DS_TASK_AI_VIEWS/backend/main.py

"""FastAPI backend for DS Task AI News"""
from fastapi import FastAPI, HTTPException, Query, Request
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List, Dict, Any, Optional
import uvicorn
import time
from collections import defaultdict

from config import settings
from news_fetcher import NewsFetcher
from recommender import NewsRecommender
from ai_analyzer import AIAnalyzer

# Groq integration
try:
    from groq import Groq
    groq_client = Groq(api_key=settings.groq_api_key) if settings.groq_api_key else None
    groq_available = groq_client is not None
    if groq_available:
        print("✅ Groq LLM service initialized")
    else:
        print("⚠️  Groq API key not provided")
except Exception as e:
    print(f"⚠️  Groq initialization failed: {e}")
    groq_client = None
    groq_available = False

# Initialize FastAPI app
app = FastAPI(
    title="DS Task AI News API",
    description="AI-powered news retrieval and recommendation system",
    version="1.0.0"
)

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # In production, specify actual origins
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize components
news_fetcher = NewsFetcher()
recommender = NewsRecommender()
ai_analyzer = AIAnalyzer()

# Simple rate limiter
rate_limit_storage = defaultdict(list)
RATE_LIMIT_REQUESTS = 100  # requests per minute
RATE_LIMIT_WINDOW = 60  # seconds

def check_rate_limit(client_ip: str) -> bool:
    """Check if client has exceeded rate limit"""
    current_time = time.time()

    # Clean old requests
    rate_limit_storage[client_ip] = [
        req_time for req_time in rate_limit_storage[client_ip]
        if current_time - req_time < RATE_LIMIT_WINDOW
    ]

    # Check if limit exceeded
    if len(rate_limit_storage[client_ip]) >= RATE_LIMIT_REQUESTS:
        return False

    # Add current request
    rate_limit_storage[client_ip].append(current_time)
    return True

# Pydantic models
class NewsQuery(BaseModel):
    query: str
    top_k: int = 5

class InterestsQuery(BaseModel):
    interests: List[str]
    top_k: int = 10

class SearchQuery(BaseModel):
    query: str
    source: Optional[str] = None
    category: Optional[str] = None
    date_from: Optional[str] = None
    date_to: Optional[str] = None
    top_k: int = 10
    include_content: bool = False



# API Endpoints

@app.get("/")
async def root():
    """Health check endpoint"""
    return {
        "message": "DS Task AI News API is running!",
        "version": "1.0.0",
        "status": "healthy"
    }

@app.get("/health")
async def health_check():
    """Detailed health check"""
    stats = recommender.get_store_stats()
    return {
        "status": "healthy",
        "vector_store": stats,
        "settings": {
            "embedding_model": settings.embedding_model,
            "vector_db_type": settings.vector_db_type,
            "rss_feeds_count": len(settings.rss_feeds)
        }
    }

@app.post("/fetch-news")
async def fetch_news():
    """Fetch news from RSS feeds and add to vector store"""
    try:
        # Fetch news articles
        result = news_fetcher.fetch_and_save_news()
        
        if not result["success"]:
            raise HTTPException(status_code=500, detail=result.get("message", "Failed to fetch news"))
        
        # Add articles to vector store
        articles = result["articles"]
        store_result = recommender.add_articles_to_store(articles)
        
        if not store_result["success"]:
            raise HTTPException(status_code=500, detail=store_result.get("message", "Failed to add articles to store"))
        
        return {
            "success": True,
            "message": "News fetched and processed successfully",
            "articles_fetched": result["articles_count"],
            "articles_stored": store_result["articles_added"],
            "total_articles": store_result["total_articles"]
        }
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error fetching news: {str(e)}")


@app.post("/recommend-by-query")
async def recommend_by_query(query_data: NewsQuery):
    """Get news recommendations based on text query"""
    try:
        recommendations = recommender.recommend_by_query(query_data.query, query_data.top_k)
        
        return {
            "success": True,
            "query": query_data.query,
            "recommendations": recommendations,
            "count": len(recommendations)
        }
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error getting recommendations: {str(e)}")

@app.post("/recommend-by-interests")
async def recommend_by_interests(interests_data: InterestsQuery):
    """Get news recommendations based on user interests"""
    try:
        recommendations = recommender.recommend_by_interests(interests_data.interests, interests_data.top_k)
        
        return {
            "success": True,
            "interests": interests_data.interests,
            "recommendations": recommendations,
            "count": len(recommendations)
        }
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error getting recommendations: {str(e)}")

@app.get("/trending")
async def get_trending_news(top_k: int = Query(10, description="Number of trending articles to return")):
    """Get trending news articles"""
    try:
        trending = recommender.get_trending_articles(top_k)
        
        return {
            "success": True,
            "trending_articles": trending,
            "count": len(trending)
        }
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error getting trending news: {str(e)}")

@app.get("/articles")
async def get_all_articles(
    source: Optional[str] = Query(None, description="Filter by news source"),
    limit: int = Query(50, description="Maximum number of articles to return"),
    offset: int = Query(0, description="Number of articles to skip for pagination"),
    category: Optional[str] = Query(None, description="Filter by article category"),
    date_from: Optional[str] = Query(None, description="Filter articles from this date (YYYY-MM-DD)"),
    date_to: Optional[str] = Query(None, description="Filter articles to this date (YYYY-MM-DD)")
):
    """Get all articles with pagination and advanced filtering"""
    try:
        # Get all articles first
        all_articles = recommender.vector_store.get_all_articles()

