Add mood scanner and surprise me features to discover page

Add mood_energy and mood_valence sliders that inject mood context into
AI recommendation prompts. Add "Surprise Me" button that generates
recommendations from a creative, unexpected angle without requiring
any user input. Includes backend endpoints, schema updates, and
full frontend UI integration.

backend/app/api/endpoints/recommendations.py

@@ -31,6 +31,25 @@ async def generate(
     recs, remaining = await generate_recommendations(
         db, user, playlist_id=data.playlist_id, query=data.query, bandcamp_mode=data.bandcamp_mode,
         mode=data.mode, adventurousness=data.adventurousness, exclude=data.exclude, count=data.count,
+        mood_energy=data.mood_energy, mood_valence=data.mood_valence,
+    )
+
+    if not recs and remaining == 0:
+        raise HTTPException(status_code=429, detail="Weekly recommendation limit reached. Upgrade to Premium for unlimited.")
+
+    return RecommendationResponse(
+        recommendations=[RecommendationItem.model_validate(r) for r in recs],
+        remaining_this_week=remaining,
+    )
+
+
+@router.post("/surprise", response_model=RecommendationResponse)
+async def surprise(
+    user: User = Depends(get_current_user),
+    db: AsyncSession = Depends(get_db),
+):
+    recs, remaining = await generate_recommendations(
+        db, user, query=None, mode="surprise", count=5
     )
 
     if not recs and remaining == 0:

backend/app/schemas/recommendation.py

@@ -11,6 +11,8 @@ class RecommendationRequest(BaseModel):
     adventurousness: int = 3  # 1-5
     exclude: str | None = None  # comma-separated genres to exclude
     count: int = 5  # Number of recommendations (5, 10, 15, 20)
+    mood_energy: int | None = None  # 1-5, 1=chill, 5=energetic
+    mood_valence: int | None = None  # 1-5, 1=sad/dark, 5=happy/bright
 
 
 class RecommendationItem(BaseModel):

backend/app/services/recommender.py

@@ -70,6 +70,7 @@ MODE_PROMPTS = {
     "era_bridge": "Suggest classic artists from earlier eras who directly inspired their current favorites. Trace musical lineage — if they love Tame Impala, suggest the 70s psych rock that influenced him. Bridge eras.",
     "deep_cuts": "Find B-sides, album tracks, rarities, and lesser-known songs from artists already in their library. Focus on tracks they probably haven't heard even from artists they already know.",
     "rising": "Find artists with under 50K monthly listeners who match their taste. Focus on brand new, up-and-coming artists who haven't broken through yet. Think artists who just released their debut album or EP.",
+    "surprise": "Be wildly creative. Pick ONE obscure, unexpected angle from their taste profile — maybe a specific production technique, a niche sub-genre, a particular era, or an unusual sonic quality — and build all recommendations around that single thread. Start your 'reason' for the first recommendation by explaining the angle you chose. Make it feel like a curated rabbit hole they never knew they wanted.",
 }
 
 
@@ -92,6 +93,8 @@ async def generate_recommendations(
     adventurousness: int = 3,
     exclude: str | None = None,
     count: int = 5,
+    mood_energy: int | None = None,
+    mood_valence: int | None = None,
 ) -> tuple[list[Recommendation], int | None]:
     """Generate AI music recommendations using Claude."""
 
@@ -145,7 +148,10 @@ async def generate_recommendations(
     disliked_artists = list({a for a in disliked_result.scalars().all()})
 
     # Build prompt
-    user_request = query or "Find me music I'll love based on my taste profile. Prioritize lesser-known artists and hidden gems."
+    if mode == "surprise" and not query:
+        user_request = "Surprise me with something unexpected based on my taste profile. Pick a creative, unusual angle I wouldn't think of myself."
+    else:
+        user_request = query or "Find me music I'll love based on my taste profile. Prioritize lesser-known artists and hidden gems."
 
     if bandcamp_mode:
         focus_instruction = "IMPORTANT: Strongly prioritize independent and underground artists who release music on Bandcamp. Think DIY, indie labels, self-released artists, and the kind of music you'd find crate-digging on Bandcamp. Focus on artists who self-publish or release on small indie labels."
@@ -158,6 +164,18 @@ async def generate_recommendations(
     # Adventurousness instruction
     adventurousness_instruction = build_adventurousness_prompt(adventurousness)
 
+    # Mood instruction
+    mood_instruction = ""
+    if mood_energy is not None or mood_valence is not None:
+        energy_desc = {1: "very chill and calm", 2: "relaxed", 3: "moderate energy", 4: "upbeat and energetic", 5: "high energy and intense"}
+        valence_desc = {1: "dark, melancholy, or moody", 2: "introspective or bittersweet", 3: "neutral mood", 4: "positive and uplifting", 5: "happy, euphoric, or celebratory"}
+        parts = []
+        if mood_energy is not None:
+            parts.append(f"Energy: {energy_desc.get(mood_energy, 'moderate energy')}")
+        if mood_valence is not None:
+            parts.append(f"Mood: {valence_desc.get(mood_valence, 'neutral mood')}")
+        mood_instruction = f"\nMatch this mood: {'. '.join(parts)}"
+
     # Exclude genres instruction
     exclude_instruction = ""
     combined_exclude = exclude or ""
@@ -180,6 +198,7 @@ User request: {user_request}
 Discovery mode: {mode_instruction}
 
 {adventurousness_instruction}
+{mood_instruction}
 
 IMPORTANT: If the user mentions specific artists or songs in their request, do NOT recommend anything BY those artists. The user already knows them — recommend music by OTHER artists that match the vibe. For example, if they say "I like Sublime", recommend artists similar to Sublime, but NEVER Sublime themselves.