refactor: Remove 3 non-working API endpoints for demo readiness

🔧 REMOVED NON-WORKING ENDPOINTS:
- Removed GET /recommend-news (article ID recommendations)
- Removed POST /analyze-article (AI article analysis)
- Removed POST /generate-insights (AI insights generation)
- Removed associated request models (AnalyzeRequest, InsightsRequest)

📝 UPDATED DOCUMENTATION:
- Updated README.md from 13 to 10 API endpoints
- Updated all endpoint counts throughout documentation
- Reorganized API sections to reflect current functionality
- Maintained accurate system metrics (337 articles)

✅ CURRENT WORKING ENDPOINTS (10):
- Core System (3): /, /health, /stats
- News Management (2): /fetch-news, /articles
- Recommendations (3): /recommend-by-query, /recommend-by-interests, /trending
- Search & Discovery (1): /search
- AI Analysis (1): /ai-status

🚀 System now ready for live demo with 100% working endpoints!

backend/main.py

@@ -88,11 +88,7 @@ class SearchQuery(BaseModel):
     top_k: int = 10
     include_content: bool = False
 
-class AnalyzeRequest(BaseModel):
-    article_id: str
 
-class InsightsRequest(BaseModel):
-    article_count: int = 5
 
 # API Endpoints
 
@@ -147,24 +143,6 @@ async def fetch_news():
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Error fetching news: {str(e)}")
 
-@app.get("/recommend-news")
-async def recommend_news(
-    article_id: str = Query(..., description="ID of the article to find similar articles for"),
-    top_k: int = Query(5, description="Number of recommendations to return")
-):
-    """Get news recommendations based on article ID"""
-    try:
-        recommendations = recommender.recommend_by_article_id(article_id, top_k)
-        
-        return {
-            "success": True,
-            "article_id": article_id,
-            "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-query")
 async def recommend_by_query(query_data: NewsQuery):
@@ -408,54 +386,6 @@ async def get_stats():
 
 # AI Analysis Endpoints
 
-@app.post("/analyze-article")
-async def analyze_article(request: AnalyzeRequest):
-    """Analyze a specific article with AI"""
-    try:
-        # Get article from vector store
-        articles = recommender.vector_store.get_all_articles()
-        article = next((a for a in articles if a.get('id') == request.article_id), None)
-
-        if not article:
-            raise HTTPException(status_code=404, detail="Article not found")
-
-        # Perform AI analysis
-        summary = ai_analyzer.summarize_article(article)
-        keywords = ai_analyzer.extract_keywords(article)
-        sentiment = ai_analyzer.analyze_sentiment(article)
-
-        return {
-            "success": True,
-            "article_id": request.article_id,
-            "analysis": {
-                "summary": summary,
-                "keywords": keywords,
-                "sentiment": sentiment
-            }
-        }
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error analyzing article: {str(e)}")
-
-@app.post("/generate-insights")
-async def generate_insights(request: InsightsRequest):
-    """Generate AI insights from recent articles"""
-    try:
-        # Get recent articles
-        recent_articles = recommender.get_trending_articles(request.article_count)
-
-        # Generate insights
-        insights = ai_analyzer.generate_insights(recent_articles)
-
-        return {
-            "success": True,
-            "insights": insights,
-            "article_count": len(recent_articles)
-        }
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error generating insights: {str(e)}")
-
 @app.get("/ai-status")
 async def get_ai_status():
     """Get AI analyzer status and capabilities"""