        # Apply filters
        filtered_articles = all_articles

        # Filter by source
        if source:
            filtered_articles = [a for a in filtered_articles if a.get('source', '').lower() == source.lower()]

        # Filter by category (if articles have categories)
        if category:
            filtered_articles = [a for a in filtered_articles
                               if category.lower() in [cat.lower() for cat in a.get('categories', [])]]

        # Filter by date range
        if date_from or date_to:
            from datetime import datetime

            def parse_date(date_str):
                try:
                    return datetime.fromisoformat(date_str.replace('Z', '+00:00'))
                except:
                    try:
                        return datetime.strptime(date_str, '%Y-%m-%d')
                    except:
                        return None

            if date_from:
                from_date = parse_date(date_from)
                if from_date:
                    filtered_articles = [a for a in filtered_articles
                                       if parse_date(a.get('published_date', '')) and
                                          parse_date(a.get('published_date', '')) >= from_date]

            if date_to:
                to_date = parse_date(date_to)
                if to_date:
                    filtered_articles = [a for a in filtered_articles
                                       if parse_date(a.get('published_date', '')) and
                                          parse_date(a.get('published_date', '')) <= to_date]

        # Sort by published date (newest first)
        filtered_articles = sorted(filtered_articles,
                                 key=lambda x: x.get('published_date', ''),
                                 reverse=True)

        # Calculate pagination
        total_count = len(filtered_articles)
        start_idx = offset
        end_idx = offset + limit
        paginated_articles = filtered_articles[start_idx:end_idx]

        # Calculate pagination metadata
        has_next = end_idx < total_count
        has_prev = offset > 0
        total_pages = (total_count + limit - 1) // limit  # Ceiling division
        current_page = (offset // limit) + 1

        return {
            "success": True,
            "articles": paginated_articles,
            "pagination": {
                "total_count": total_count,
                "count": len(paginated_articles),
                "limit": limit,
                "offset": offset,
                "current_page": current_page,
                "total_pages": total_pages,
                "has_next": has_next,
                "has_prev": has_prev,
                "next_offset": end_idx if has_next else None,
                "prev_offset": max(0, offset - limit) if has_prev else None
            },
            "filters": {
                "source": source,
                "category": category,
                "date_from": date_from,
                "date_to": date_to
            }
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error getting articles: {str(e)}")

@app.post("/search")
async def search_articles(search_data: SearchQuery, request: Request):
    """Advanced search with multiple filters and semantic similarity"""
    try:
        # Rate limiting
        client_ip = request.client.host
        if not check_rate_limit(client_ip):
            raise HTTPException(status_code=429, detail="Rate limit exceeded. Please try again later.")
        # Get semantic search results first
        semantic_results = recommender.search_articles(search_data.query, {}, search_data.top_k * 2)

        # Apply additional filters
        filtered_results = semantic_results

        # Filter by source
        if search_data.source:
            filtered_results = [r for r in filtered_results
                              if r.get('source', '').lower() == search_data.source.lower()]

        # Filter by category
        if search_data.category:
            filtered_results = [r for r in filtered_results
                              if search_data.category.lower() in [cat.lower() for cat in r.get('categories', [])]]

        # Filter by date range
        if search_data.date_from or search_data.date_to:
            from datetime import datetime

            def parse_date(date_str):
                try:
                    return datetime.fromisoformat(date_str.replace('Z', '+00:00'))
                except:
                    try:
                        return datetime.strptime(date_str, '%Y-%m-%d')
                    except:
                        return None

            if search_data.date_from:
                from_date = parse_date(search_data.date_from)
                if from_date:
                    filtered_results = [r for r in filtered_results
                                      if parse_date(r.get('published_date', '')) and
                                         parse_date(r.get('published_date', '')) >= from_date]

            if search_data.date_to:
                to_date = parse_date(search_data.date_to)
                if to_date:
                    filtered_results = [r for r in filtered_results
                                      if parse_date(r.get('published_date', '')) and
                                         parse_date(r.get('published_date', '')) <= to_date]

        # Limit results to requested amount
        final_results = filtered_results[:search_data.top_k]

        # Optionally include full content
        if not search_data.include_content:
            for result in final_results:
                if 'content' in result and len(result['content']) > 200:
                    result['content'] = result['content'][:200] + "..."

        return {
            "success": True,
            "query": search_data.query,
            "filters": {
                "source": search_data.source,
                "category": search_data.category,
                "date_from": search_data.date_from,
                "date_to": search_data.date_to
            },
            "results": final_results,
            "count": len(final_results),
            "total_semantic_matches": len(semantic_results),
            "filtered_matches": len(filtered_results)
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error searching articles: {str(e)}")

@app.get("/stats")
async def get_stats():
    """Get system statistics"""
    try:
        stats = recommender.get_store_stats()

        # Add RSS feed information
        stats['rss_feeds'] = settings.rss_feeds
        stats['embedding_model'] = settings.embedding_model
        stats['groq_available'] = groq_available

        return {
            "success": True,
            "statistics": stats
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error getting stats: {str(e)}")

# AI Analysis Endpoints

@app.get("/ai-status")
async def get_ai_status():
    """Get AI analyzer status and capabilities"""
    try:
        status = ai_analyzer.get_status()

        return {
            "success": True,
            "ai_status": status
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error getting AI status: {str(e)}")

# Run the application
if __name__ == "__main__":
    uvicorn.run(
        "main:app",
        host=settings.host,
        port=settings.port,
        reload=settings.debug
    )