--- a
+++ b/utils/classification.py
@@ -0,0 +1,57 @@
+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model
+import os
+
+# Load model with input shape validation
+CLASSIFIER_PATH = os.path.join(os.path.dirname(__file__), '..', 'app', 'model', 'classifier_model.h5')
+classifier_model = load_model(CLASSIFIER_PATH)
+
+# Get model's expected input shape
+if len(classifier_model.input_shape) == 4:  # CNN (e.g., (None, 120, 120, 1))
+    TARGET_SIZE = classifier_model.input_shape[1:3]
+    GRAYSCALE = classifier_model.input_shape[-1] == 1
+else:  # Flattened input (e.g., (None, 57600))
+    TARGET_SIZE = (int(np.sqrt(classifier_model.input_shape[1]/3)), )*2 if not GRAYSCALE else (int(np.sqrt(classifier_model.input_shape[1])),)*2
+    GRAYSCALE = classifier_model.input_shape[1] % 3 != 0
+
+print(f"Model expects: {TARGET_SIZE} {'grayscale' if GRAYSCALE else 'RGB'}")
+
+def preprocess_image(image_path):
+    """Universal preprocessor adapting to model requirements"""
+    # Read image
+    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE if GRAYSCALE else cv2.IMREAD_COLOR)
+    if img is None:
+        raise ValueError("Invalid image file")
+
+    # Resize and normalize
+    img = cv2.resize(img, TARGET_SIZE)
+    img = img.astype(np.float32) / 255.0
+
+    # Format for model
+    if len(classifier_model.input_shape) == 4:
+        return np.expand_dims(img, axis=(0, -1)) if GRAYSCALE else np.expand_dims(img, axis=0)
+    else:
+        return img.reshape(1, -1)  # Flatten
+
+def classify_disease(image_path):
+    try:
+        # Preprocess
+        input_img = preprocess_image(image_path)
+        
+        # Predict
+        preds = classifier_model.predict(input_img)
+        classes = ['Normal', 'COVID-19', 'Pneumonia', 'Tuberculosis']
+        
+        # Interpret results
+        predicted_idx = np.argmax(preds)
+        confidence = float(np.max(preds))
+        severity = "Severe" if confidence > 0.9 else "Moderate" if confidence > 0.7 else "Mild"
+        
+        return classes[predicted_idx], confidence, severity
+        
+    except Exception as e:
+        raise ValueError(f"Classification failed: {str(e)}")
+
+
+