Feature/delta team/epic r04 (#7)

## Summary

epic r04 - begin refactor

## Change Type

- [x] Cowork feature
- [ ] Bug fix
- [ ] Core AI contribution
- [ ] Test / hardening
- [ ] Performance
- [ ] Documentation

## Related Work

Cowork Task:

Core Repo: http://34.143.229.138/gitea-admin/fsg-ai-core-assets

Core AI Issue:

Core Task:

Related PR:

## Scope

What is intentionally included?

What is intentionally NOT included?

## Validation

- [ ] Unit tests
- [ ] Integration tests
- [ ] Manual verification
- [ ] Regression check

Commands / evidence:

## Security Impact

Permission / credential / network / customer data impact:

## Compatibility

- [ ] No breaking change
- [ ] Breaking change documented

## Reviewer Notes

Anything Cowork reviewers should pay attention to.

---------

Co-authored-by: Anh Tran Nguyen Minh <anhtnm1@fpt.com>
Co-authored-by: Huong Le Thi Thien <huongltt35@fpt.com>
Co-authored-by: Nam Pham Dinh Thanh <nampdt@fpt.com>
Co-authored-by: Vu Dam Tuan <vudt15@fpt.com>
Co-authored-by: Hiep Ha Van <hiephv3@fpt.com>
Co-authored-by: Lam Hoang Van <lamhv7@fpt.com>
Reviewed-on: #7
Co-authored-by: Duy Le Huu <duylh19@fpt.com>

core/usage_ai_report.py

@@ -0,0 +1,56 @@
+"""Dựng câu nhắc cho AI phân tích mức dùng — R09-T02.
+
+Chỉ sinh văn bản. Tách riêng vì đây là phần dễ đổi nhất (câu chữ, cột hiển
+thị) và không liên quan tới việc ghi nhận hay tính tiền.
+"""
+from __future__ import annotations
+
+import json
+import threading
+from datetime import date, datetime
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+from ..config import CONFIG_DIR
+from . import model_pricing as mp
+from .usage_periods import period_breakdown, period_range_label
+
+_AI_ANALYSIS_HEADERS = {
+    "vi": ("Nhận xét thói quen", "Cách viết prompt tiết kiệm hơn", "Hành động giảm token"),
+    "en": ("Usage habits", "Writing more efficient prompts", "Actions to cut token usage"),
+    "ja": ("利用傾向", "より効率的なプロンプトの書き方", "トークン削減のためのアクション"),
+}
+
+def build_ai_analysis_prompt(summary: Dict[str, Any], language: str = "vi") -> str:
+    """The prompt sent to the model for '✨ AI analyze my usage': aggregated
+    numbers only — never raw prompt contents — asking for concrete habits
+    feedback and token-saving recommendations, in the CURRENTLY SELECTED
+    display language (headers included — not just the model's free-text reply,
+    which would otherwise leave the section titles in Vietnamese regardless of
+    the app's language setting)."""
+    lang_names = {"vi": "Vietnamese", "ja": "Japanese", "en": "English"}
+    h1, h2, h3 = _AI_ANALYSIS_HEADERS.get(language, _AI_ANALYSIS_HEADERS["vi"])
+    top = "\n".join(f"- {label}: {tok:,} tokens"
+                    for label, tok in summary.get("top_labels", []))
+    by_source = ", ".join(f"{k}={v:,}" for k, v in summary.get("by_source", []))
+    return (
+        "You are a token-efficiency coach for an AI desktop app (chat tabs + "
+        "scheduled agent tasks). Analyze this usage summary and give the user "
+        "practical advice, replying in "
+        f"{lang_names.get(language, 'Vietnamese')}.\n\n"
+        f"Period stats: {summary.get('turns', 0)} turns, "
+        f"input={summary.get('in', 0):,} tokens, output={summary.get('out', 0):,}, "
+        f"cache={summary.get('cache', 0):,}, "
+        f"avg per prompt={summary.get('avg_per_turn', 0):,}.\n"
+        f"Top consumers:\n{top or '- (none)'}\n"
+        f"By area: {by_source or '(none)'}\n"
+        f"Busiest day: {summary.get('busiest_day')} · busiest hour: {summary.get('busiest_hour')}\n\n"
+        "Reply with EXACTLY these 3 short sections, in markdown, using THESE "
+        f"section headers verbatim (already in {lang_names.get(language, 'Vietnamese')}):\n"
+        f"1. **{h1}** — 2-3 bullet points about the usage pattern.\n"
+        f"2. **{h2}** — 3 concrete prompt-writing tips "
+        "tailored to the numbers above (e.g. long inputs → attach less / summarize "
+        "first; many small turns → batch questions).\n"
+        f"3. **{h3}** — 2-3 app-level actions (compact history, "
+        "smaller model for simple tasks, reuse task outputs instead of re-asking).\n"
+        "Keep the whole reply under 250 words."
+    )