improvements added

app.py

@@ -6,7 +6,11 @@ from fastapi.staticfiles import StaticFiles
 from fastapi.templating import Jinja2Templates
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
-import uvicorn # For running the app directly if needed
+import uvicorn 
+import sys # <-- Add this import
+from pathlib import Path # <-- Add this import
+
+sys.path.append(str(Path(__file__).parent / "src"))
 
 # Your existing ML components
 from cnnClassifier.utils.common import decodeImage

dockerfile

@@ -15,6 +15,7 @@ RUN pip install --no-cache-dir -r requirements.txt
 COPY app.py .
 COPY static ./static
 COPY templates ./templates
+COPY src/ . 
 # We need the model, which should be tracked by Git LFS.
 # Create a model directory in the container and copy the model into it.
 RUN mkdir model

src/cnnClassifier/__init__.py

@@ -1,21 +1,14 @@
-import os
 import sys
 import logging
 
+# We will log directly to the console (stdout), which Hugging Face captures.
 logging_str = "[%(asctime)s: %(levelname)s: %(module)s: %(message)s]"
 
-log_dir = "logs"
-log_filepath = os.path.join(log_dir,"running_logs.log")
-os.makedirs(log_dir, exist_ok=True)
-
-
 logging.basicConfig(
-    level= logging.INFO,
-    format= logging_str,
-
+    level=logging.INFO,
+    format=logging_str,
     handlers=[
-        logging.FileHandler(log_filepath),
-        logging.StreamHandler(sys.stdout)
+        logging.StreamHandler(sys.stdout) # Only log to the console
     ]
 )