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fix: startup logs and DeckBuilderAdapter respect COACH_PROVIDER/DECK_GEN_PROVIDER instead of hardcoding Ollama

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KoalaTrapLord committed 2026-03-25 10:34:04 -04:00
1 parent 21f032d32e
commit 2e3c1ec0f1
1 file changed
+84 -107
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routes/coach.py
+84 -107
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@@ -38,16 +38,16 @@ from services.database import _get_db_conn
from services.deck_service import _compute_deck_analysis
from services.logging import log_coach, log_deckgen
import httpx
from coach.config import COACH_PROVIDER, PPLX_MODEL
from coach.config import COACH_PROVIDER, PPLX_MODEL, DECK_GEN_PROVIDER, DECK_GEN_MODEL
LLM_TIMEOUT = 120 # seconds
router = APIRouter(tags=["coach"])
# ══════════════════════════════════════════════════════════════
# ════════════════════════════════════════════════════════════
# Coach Service (LLM-powered deck coaching)
# ══════════════════════════════════════════════════════════════
# ════════════════════════════════════════════════════════════
# Global coach instances (initialized at startup)
_coach_service = None
@@ -84,22 +84,34 @@ def init_coach_service():
_coach_service = CoachService(_coach_llm, _coach_embeddings)
# Check LLM connection
llm_status = _coach_llm.check_connection()
if llm_status.get("connected"):
log_coach.info(f" Coach LLM: Connected ({llm_status.get('active_model', 'unknown')})")
# Log coach LLM provider
if COACH_PROVIDER == "perplexity":
log_coach.info(f" Coach LLM: Perplexity ({PPLX_MODEL})")
else:
log_coach.warning(f" Coach LLM: Not connected (start Ollama on localhost:11434)")
# Only probe Ollama when actually using it
llm_status = _coach_llm.check_connection()
if llm_status.get("connected"):
log_coach.info(f" Coach LLM: Connected ({llm_status.get('active_model', 'unknown')})")
else:
log_coach.warning(f" Coach LLM: Not connected (start Ollama on localhost:11434)")
# Initialize V3 Deck Generator (Ollama-based)
# Initialize V3 Deck Generator
_deck_gen_v3_error = None
try:
from src.commander_ai_lab.deck_builder.adapter import DeckBuilderAdapter
_deck_gen_v3 = DeckBuilderAdapter(
db_conn_factory=_get_db_conn,
model="gpt-oss:20b",
)
log_deckgen.info(" Deck Gen V3: Initialized (Ollama DeckBuilderAdapter)")
if DECK_GEN_PROVIDER == "perplexity":
_deck_gen_v3 = DeckBuilderAdapter(
db_conn_factory=_get_db_conn,
model=DECK_GEN_MODEL,
)
log_deckgen.info(f" Deck Gen V3: Initialized (Perplexity {DECK_GEN_MODEL})")
else:
from coach.config import LLM_MODEL
_deck_gen_v3 = DeckBuilderAdapter(
db_conn_factory=_get_db_conn,
model=LLM_MODEL,
)
log_deckgen.info(f" Deck Gen V3: Initialized (Ollama {LLM_MODEL})")
except ImportError as e:
_deck_gen_v3_error = f"Missing dependency: {e}"
log_deckgen.error(f" Deck Gen V3: {_deck_gen_v3_error}")
@@ -146,7 +158,6 @@ async def coach_list_decks():
decks = []
for r in rows:
deck_name = r["deck_name"]
# Check if a report exists (by deck name slug match)
has_report = any(
deck_name.lower().replace(" ", "-") == rid.lower() or
deck_name.lower() == rid.lower()
@@ -181,7 +192,6 @@ async def coach_run_session(deck_id: str, body: CoachRequestBody = None):
if _coach_service is None:
raise HTTPException(500, "Coach service not initialized")
# Load embeddings on first use if not loaded
if _coach_embeddings and not _coach_embeddings.loaded:
try:
_coach_embeddings.load(force_download=True)
