Update app.py

app.py

@@ -12,18 +12,7 @@ braintumor_model = load_model('models/brain_tumor_binary.h5')
 # Configuring Streamlit
 st.set_page_config(page_title="Brain Tumor Prediction App", page_icon=":brain:")
 
-def preprocess_imgs(set_name, img_size):
-    set_new = []
-    for img in set_name:
-        img = cv2.resize(img, dsize=img_size, interpolation=cv2.INTER_CUBIC)
-        set_new.append(preprocess_input(img))
-    return np.array(set_new)
-
-# Handle binary decision
-def binary_decision(confidence):
-    return 1 if confidence >= 0.5 else 0
-
-def predict_braintumor(img):
+def preprocess_image(img):
     # If it's a NumPy array, use it directly
     if isinstance(img, np.ndarray):
         img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
@@ -38,7 +27,19 @@ def predict_braintumor(img):
         img_gray = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE)
 
     # Crop and preprocess the grayscale image
-    img_processed = preprocess_imgs([img_gray], (224, 224))
+    img_processed = cv2.resize(img_gray, (224, 224), interpolation=cv2.INTER_CUBIC)
+    img_processed = preprocess_input(img_processed)
+    img_processed = np.expand_dims(img_processed, axis=0)
+
+    return img_processed
+
+# Handle binary decision
+def binary_decision(confidence):
+    return 1 if confidence >= 0.5 else 0
+
+def predict_braintumor(img):
+    # Preprocess the image
+    img_processed = preprocess_image(img)
 
     # Make prediction
     pred = braintumor_model.predict(img_processed)