fix: resolve Discovery service errors for UI functionality

- Fix Pydantic validation error by making release_title optional in RecommendationResult
- Add numpy array serialization to handle Plotly chart data in analytics responses
- Remove empty string defaults to properly handle None values in recommendations

These changes ensure the Discovery UI can properly handle API responses even when
data is missing or when analytics charts contain numpy arrays.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: SimplicityGuy

discovery/analytics.py

@@ -4,6 +4,7 @@
 from datetime import datetime
 from typing import Any
 
+import numpy as np
 import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
@@ -16,6 +17,19 @@
 logger = logging.getLogger(__name__)
 
 
+def convert_numpy_to_json_serializable(obj: Any) -> Any:
+    """Convert numpy arrays and other non-serializable objects to JSON-serializable format."""
+    if isinstance(obj, np.ndarray):
+        return obj.tolist()
+    elif isinstance(obj, np.integer | np.floating):
+        return obj.item()
+    elif isinstance(obj, dict):
+        return {key: convert_numpy_to_json_serializable(value) for key, value in obj.items()}
+    elif isinstance(obj, list):
+        return [convert_numpy_to_json_serializable(item) for item in obj]
+    return obj
+
+
 class AnalyticsRequest(BaseModel):
     """Request model for analytics queries."""
 
@@ -151,7 +165,7 @@ async def analyze_genre_trends(self, time_range: tuple[int, int] | None = None)
 
         return AnalyticsResult(
             chart_type="line",
-            chart_data=fig.to_dict(),
+            chart_data=convert_numpy_to_json_serializable(fig.to_dict()),
             insights=insights,
             metadata={
                 "time_range": (start_year, end_year),
@@ -279,7 +293,7 @@ async def analyze_artist_evolution(self, artist_name: str) -> AnalyticsResult:
 
         return AnalyticsResult(
             chart_type="scatter",
-            chart_data=fig.to_dict(),
+            chart_data=convert_numpy_to_json_serializable(fig.to_dict()),
             insights=insights,
             metadata={
                 "artist": artist_name,
@@ -388,7 +402,7 @@ async def analyze_label_insights(self, label_name: str | None = None) -> Analyti
 
         return AnalyticsResult(
             chart_type="bar",
-            chart_data=fig.to_dict(),
+            chart_data=convert_numpy_to_json_serializable(fig.to_dict()),
             insights=insights,
             metadata={"label": label_name, "total_records": len(label_data)},
         )
@@ -499,7 +513,7 @@ async def analyze_market_trends(self, analysis_focus: str = "format") -> Analyti
 
         return AnalyticsResult(
             chart_type="area" if analysis_focus == "format" else "line",
-            chart_data=fig.to_dict(),
+            chart_data=convert_numpy_to_json_serializable(fig.to_dict()),
             insights=insights,
             metadata={"focus": analysis_focus, "total_records": len(market_data)},
         )

discovery/recommender.py

@@ -44,9 +44,9 @@ class RecommendationResult(BaseModel):
     """Result model for music recommendations."""
 
     artist_name: str
-    release_title: str
-    year: int | None
-    genres: list[str]
+    release_title: str | None = None
+    year: int | None = None
+    genres: list[str] = []
     similarity_score: float
     explanation: str
     neo4j_id: str
@@ -203,7 +203,7 @@ async def get_similar_artists(self, artist_name: str, limit: int = 10) -> list[R
                     recommendations.append(
                         RecommendationResult(
                             artist_name=similar_artist,
-                            release_title=artist_info.get("recent_release", ""),
+                            release_title=artist_info.get("recent_release"),
                             year=artist_info.get("recent_year"),
                             genres=artist_info.get("genres", []),
                             similarity_score=score,
@@ -233,7 +233,7 @@ async def get_similar_artists(self, artist_name: str, limit: int = 10) -> list[R
                     recommendations.append(
                         RecommendationResult(
                             artist_name=similar_artist,
-                            release_title=artist_info.get("recent_release", ""),
+                            release_title=artist_info.get("recent_release"),
                             year=artist_info.get("recent_year"),
                             genres=artist_info.get("genres", []),
                             similarity_score=float(score),
@@ -282,7 +282,7 @@ async def get_trending_music(self, genres: list[str] | None = None, limit: int =
                 trending.append(
                     RecommendationResult(
                         artist_name=record["name"],
-                        release_title=record["recent_release"] or "",
+                        release_title=record["recent_release"],
                         year=record["recent_year"],
                         genres=record["genres"] or [],
                         similarity_score=float(record["release_count"]) / 100.0,  # Normalize to 0-1
@@ -316,7 +316,7 @@ async def discovery_search(self, query: str, limit: int = 10) -> list[Recommenda
                     results.append(
                         RecommendationResult(
                             artist_name=artist_name,
-                            release_title=artist_info.get("recent_release", ""),
+                            release_title=artist_info.get("recent_release"),
                             year=artist_info.get("recent_year"),
                             genres=artist_info.get("genres", []),
                             similarity_score=float(score),