@@ -196,7 +206,6 @@ async def coach_run_session(deck_id: str, body: CoachRequestBody = None):
except Exception:
goals = None
# Build a fallback DeckReport from the DB if no simulation report exists
fallback_report = None
try:
fallback_report = _build_deck_report_from_db(deck_id)
@@ -223,7 +232,6 @@ def _build_deck_report_from_db(deck_slug: str):
from coach.models import DeckReport, CardPerformance, DeckStructure
conn = _get_db_conn()
# --- Input validation ---
import re
if not deck_slug or len(deck_slug) > 200:
return None
@@ -231,19 +239,16 @@ def _build_deck_report_from_db(deck_slug: str):
if not deck_slug:
return None
# Find the deck by parameterized query instead of full-table scan
slug_lower = deck_slug.lower()
slug_hyphenated = slug_lower.replace(' ', '-')
clean_slug = re.sub(r'[^a-z0-9]+', '-', slug_lower).strip('-')
if slug_lower.isdigit():
# Numeric slug -- match by ID directly
matched_deck = conn.execute(
"SELECT id, name, commander_name, color_identity FROM decks WHERE id = ?",
(int(slug_lower),)
).fetchone()
else:
# Try exact name match, hyphenated match, or clean-slug match
matched_deck = conn.execute(
"""SELECT id, name, commander_name, color_identity FROM decks
WHERE LOWER(name) = ?
@@ -260,14 +265,12 @@ def _build_deck_report_from_db(deck_slug: str):
deck_name = matched_deck["name"]
commander = matched_deck["commander_name"] or ""
# Parse color identity
ci_raw = matched_deck["color_identity"] or "[]"
try:
color_identity = json.loads(ci_raw) if isinstance(ci_raw, str) else ci_raw
except Exception:
color_identity = []
# Load all cards in this deck
card_rows = conn.execute(
"""SELECT dc.card_name, dc.quantity, dc.is_commander, dc.role_tag,
ce.type_line, ce.cmc, ce.oracle_text
@@ -289,7 +292,6 @@ def _build_deck_report_from_db(deck_slug: str):
qty = cr["quantity"] or 1
role_tag = cr["role_tag"] or ""
# Build tags from type_line and role_tag
tags = []
if role_tag:
tags.append(role_tag)
@@ -309,16 +311,13 @@ def _build_deck_report_from_db(deck_slug: str):
elif "Planeswalker" in type_line:
tags.append("planeswalker")
# Type count
for t in ["Creature", "Instant", "Sorcery", "Artifact", "Enchantment", "Planeswalker", "Land"]:
if t in type_line:
type_counts[t] = type_counts.get(t, 0) + qty
# CMC bucket
bucket = min(int(cmc), 7)
cmc_buckets[bucket] += qty
# CardPerformance with zeroed-out sim stats
cards.append(CardPerformance(
name=card_name,
drawnRate=0.0,
@@ -394,10 +393,8 @@ async def coach_chat(body: CoachChatRequest):
if _coach_service is None:
raise HTTPException(500, "Coach service not initialized")
# Load deck report for context
report = _coach_service.load_deck_report(body.deck_id)
# Build system prompt with report context
from coach.prompt_template import build_system_prompt
from coach.models import CoachGoals
@@ -418,71 +415,70 @@ async def coach_chat(body: CoachChatRequest):
"Be specific and actionable in your advice."
)
# Build messages array for LLM
messages = [{"role": "system", "content": system_prompt}]
for msg in body.messages:
messages.append({"role": msg.get("role", "user"), "content": msg.get("content", "")})
# ── Perplexity provider path ──────────────────────────────
messages.append({"role": msg.get("role", "user"), "content": msg.get("content", "")})
# ── Perplexity provider path ─────────────────────────────────────────
if COACH_PROVIDER == 'perplexity':
if not CFG.pplx_api_key:
raise HTTPException(400, 'Perplexity API key not configured. Set PPLX_API_KEY env var.')
