personal memory agent
formatting
+20
-16
+19
-13
apps/home/routes.py
+19
-13
apps/home/routes.py
···
165
165
)
166
166
167
167
# Extract daily goal (first pending todo)
168
-
facet_data["goal"] = next((t["text"] for t in facet_data["todos"] if not t["completed"]), None)
168
+
facet_data["goal"] = next(
169
+
(t["text"] for t in facet_data["todos"] if not t["completed"]), None
170
+
)
169
171
170
172
# Add facet metadata
171
173
result["facet_meta"][facet_name] = {
···
186
188
next_day = (ref_date + timedelta(days=i)).strftime("%Y%m%d")
187
189
day_events = get_events(next_day)
188
190
for e in day_events:
189
-
upcoming_data.append({
190
-
"type": "event",
191
-
"day": next_day,
192
-
"title": e.get("title") or e.get("summary", "Untitled Event"),
193
-
"facet": e.get("facet")
194
-
})
191
+
upcoming_data.append(
192
+
{
193
+
"type": "event",
194
+
"day": next_day,
195
+
"title": e.get("title") or e.get("summary", "Untitled Event"),
196
+
"facet": e.get("facet"),
197
+
}
198
+
)
195
199
for f_name in facet_names:
196
200
f_todos = get_todos(next_day, f_name)
197
201
if f_todos:
198
202
for t in f_todos:
199
203
if not t.get("completed") and not t.get("cancelled"):
200
-
upcoming_data.append({
201
-
"type": "todo",
202
-
"day": next_day,
203
-
"title": t.get("text"),
204
-
"facet": f_name
205
-
})
204
+
upcoming_data.append(
205
+
{
206
+
"type": "todo",
207
+
"day": next_day,
208
+
"title": t.get("text"),
209
+
"facet": f_name,
210
+
}
211
+
)
206
212
except Exception:
207
213
pass
208
214
result["upcoming"] = upcoming_data[:5] # Limit to 5 items
+1
-3
apps/tokens/routes.py
+1
-3
apps/tokens/routes.py
···
247
247
segment_list.sort(key=lambda x: x["cost"], reverse=True)
248
248
249
249
# Count attributed segments (excluding [unattributed])
250
-
segment_count = sum(
251
-
1 for s in segment_list if s["segment"] != "[unattributed]"
252
-
)
250
+
segment_count = sum(1 for s in segment_list if s["segment"] != "[unattributed]")
253
251
254
252
# Calculate cached/reasoning percentages for display annotations
255
253
# - cached_tokens are a subset of input_tokens (reduce cost)