Rebuild frontend with Tailwind CSS + fix Python 3.14 compatibility

- Upgrade SQLAlchemy 2.0.27→2.0.45, google-genai SDK for Python 3.14
- Replace google-generativeai with google-genai in narrative_service.py
- Fix HTTPException handling in main.py (was wrapping as 500)
- Rebuild all frontend components with Tailwind CSS v3:
  - Dashboard, NarrativeSection, StatsGrid, VibeRadar, HeatMap, TopRotation
  - Custom color palette (background-dark, card-dark, accent-neon, etc.)
  - Add glass-panel, holographic-badge CSS effects
- Docker improvements:
  - Combined backend container (API + worker in entrypoint.sh)
  - DATABASE_URL configurable via env var
  - CI workflow builds both backend and frontend images
- Update README with clearer docker-compose instructions

backend/app/database.py

@@ -1,11 +1,14 @@
+import os
 from sqlalchemy import create_engine
 from sqlalchemy.orm import sessionmaker, declarative_base
 
-SQLALCHEMY_DATABASE_URL = "sqlite:///./music.db"
+SQLALCHEMY_DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///./music.db")
 
-engine = create_engine(
-    SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False}
-)
+connect_args = {}
+if SQLALCHEMY_DATABASE_URL.startswith("sqlite"):
+    connect_args["check_same_thread"] = False
+
+engine = create_engine(SQLALCHEMY_DATABASE_URL, connect_args=connect_args)
 SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
 
 Base = declarative_base()

backend/app/main.py

@@ -16,8 +16,18 @@ load_dotenv()
 # Create tables
 Base.metadata.create_all(bind=engine)
 
+from fastapi.middleware.cors import CORSMiddleware
+
 app = FastAPI(title="Music Analyser Backend")
 
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:5173"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 @app.get("/")
 def read_root():
     return {"status": "ok", "message": "Music Analyser API is running"}
@@ -59,9 +69,8 @@ def trigger_analysis(
         if stats_json["volume"]["total_plays"] == 0:
              raise HTTPException(status_code=404, detail="No plays found in the specified period.")
 
-        # 2. Generate Narrative
         narrative_service = NarrativeService(model_name=model_name)
-        narrative_json = narrative_service.generate_narrative(stats_json)
+        narrative_json = narrative_service.generate_full_narrative(stats_json)
 
         # 3. Save Snapshot
         snapshot = AnalysisSnapshot(
@@ -84,6 +93,8 @@ def trigger_analysis(
             "narrative": narrative_json
         }
 
+    except HTTPException:
+        raise  # Re-raise HTTPExceptions as-is (404, etc.)
     except Exception as e:
         print(f"Analysis Failed: {e}")
         raise HTTPException(status_code=500, detail=str(e))

backend/app/services/narrative_service.py

@@ -1,16 +1,15 @@
 import os
 import json
 import re
-import google.generativeai as genai
+from google import genai
 from typing import Dict, Any, List, Optional
 
 class NarrativeService:
     def __init__(self, model_name: str = "gemini-2.0-flash-exp"):
         self.api_key = os.getenv("GEMINI_API_KEY")
+        self.client = genai.Client(api_key=self.api_key) if self.api_key else None
         if not self.api_key:
             print("WARNING: GEMINI_API_KEY not found. LLM features will fail.")
-        else:
-            genai.configure(api_key=self.api_key)
 
         self.model_name = model_name
 
@@ -48,11 +47,10 @@ Your goal is to generate a JSON report that acts as a deeper, more honest "Spoti
 }}
 """
         try:
-            model = genai.GenerativeModel(self.model_name)
-            # Use JSON mode if available, otherwise rely on prompt + cleaning
-            response = model.generate_content(
-                prompt,
-                generation_config={"response_mime_type": "application/json"}
+            response = self.client.models.generate_content(
+                model=self.model_name,
+                contents=prompt,
+                config=genai.types.GenerateContentConfig(response_mime_type="application/json")
             )
             
             return self._clean_and_parse_json(response.text)