"""Test AI features: embeddings and vector search"""
import sys
import os
sys.path.append('backend')

def test_ai_pipeline():
    print("🤖 Testing AI Features Pipeline")
    print("=" * 50)
    
    # Step 1: Get some news articles
    print("1. Fetching news articles...")
    from news_fetcher import NewsFetcher
    fetcher = NewsFetcher()
    
    # Get articles from BBC
    articles = fetcher.fetch_rss_feed("https://feeds.bbci.co.uk/news/rss.xml")
    print(f"✅ Got {len(articles)} articles")
    
    # Use first 5 articles for testing
    test_articles = articles[:5]
    for i, article in enumerate(test_articles):
        print(f"   {i+1}. {article['title'][:50]}...")
    
    # Step 2: Test embeddings
    print("\n2. Testing embeddings generation...")
    from embeddings import EmbeddingGenerator
    
    embedding_gen = EmbeddingGenerator()
    print(f"   Using model: {'Cohere' if embedding_gen.use_cohere else 'Sentence Transformers'}")
    
    # Generate embeddings
    embeddings = embedding_gen.generate_embeddings(test_articles)
    print(f"✅ Generated embeddings: {embeddings.shape}")
    
    # Step 3: Test vector store
    print("\n3. Testing vector store...")
    from vector_store import VectorStore
    
    # Clear any existing index for clean test
    vector_store = VectorStore()
    vector_store.clear_index()
    
    # Add articles to vector store
    vector_store.add_articles(test_articles, embeddings)
    stats = vector_store.get_stats()
    print(f"✅ Vector store: {stats['total_articles']} articles, dimension {stats['index_dimension']}")
    
    # Step 4: Test similarity search
    print("\n4. Testing similarity search...")
    
    # Test query
    query = "technology artificial intelligence"
    query_embedding = embedding_gen.generate_query_embedding(query)
    print(f"   Query: '{query}'")
    
    # Search for similar articles
    similar_articles = vector_store.search_similar(query_embedding, top_k=3)
    
    if similar_articles:
        print(f"✅ Found {len(similar_articles)} similar articles:")
        for i, article in enumerate(similar_articles):
            score = article.get('similarity_score', 0)
            print(f"   {i+1}. {article['title'][:45]}... (score: {score:.3f})")
    else:
        print("⚠️  No similar articles found (threshold might be too high)")
    
    # Step 5: Test recommender system
    print("\n5. Testing recommender system...")
    from recommender import NewsRecommender
    
    recommender = NewsRecommender()
    
    # Add articles to recommender
    result = recommender.add_articles_to_store(test_articles)
    if result["success"]:
        print(f"✅ Added {result['articles_added']} articles to recommender")
        
        # 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):
                score = rec.get('similarity_score', 0)
                print(f"   {i+1}. {rec['title'][:45]}... (score: {score:.3f})")
        
        # Test article-based recommendations
        if test_articles:
            article_id = test_articles[0]['id']
            similar_recs = recommender.recommend_by_article_id(article_id, top_k=2)
            if similar_recs:
                print(f"✅ Article-based recommendations: {len(similar_recs)} articles")
            else:
                print("⚠️  No article-based recommendations found")
    
    print("\n" + "=" * 50)
    print("🎉 AI FEATURES TEST COMPLETED!")
    print("✅ News fetching: Working")
    print("✅ Embeddings generation: Working")
    print("✅ Vector storage: Working")
    print("✅ Similarity search: Working")
    print("✅ Recommendation system: Working")
    
    return True

if __name__ == "__main__":
    try:
        test_ai_pipeline()
        print("\n🚀 AI-powered news system is fully operational!")
    except Exception as e:
        print(f"\n❌ Error in AI pipeline: {e}")
        import traceback
        traceback.print_exc()