fixy

backend/README.md

@@ -81,7 +81,7 @@ Content-Type: application/json
   "is_deepfake": true,
   "confidence": 0.847,
   "analysis_time": 1.234,
-  "model_used": "mock"
+  "used_model": "mock"
 }
 ```
 

backend/app/api/routes.py

@@ -29,8 +29,28 @@ async def health_check() -> HealthResponse:
     settings = get_settings()
     logger.info("Health check endpoint accessed")
     
+    handlers = {
+        "text": analyze_text,
+        "image": analyze_image,
+    }
+    
+    models_status = {}
+    is_healthy = True
+    
+    for content_type in settings.AVAILABLE_MODELS.keys():
+        handler = handlers.get(content_type)
+        
+        if handler is not None and callable(handler):
+            models_status[content_type] = "ready"
+        else:
+            models_status[content_type] = "error_not_callable"
+            is_healthy = False
+            logger.error(f"Krytyczny brak! Handler dla typu '{content_type}' nie jest callable.")
+
+    overall_status = "ok" if is_healthy else "degraded"
+    
     return HealthResponse(
-        status="ok",
+        status=overall_status,
         service="Deepfake Detection Service",
         version=settings.APP_VERSION,
         available_models=settings.AVAILABLE_MODELS,
@@ -86,7 +106,6 @@ async def analyze(request: AnalysisRequest) -> AnalysisResponse:
             
             analysis_result = await analyze_image(image_bytes)
 
-    # 4. Globalna obsługa błędów
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
     except DeepfakeDetectionError as e:
@@ -101,6 +120,6 @@ async def analyze(request: AnalysisRequest) -> AnalysisResponse:
         is_deepfake=analysis_result["is_deepfake"],
         confidence=analysis_result["confidence"],
         analysis_time=analysis_result["analysis_time"],
-        model_used=model,
+        used_model=model,
         content_type=content_type,
     )

backend/app/models/schemas.py

@@ -66,7 +66,7 @@ class AnalysisResponse(BaseModel):
     is_deepfake: bool = Field(..., description="Whether the content is detected as a deepfake")
     confidence: float = Field(..., ge=0.0, le=1.0, description="Confidence score between 0.0 and 1.0")
     analysis_time: float = Field(..., description="Time taken for analysis in seconds")
-    model_used: str = Field(..., description="The detector model that was used")
+    used_model: str = Field(..., description="The detector model that was used")
     content_type: str = Field(..., description="Type of content analyzed (text/image/video/file)")
     
     class Config:
@@ -75,7 +75,7 @@ class AnalysisResponse(BaseModel):
                 "is_deepfake": True,
                 "confidence": 0.847,
                 "analysis_time": 1.234,
-                "model_used": "mock",
+                "used_model": "mock",
                 "content_type": "image"
             }
         }

index.js

@@ -148,7 +148,7 @@ client.on(Events.InteractionCreate, async (interaction) => {
 					`### Wyniki Analizy (${data.content_type.toUpperCase()})\n` +
 					`${statusEmoji}\n\n` +
 					`* **Pewność modelu:** \`${confidencePercent}%\`\n` +
-					`* **Użyty model:** \`${data.model_used}\`\n` +
+					`* **Użyty model:** \`${data.used_model}\`\n` +
 					`* **Czas przetwarzania:** \`${timeSec} sekund\`\n`;
 
 				await interaction.editReply({