Thalysson#74

.gitignore

@@ -96,3 +96,15 @@ coverage.xml
 .hypothesis/
 .pytest_cache/
 
+# Logs e Arquivos Temporários
+front/.npm-cache/
+front/dist-web/
+front/expo-server.log
+front/expo-server.err.log
+mobile/node_modules/
+mobile/.expo/
+mobile/.npm-cache/
+mobile/dist-web/
+front/.npm-cache/
+front/dist-web/
+*.log

backend/.env.dev

@@ -2,7 +2,7 @@ DATABASE_URL=meubanco123
 GEMINI_API_KEY=chave123
 
 OLLAMA_HOST=https://ollama.com
-OLLAMA_API_KEY=chave321
+OLLAMA_API_KEY=
 LLM_MODEL=gpt-oss:120b-cloud
 
 EMBED_MODEL=qwen3-embedding:4b

backend/services/llm_facade.py

@@ -9,6 +9,13 @@
 from services.rag_engine import RAGEngine
 from services.workout_factory import WorkoutFactory
 
+
+def _ollama_headers() -> dict[str, str]:
+    if not settings.OLLAMA_API_KEY:
+        return {}
+    return {"Authorization": f"Bearer {settings.OLLAMA_API_KEY}"}
+
+
 _EXERCISE_SCHEMA = {
     "type": "object",
     "properties": {
@@ -57,7 +64,7 @@ def __init__(self, rag_engine: RAGEngine) -> None:
         self._rag = rag_engine
         self._client = Client(
             host=settings.OLLAMA_HOST,
-            headers={"Authorization": f"Bearer {settings.OLLAMA_API_KEY}"},
+            headers=_ollama_headers(),
         )
 
     def generate_workout_plan(self, user_query: str, user: User) -> WorkoutPlan:
@@ -86,11 +93,11 @@ def _build_prompt(self, user_query: str, user: User, context: str | None) -> str
             "Always answer in pt-BR, translate everything.\n\n"
             "STRICT RULES FOR THE days ARRAY:\n"
             "- Split the plan into one entry per training day. Each entry must have 'day' (integer), "
-            "'focus' (muscle group or session theme, e.g. 'Peito e Tríceps'), and 'exercises' (array).\n"
+            "'focus' (muscle group or session theme, e.g. 'Peito e Triceps'), and 'exercises' (array).\n"
             "- The number of day entries must equal days_per_week.\n"
             "- The 'reps' field must be compact: use only numbers or ranges (e.g. '12', '8-10'). "
             "For time-based exercises use 'Xs' for seconds or 'Xmin' for minutes (e.g. '30s', '2min'). "
-            "Never write out words like 'repetições', 'segundos', 'minutos'.\n\n"
+            "Never write out words like 'repeticoes', 'segundos', 'minutos'.\n\n"
             "Respond ONLY with a valid JSON object matching this schema (no markdown, no explanation):\n"
             f"{schema_str}"
         )
@@ -100,7 +107,7 @@ def _call_llm_json(self, prompt: str, retries: int = 2) -> dict:
             chunks = []
             for part in self._client.chat(model=settings.LLM_MODEL, messages=[{"role": "user", "content": prompt}], format="json", stream=True):
                 chunks.append(part.message.content)
-                
+
             content = "".join(chunks)
             match = re.search(r"\{.*\}", content, re.DOTALL)
             if not match:

backend/services/rag_engine.py

@@ -1,30 +1,44 @@
-# Serviço tlvz precise modificar ja que o banco ja ta estruturado
+# Servico tlvz precise modificar ja que o banco ja ta estruturado
 # pra poupar fazer busca vetorial/contextual toda vez
 # e fazer um select normal
 # "preciso de um treino para iniciante" -> busca no banco direto por iniciante
 
-import ollama
+from ollama import Client
 
 from core.config import settings
 from repositories.workout_repository import WorkoutRepository
 
 EMBED_MODEL = settings.EMBED_MODEL
 EMBEDDING_DIM = settings.EMBED_DIM
 
+
+def _ollama_headers() -> dict[str, str]:
+    if not settings.OLLAMA_API_KEY:
+        return {}
+    return {"Authorization": f"Bearer {settings.OLLAMA_API_KEY}"}
+
+
 class RAGEngine:
     def __init__(self, workout_repository: WorkoutRepository) -> None:
         self._repo = workout_repository
+        self._client = Client(
+            host=settings.OLLAMA_HOST,
+            headers=_ollama_headers(),
+        )
 
     def _embed_query(self, query: str) -> list[float]:
-        response = ollama.embed(model=EMBED_MODEL, input=query, dimensions=EMBEDDING_DIM)
+        response = self._client.embed(model=EMBED_MODEL, input=query, dimensions=EMBEDDING_DIM)
         return response["embeddings"][0]
 
     def retrieve_context(self, query: str, n: int = 3) -> str | None:
-        vector = self._embed_query(query)
-        rows = self._repo.search_similar(vector, n=n)
-
+        try:
+            vector = self._embed_query(query)
+            rows = self._repo.search_similar(vector, n=n)
+        except Exception:
+            return None
+
         if not rows:
             return None
-        
+
         parts = [f"[{i}] {title}\n{chunk_text}" for i, (title, chunk_text) in enumerate(rows, start=1)]
-        return "\n\n---\n\n".join(parts)
+        return "\n\n---\n\n".join(parts)

front/__tests__/detalheTreino.test.tsx

@@ -13,6 +13,7 @@ jest.mock("@/services/api", () => ({
 jest.mock("expo-router", () => ({
   useLocalSearchParams: () => mockUseLocalSearchParams(),
   useRouter: () => ({ back: jest.fn(), push: jest.fn() }),
+  usePathname: () => '/workout/1',
 }));
 
 jest.mock("react-native-safe-area-context", () => ({
@@ -94,4 +95,4 @@ describe("Tela de detalhe do treino", () => {
 
     expect(screen.getByTestId("loading")).toBeOnTheScreen();
   });
-});
+});