"""Test the complete DS Task AI News pipeline"""
import sys
import os
sys.path.append('backend')

def test_complete_pipeline():
    """Test the entire news processing pipeline"""
    print("🚀 Testing Complete DS Task AI News Pipeline")
    print("=" * 60)
    
    try:
        # Step 1: Test News Fetching
        print("\n1️⃣ Testing News Fetching...")
        from news_fetcher import NewsFetcher
        
        fetcher = NewsFetcher()
        result = fetcher.fetch_and_save_news()
        
        if result["success"]:
            print(f"✅ Fetched {result['articles_count']} articles")
            articles = result["articles"]
            
            if articles:
                print(f"   Sample article: {articles[0]['title'][:50]}...")
                print(f"   Source: {articles[0]['source']}")
            else:
                print("❌ No articles in result")
                return False
        else:
            print(f"❌ News fetching failed: {result.get('message', 'Unknown error')}")
            return False
        
        # Step 2: Test Embeddings Generation
        print("\n2️⃣ Testing Embeddings Generation...")
        from embeddings import EmbeddingGenerator
        
        embedding_gen = EmbeddingGenerator()
        
        # Test with first few articles
        test_articles = articles[:3]
        embeddings = embedding_gen.generate_embeddings(test_articles)
        
        if embeddings is not None and len(embeddings) > 0:
            print(f"✅ Generated embeddings shape: {embeddings.shape}")
        else:
            print("❌ Embeddings generation failed")
            return False
        
        # Step 3: Test Vector Store
        print("\n3️⃣ Testing Vector Store...")
        from vector_store import VectorStore
        
        vector_store = VectorStore()
        vector_store.add_articles(test_articles, embeddings)
        
        stats = vector_store.get_stats()
        print(f"✅ Vector store stats: {stats['total_articles']} articles")
        
        # Test similarity search
        query_embedding = embedding_gen.generate_query_embedding("artificial intelligence technology")
        similar_articles = vector_store.search_similar(query_embedding, top_k=2)
        
        if similar_articles:
            print(f"✅ Found {len(similar_articles)} similar articles")
            for i, article in enumerate(similar_articles):
                print(f"   {i+1}. {article['title'][:40]}... (score: {article['similarity_score']:.3f})")
        else:
            print("⚠️  No similar articles found (might be due to threshold)")
        
        # Step 4: Test Recommender System
        print("\n4️⃣ Testing Recommender System...")
        from recommender import NewsRecommender
        
        recommender = NewsRecommender()
        
        # Add articles to recommender's store
        store_result = recommender.add_articles_to_store(articles[:5])
        if store_result["success"]:
            print(f"✅ Added {store_result['articles_added']} articles to recommender")
        else:
            print(f"❌ Failed to add articles: {store_result['message']}")
            return False
        
        # Test query-based recommendations
        recommendations = recommender.recommend_by_query("technology news", top_k=3)
        if recommendations:
            print(f"✅ Query recommendations: {len(recommendations)} articles")
            for i, rec in enumerate(recommendations):
                print(f"   {i+1}. {rec['title'][:40]}... (score: {rec['similarity_score']:.3f})")
        else:
            print("⚠️  No query recommendations found")
        
        # Test trending articles
        trending = recommender.get_trending_articles(top_k=3)
        if trending:
            print(f"✅ Trending articles: {len(trending)} articles")
        else:
            print("⚠️  No trending articles found")
        
        # Step 5: Test FastAPI Integration
        print("\n5️⃣ Testing FastAPI Integration...")
        
        # Test if server is running
        import requests
        try:
            response = requests.get("http://localhost:8000/health", timeout=5)
            if response.status_code == 200:
                print("✅ FastAPI server is running")
                health_data = response.json()
                print(f"   Vector store has {health_data.get('vector_store', {}).get('total_articles', 0)} articles")
            else:
                print(f"⚠️  FastAPI server responded with status {response.status_code}")
        except requests.exceptions.RequestException:
            print("⚠️  FastAPI server not accessible (might not be running)")
        
        print("\n" + "=" * 60)
        print("🎉 COMPLETE PIPELINE TEST SUCCESSFUL!")
        print("✅ News fetching working")
        print("✅ Embeddings generation working") 
        print("✅ Vector storage working")
        print("✅ Similarity search working")
        print("✅ Recommendation system working")
        print("✅ All components integrated successfully")
        
        return True
        
    except Exception as e:
        print(f"\n❌ Pipeline test failed with error: {e}")
        import traceback
        traceback.print_exc()
        return False

def test_api_endpoints():
    """Test API endpoints if server is running"""
    print("\n🌐 Testing API Endpoints...")
    
    import requests
    base_url = "http://localhost:8000"
    
    endpoints_to_test = [
        ("GET", "/", "Health check"),
        ("GET", "/health", "Detailed health"),
        ("POST", "/fetch-news", "Fetch news"),
        ("GET", "/trending", "Trending articles"),
        ("GET", "/stats", "System stats")
    ]
    
    for method, endpoint, description in endpoints_to_test:
        try:
            if method == "GET":
                response = requests.get(f"{base_url}{endpoint}", timeout=10)
            else:
                response = requests.post(f"{base_url}{endpoint}", timeout=10)
            
            if response.status_code == 200:
                print(f"✅ {description}: OK")
            else:
                print(f"⚠️  {description}: Status {response.status_code}")
                
        except requests.exceptions.RequestException as e:
            print(f"❌ {description}: Connection error")

if __name__ == "__main__":
    success = test_complete_pipeline()
    
    if success:
        print("\n🚀 Testing API endpoints...")
        test_api_endpoints()
        print("\n✅ SYSTEM FULLY OPERATIONAL!")
    else:
        print("\n❌ Pipeline needs debugging")