pplx_payload = {
'model': PPLX_MODEL,
'messages': messages,
'max_tokens': 4096,
'temperature': 0.7,
}
pplx_headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {CFG.pplx_api_key}',
}
if body.stream:
async def pplx_stream():
pplx_payload['stream'] = True
async with httpx.AsyncClient(timeout=120.0) as client:
async with client.stream('POST', 'https://api.perplexity.ai/chat/completions',
json=pplx_payload, headers=pplx_headers) as resp:
async for line in resp.aiter_lines():
if line.startswith('data: '):
data = line[6:]
if data == '[DONE]':
yield 'data: [DONE]\n\n'
break
try:
chunk = json.loads(data)
content = chunk.get('choices', [{}])[0].get('delta', {}).get('content', '')
if content:
yield f'data: {json.dumps({"content": content})}\n\n'
except json.JSONDecodeError:
continue
return StreamingResponse(pplx_stream(), media_type='text/event-stream')
else:
try:
async with httpx.AsyncClient(timeout=120.0) as client:
resp = await client.post(
'https://api.perplexity.ai/chat/completions',
json=pplx_payload, headers=pplx_headers,
)
resp.raise_for_status()
raw = resp.json()
content = raw.get('choices', [{}])[0].get('message', {}).get('content', '')
usage = raw.get('usage', {})
return {
'content': content,
'model': raw.get('model', ''),
'prompt_tokens': usage.get('prompt_tokens', 0),
'completion_tokens': usage.get('completion_tokens', 0),
}
except httpx.HTTPStatusError as e:
raise HTTPException(502, f'Perplexity API returned {e.response.status_code}')
except httpx.RequestError as e:
raise HTTPException(502, f'Perplexity API call failed: {e}')
# Call LLM with multi-turn messages
if not CFG.pplx_api_key:
raise HTTPException(400, 'Perplexity API key not configured. Set PPLX_API_KEY env var.')
pplx_payload = {
'model': PPLX_MODEL,
'messages': messages,
'max_tokens': 4096,
'temperature': 0.7,
}
pplx_headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {CFG.pplx_api_key}',
}
if body.stream:
async def pplx_stream():
pplx_payload['stream'] = True
async with httpx.AsyncClient(timeout=120.0) as client:
async with client.stream('POST', 'https://api.perplexity.ai/chat/completions',
json=pplx_payload, headers=pplx_headers) as resp:
async for line in resp.aiter_lines():
if line.startswith('data: '):
data = line[6:]
if data == '[DONE]':
yield 'data: [DONE]\n\n'
break
try:
chunk = json.loads(data)
content = chunk.get('choices', [{}])[0].get('delta', {}).get('content', '')
if content:
yield f'data: {json.dumps({"content": content})}\n\n'
except json.JSONDecodeError:
continue
return StreamingResponse(pplx_stream(), media_type='text/event-stream')
else:
try:
async with httpx.AsyncClient(timeout=120.0) as client:
resp = await client.post(
'https://api.perplexity.ai/chat/completions',
json=pplx_payload, headers=pplx_headers,
)
resp.raise_for_status()
raw = resp.json()
content = raw.get('choices', [{}])[0].get('message', {}).get('content', '')
usage = raw.get('usage', {})
return {
'content': content,
'model': raw.get('model', ''),
'prompt_tokens': usage.get('prompt_tokens', 0),
'completion_tokens': usage.get('completion_tokens', 0),
}
except httpx.HTTPStatusError as e:
raise HTTPException(502, f'Perplexity API returned {e.response.status_code}')
except httpx.RequestError as e:
raise HTTPException(502, f'Perplexity API call failed: {e}')
# ── Local Ollama provider path ───────────────────────────────────
try:
import json
from urllib.request import urlopen, Request as UrlRequest
model_name = _coach_llm._resolve_model()
llm_body = {
@@ -493,18 +489,11 @@ async def coach_chat(body: CoachChatRequest):
}
if body.stream:
