Update app.py

app.py

@@ -14,12 +14,8 @@ logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(
 
 # Set up uploads folder
 UPLOAD_FOLDER = "uploads"
-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}")
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)  # Ensure upload folder exists
+os.chmod(UPLOAD_FOLDER, 0o755)  # Set proper permissions
 
 app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER
 
@@ -31,15 +27,20 @@ def allowed_file(filename):
     return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS
 
 # Load trained model
-try:
-    model = tf.keras.models.load_model("trained.h5")
-    logging.info("✅ Model loaded successfully.")
-except Exception as e:
-    logging.error(f"❌ Error loading model: {e}")
+model_path = "trained.h5"
+if os.path.exists(model_path):
+    try:
+        model = tf.keras.models.load_model(model_path)
+        logging.info("✅ Model loaded successfully.")
+    except Exception as e:
+        logging.error(f"❌ Error loading model: {e}")
+        model = None
+else:
+    logging.error("❌ Model file not found! Ensure 'trained.h5' exists in the correct path.")
     model = None
 
 def preprocess_image(img_path):
-    """Preprocesses the image for model prediction."""
+    """Preprocess the image for model prediction."""
     try:
         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
@@ -49,6 +50,10 @@ def preprocess_image(img_path):
         logging.error(f"❌ Error in image preprocessing: {e}")
         return None
 
+@app.route("/", methods=["GET"])
+def home():
+    return jsonify({"message": "Pneumonia Detection API is running!"})
+
 @app.route("/predict", methods=["POST"])
 def predict():
     """Handles image upload and model prediction."""
@@ -90,4 +95,4 @@ def predict():
     return jsonify({"error": "Invalid file format"}), 400
 
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860, debug=True, threaded=True)
+    app.run(host="0.0.0.0", port=7860, debug=True)  # Removed threaded=True for stability