Update app.py

app.py

@@ -3,45 +3,57 @@ import tensorflow as tf
 import numpy as np
 from tensorflow.keras.preprocessing import image
 import os
+import logging
 from werkzeug.utils import secure_filename
 
+# Initialize Flask app
 app = Flask(__name__)
 
-# Ensure the uploads directory exists
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
+# Set up uploads folder
 UPLOAD_FOLDER = "uploads"
-os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+if not os.path.exists(UPLOAD_FOLDER):
+    try:
+        os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+        os.chmod(UPLOAD_FOLDER, 0o755)  # Set proper permissions
+    except Exception as e:
+        logging.error(f"❌ Error creating uploads directory: {e}")
+
 app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER
 
 # Allowed file types
 ALLOWED_EXTENSIONS = {"png", "jpg", "jpeg"}
 
 def allowed_file(filename):
+    """Check if file has an allowed extension."""
     return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS
 
 # Load trained model
 try:
     model = tf.keras.models.load_model("trained.h5")
-    print("✅ Model loaded successfully")
+    logging.info("✅ Model loaded successfully.")
 except Exception as e:
-    print(f"❌ Error loading model: {str(e)}")
+    logging.error(f"❌ Error loading model: {e}")
     model = None
 
 def preprocess_image(img_path):
-    """ Preprocesses image for model prediction. """
+    """Preprocesses the image for model prediction."""
     try:
-        img = image.load_img(img_path, target_size=(150, 150))  # Resize
+        img = image.load_img(img_path, target_size=(150, 150))  # Resize to model input size
         img_array = image.img_to_array(img) / 255.0  # Normalize
         img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
         return img_array
     except Exception as e:
-        print(f"❌ Error in image preprocessing: {str(e)}")
+        logging.error(f"❌ Error in image preprocessing: {e}")
         return None
 
 @app.route("/predict", methods=["POST"])
 def predict():
-    """ Handles image upload and model prediction. """
+    """Handles image upload and model prediction."""
     if model is None:
-        return jsonify({"error": "Model not loaded. Check logs for details."}), 500
+        return jsonify({"error": "Model not loaded. Check server logs for details."}), 500
 
     if "file" not in request.files:
         return jsonify({"error": "No file uploaded"}), 400
@@ -65,7 +77,7 @@ def predict():
             prediction = model.predict(img_array)[0][0]
         except Exception as e:
             os.remove(file_path)
-            print(f"❌ Prediction Error: {str(e)}")
+            logging.error(f"❌ Prediction Error: {e}")
             return jsonify({"error": "Error making prediction"}), 500
 
         os.remove(file_path)  # Cleanup