fix: Ran out of memory (used over 512MB) while running your code.

fastapi-starter/backend/app/config/settings.py

@@ -48,7 +48,10 @@ class Settings(BaseSettings):
 
     # MongoDB settings
     MONGODB_URI: str
-    DATABASE_NAME: str
+    DATABASE_NAME: str = "sample_mflix"
+
+    # ML settings
+    ENABLE_ML: bool = True
 
     # Logging
     LOG_LEVEL: str = "INFO"

fastapi-starter/backend/app/main.py

@@ -26,10 +26,13 @@ async def lifespan(app: FastAPI):
         logger.error(f"Failed to connect to MongoDB: {e}")
 
     # Pre-load ML model
-    try:
-        ml_service.load_model()
-    except Exception as e:
-        logger.error(f"Failed to pre-load ML model: {e}")
+    if settings.ENABLE_ML:
+        try:
+            ml_service.load_model()
+        except Exception as e:
+            logger.error(f"Failed to pre-load ML model: {e}")
+    else:
+        logger.info("ML model pre-loading skipped (ENABLE_ML=False)")
 
     yield
 

fastapi-starter/backend/app/modules/api/v1/ml/service.py

@@ -1,9 +1,6 @@
-"""ML Service for sentiment analysis using PyTorch and Transformers."""
-
-import torch
-from transformers import pipeline
 from typing import List, Dict, Any
 import time
+
 from app.utils.logger import get_logger
 
 logger = get_logger(__name__)
@@ -14,21 +11,29 @@ class MLService:
     def __init__(self, model_name: str = "distilbert-base-uncased-finetuned-sst-2-english"): 
         """
         Initialize the ML service.
-
-        HERE IS THE MODEL REFERENCE: https://huggingface.co/docs/transformers/en/model_doc/distilbert?usage=Pipeline    
-
-        Args:
-            model_name: HuggingFace model name
         """ 
         self.model_name = model_name
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
         self.classifier = None
-        logger.info(f"Initializing MLService with model: {model_name} on device: {self.device}")
+        self._device_cache = None
+        logger.info(f"MLService initialized (lazy-loading enabled for {model_name})")
+
+    @property
+    def device(self):
+        """Lazy-loaded device info."""
+        if self._device_cache is None:
+            try:
+                import torch
+                self._device_cache = "cuda" if torch.cuda.is_available() else "cpu"
+            except ImportError:
+                self._device_cache = "cpu"
+        return self._device_cache
 
     def load_model(self):
         """Load the model if not already loaded."""
         if self.classifier is None:
             try:
+                from transformers import pipeline
+                logger.info(f"Loading ML model: {self.model_name} on {self.device}")
                 self.classifier = pipeline(
                     "sentiment-analysis",
                     model=self.model_name,

fastapi-starter/backend/render.yaml

@@ -15,5 +15,6 @@ services:
         sync: false
         description: "MongoDB Connection String"
       - key: DATABASE_NAME
-        sync: false
-        description: "MongoDB Database Name"
+        value: sample_mflix
+      - key: ENABLE_ML
+        value: false