"""
CryptoSentinel Pro — High-performance FastAPI application.

This is the main entry point that orchestrates the entire application.
- Integrates an asynchronous PriceFetcher for live market data.
- Integrates a sophisticated GeminiAnalyzer for deep text analysis.
- Implements an automated pipeline to fetch, analyze, and stream top crypto news.
- Serves the interactive frontend and provides all necessary API endpoints.
"""
import asyncio
import json
import os
from contextlib import asynccontextmanager
from typing import Optional, Union
from newsapi import NewsApiClient

import httpx
from fastapi import FastAPI, Request, BackgroundTasks
from fastapi.responses import HTMLResponse, StreamingResponse
from fastapi.templating import Jinja2Templates
from pydantic import BaseModel, constr

# Correct imports using relative paths
from .price_fetcher import PriceFetcher
from .gemini_analyzer import GeminiAnalyzer
from newsapi import NewsApiClient

# --- Pydantic Model for API Input Validation ---

class SentimentRequest(BaseModel):
    """Ensures the text for sentiment analysis is a non-empty string."""
    text: constr(strip_whitespace=True, min_length=1)

# --- Application Lifespan for Resource Management ---

@asynccontextmanager
async def lifespan(app: FastAPI):
    """
    Manages application startup and shutdown events using the modern
    lifespan context manager.
    """
    async with httpx.AsyncClient() as client:
        app.state.price_fetcher = PriceFetcher(client=client, coins=["bitcoin", "ethereum", "dogecoin"])
        app.state.gemini_analyzer = GeminiAnalyzer(client=client)
        app.state.news_api = NewsApiClient(api_key=os.getenv("NEWS_API_KEY"))

        app.state.sentiment_queue: asyncio.Queue = asyncio.Queue()
        app.state.news_queue: asyncio.Queue = asyncio.Queue()

        price_task = asyncio.create_task(
            run_periodic_updates(app.state.price_fetcher, interval_seconds=30)
        )
        news_task = asyncio.create_task(
            run_periodic_news_analysis(app, interval_seconds=900)
        )
        
        print("🚀 CryptoSentinel Pro started successfully.")
        yield
        
        print("⏳ Shutting down background tasks...")
        price_task.cancel()
        news_task.cancel()
        try:
            await asyncio.gather(price_task, news_task)
        except asyncio.CancelledError:
            print("Background tasks cancelled successfully.")
        print("✅ Shutdown complete.")

async def run_periodic_updates(fetcher: PriceFetcher, interval_seconds: int):
    """Periodically updates prices."""
    while True:
        await fetcher.update_prices_async()
        await asyncio.sleep(interval_seconds)

async def run_periodic_news_analysis(app: FastAPI, interval_seconds: int):
    """Periodically fetches and analyzes crypto news."""
    while True:
        print("📰 Fetching latest crypto news for automated analysis...")
        try:
            top_headlines = app.state.news_api.get_everything(
                q='bitcoin OR ethereum OR crypto OR blockchain',
                language='en',
                sort_by='publishedAt',
                page_size=5
            )
            analyzer: GeminiAnalyzer = app.state.gemini_analyzer
            for article in top_headlines.get('articles', []):
                title = article.get('title')
                if title and "[Removed]" not in title:
                    analysis = await analyzer.analyze_text(title)
                    analysis['url'] = article.get('url')
                    await app.state.news_queue.put(analysis)
        except Exception as e:
            print(f"❌ Error during news fetching or analysis: {e}")
            
        await asyncio.sleep(interval_seconds)

# --- FastAPI App Initialization ---

app = FastAPI(title="CryptoSentinel Pro", lifespan=lifespan)
templates = Jinja2Templates(directory="templates")

# --- HTML Rendering Helper ---

def render_analysis_card(payload: dict, is_news: bool = False) -> str:
    """Renders a dictionary of analysis into a styled HTML card."""
    s = payload
    text_to_show = s.get('summary', 'Analysis failed or not available.')
    if is_news:
        url = s.get('url', '#')
        text_to_show = f'<a href="{url}" target="_blank" rel="noopener noreferrer">{s.get("summary", "N/A")}</a>'
    impact_class = f"impact-{s.get('impact', 'low').lower()}"
    sentiment_class = f"sentiment-{s.get('sentiment', 'neutral').lower()}"
    return f"""
    <div class="card {impact_class}">
        <blockquote>{text_to_show}</blockquote>
        <div class="grid">
            <div><strong>Sentiment:</strong> <span class="{sentiment_class}">{s.get('sentiment')} ({s.get('sentiment_score', 0):.2f})</span></div>
            <div><strong>Impact:</strong> {s.get('impact')}</div>
        </div>
        <div class="grid">
            <div><strong>Topic:</strong> {s.get('topic')}</div>
            <div><strong>Entities:</strong> {', '.join(s.get('entities', []))}</div>
        </div>
    </div>
    """

# --- API Endpoints ---

@app.get("/", response_class=HTMLResponse)
async def serve_dashboard(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})

@app.get("/api/prices", response_class=HTMLResponse)
async def get_prices_fragment(request: Request):
    price_fetcher: PriceFetcher = request.app.state.price_fetcher
    prices = price_fetcher.get_current_prices()
    html_fragment = "".join(
        f"<div><strong>{coin.capitalize()}:</strong> ${price:,.2f}</div>" if isinstance(price, (int, float))
        else f"<div><strong>{coin.capitalize()}:</strong> {price}</div>"
        for coin, price in prices.items()
    )
    return HTMLResponse(content=html_fragment)

@app.post("/api/sentiment")
async def analyze_sentiment(payload: SentimentRequest, request: Request, background_tasks: BackgroundTasks):
    analyzer: GeminiAnalyzer = request.app.state.gemini_analyzer
    async def analysis_task_wrapper():
        analysis_result = await analyzer.analyze_text(payload.text)
        await request.app.state.sentiment_queue.put(analysis_result)
    background_tasks.add_task(analysis_task_wrapper)
    return HTMLResponse(content="<small>✅ Queued for deep analysis...</small>")

@app.get("/api/sentiment/stream")
async def sentiment_stream(request: Request):
    queue: asyncio.Queue = request.app.state.sentiment_queue
    async def event_generator():
        while True:
            payload = await queue.get()
            html = render_analysis_card(payload)
            data_payload = html.replace('\n', '')
            sse_message = f"event: sentiment_update\ndata: {data_payload}\n\n"
            yield sse_message
    return StreamingResponse(event_generator(), media_type="text/event-stream")

@app.get("/api/news/stream")
async def news_stream(request: Request):
    queue: asyncio.Queue = request.app.state.news_queue
    async def event_generator():
        while True:
            payload = await queue.get()
            html = render_analysis_card(payload, is_news=True)
            data_payload = html.replace('\n', '')
            sse_message = f"event: news_update\ndata: {data_payload}\n\n"
            yield sse_message
    return StreamingResponse(event_generator(), media_type="text/event-stream")