# SSE streaming response
llm_body["stream"] = True
async def generate():
import asyncio
def _stream():
req = UrlRequest(
f"{_coach_llm.base_url}/chat/completions",
data=json.dumps(llm_body).encode("utf-8"),
headers={"Content-Type": "application/json"},
method="POST",
)
import http.client
import urllib.parse
parsed = urllib.parse.urlparse(f"{_coach_llm.base_url}/chat/completions")
@@ -539,14 +528,12 @@ async def coach_chat(body: CoachChatRequest):
delta = chunk.get("choices", [{}])[0].get("delta", {})
content = delta.get("content", "")
if content:
# Strip think tags from streaming chunks
yield f"data: {json.dumps({'content': content})}\n\n"
except json.JSONDecodeError:
continue
return StreamingResponse(generate(), media_type="text/event-stream")
else:
# Non-streaming: regular call
req = UrlRequest(
f"{_coach_llm.base_url}/chat/completions",
data=json.dumps(llm_body).encode("utf-8"),
@@ -563,7 +550,6 @@ async def coach_chat(body: CoachChatRequest):
raw = await loop.run_in_executor(None, _call)
content = raw.get("choices", [{}])[0].get("message", {}).get("content", "")
# Strip think tags
content = _coach_llm._strip_think_tags(content)
usage = raw.get("usage", {})
@@ -583,14 +569,12 @@ async def coach_apply_suggestions(body: CoachApplyRequest):
"""Apply accepted coaching suggestions to a deck — remove cuts, add adds."""
conn = _get_db_conn()
# Verify deck exists
deck = conn.execute("SELECT * FROM decks WHERE id = ?", (body.deck_id,)).fetchone()
if not deck:
raise HTTPException(404, f"Deck {body.deck_id} not found")
results = {"cuts": [], "adds": [], "errors": []}
# Process cuts: remove cards by name
for card_name in body.accepted_cuts:
row = conn.execute(
"SELECT id FROM deck_cards WHERE deck_id = ? AND card_name = ? LIMIT 1",
@@ -600,7 +584,6 @@ async def coach_apply_suggestions(body: CoachApplyRequest):
conn.execute("DELETE FROM deck_cards WHERE id = ?", (row["id"],))
results["cuts"].append({"name": card_name, "status": "removed"})
else:
# Try case-insensitive
row = conn.execute(
"SELECT id, card_name FROM deck_cards WHERE deck_id = ? AND LOWER(card_name) = LOWER(?) LIMIT 1",
(body.deck_id, card_name)
@@ -611,9 +594,7 @@ async def coach_apply_suggestions(body: CoachApplyRequest):
else:
results["errors"].append({"name": card_name, "error": "Card not found in deck"})
# Process adds: look up in collection first, then Scryfall
for card_name in body.accepted_adds:
# Try to find in collection
ce = conn.execute(
"SELECT scryfall_id, name FROM collection_entries WHERE LOWER(name) = LOWER(?) LIMIT 1",
(card_name,)
@@ -626,7 +607,6 @@ async def coach_apply_suggestions(body: CoachApplyRequest):
scryfall_id = ce["scryfall_id"]
resolved_name = ce["name"]
else:
# Try Scryfall API lookup
try:
import urllib.parse
from urllib.request import urlopen, Request as UrlRequest
@@ -643,7 +623,6 @@ async def coach_apply_suggestions(body: CoachApplyRequest):
pass
if scryfall_id:
# Check if already in deck
existing = conn.execute(
"SELECT id FROM deck_cards WHERE deck_id = ? AND scryfall_id = ?",
(body.deck_id, scryfall_id)
@@ -681,14 +660,12 @@ async def coach_cards_like(card: str, colors: str = None, top_n: int = 10):
conn = _get_db_conn()
results = []
for m in matches:
# Check if card is in collection
owned = conn.execute(
"SELECT SUM(quantity) as qty FROM collection_entries WHERE LOWER(name) = LOWER(?)",
(m.name,)
).fetchone()
owned_qty = (owned["qty"] or 0) if owned else 0
# Get image URL and price
ce = conn.execute(
"SELECT scryfall_id, tcg_price FROM collection_entries WHERE LOWER(name) = LOWER(?) LIMIT 1",
(m.name,)