this space)
3. **Key Players** (Who's writing about this)
4. **Diverse Perspectives** (Different viewpoints)
5. **Future Outlook** (Predictions)
6. **Recommended Reading** (Top 3 articles with links)"""


@mcp.prompt()
def code_tutorial(url: str) -> str:
    """Create a prompt to extract a code tutorial from an article."""
    return f"""Extract and structure the code tutorial from this article: {url}

Format:
1. **Prerequisites**: What you need installed/configured
2. **Step-by-Step**:
   - Step 1: [Description + Code]
   - Step 2: [Description + Code]
   - ...
3. **Complete Code**: Full working example
4. **Common Issues**: Troubleshooting tips"""


@mcp.prompt()
def analyze_trending(focus: str = "technology") -> str:
    """Create a prompt to analyze trending Medium articles with a specific focus."""
    return f"""Analyze the current trending articles on Medium with a focus on "{focus}".

WORKFLOW:
1. First, use the `medium://trending` resource to get current trending articles
2. Select 3-5 articles most relevant to "{focus}"
3. Use `medium_scrape()` on each selected article

ANALYSIS STRUCTURE:
1. **Trend Overview**: What themes are dominating?
2. **Key Insights**: Most valuable takeaways from each article
3. **Emerging Patterns**: What's changing in this space?
4. **Contrarian Views**: Any articles going against the grain?
5. **Recommendations**: Top 2-3 must-reads with reasons

Focus area: {focus}
Be specific and cite the articles you analyze."""


@mcp.prompt()
def deep_research(topic: str, depth: str = "comprehensive") -> str:
    """Create a structured multi-step research workflow prompt."""
    return f"""Conduct a {depth} research analysis on "{topic}" using Medium articles.

PHASE 1 - DISCOVERY:
1. Use `medium_search("{topic}")` to find relevant articles
2. Use `medium_fresh("{topic.replace(' ', '-')}")` for latest content
3. Note the top 5 most relevant articles

PHASE 2 - DEEP ANALYSIS:
4. Use `medium_scrape()` on each selected article
5. Extract: main arguments, evidence, unique perspectives
6. Note any contradictions between articles

PHASE 3 - SYNTHESIS:
7. Use `medium_synthesize("{topic}")` for AI-powered summary
8. Cross-reference with your own analysis
9. Identify gaps in coverage

OUTPUT FORMAT:
# Research Report: {topic}

## Executive Summary
[2-3 sentences]

## Key Findings
[Bullet points with citations]

## Diverse Perspectives
[Different viewpoints from articles]

## Emerging Trends
[What's changing?]

## Knowledge Gaps
[What's missing from the discourse?]

## Recommended Reading
[Top 3 articles with reasons]

## Sources
[Full list of analyzed articles]

Research depth: {depth}
Topic: {topic}"""


@mcp.prompt()
def content_repurpose(url: str, platforms: str = "all") -> str:
    """Create a prompt to repurpose an article for multiple platforms."""
    return f"""Repurpose this Medium article for multiple content platforms: {url}

TARGET PLATFORMS: {platforms}

First, scrape the article using `medium_scrape("{url}")`.

Then create content for each platform:

## Twitter/X Thread
- 5-7 tweets
- Hook first, value in middle, CTA at end
- Include relevant emojis

## LinkedIn Post  
- Professional tone
- 1000-1300 characters
- Include a question for engagement

## Newsletter Blurb
- 2-3 paragraphs
- Highlight key insights
- Clear call-to-action

## YouTube Script Outline
- Hook (30 sec)
- Main points (3-5 min)
- Conclusion + CTA (1 min)

## Instagram Carousel
- 7-10 slides
- One key point per slide
- Visual descriptions

Ensure each format maintains the core message while optimizing for the platform's unique characteristics."""


# ============================================================================
# MAIN
# ============================================================================

if __name__ == "__main__":
    import sys
    
    # Check for HTTP transport flag
    if "--http" in sys.argv or "-h" in sys.argv:
        # Get port from args or use default
        port = 8000
        for i, arg in enumerate(sys.argv):
            if arg in ("--port", "-p") and i + 1 < len(sys.argv):
                port = int(sys.argv[i + 1])
        
        print(f"[INFO] Starting Medium MCP Server v3 in HTTP mode on port {port}")
        print(f"[INFO] Connect via: http://127.0.0.1:{port}/mcp")
        
        # Run with HTTP transport
        mcp.run(transport="sse", host="127.0.0.1", port=port)
    else:
        # Default: stdio transport for Claude Desktop
        mcp.run()