--- a
+++ b/Segmentation/infrence.py
@@ -0,0 +1,45 @@
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+os.environ["SM_FRAMEWORK"] = "tf.keras"
+import segmentation_models as sm
+
+sm.framework()
+
+my_model = "efficientnetb1"
+model = sm.Unet(my_model, encoder_weights="imagenet", input_shape=(256, 256, 3), classes=3, activation='sigmoid')
+model.load_weights("Submission_segmentation/weights/unet_model_weights.h5")
+
+# Define the directory to save the images
+output_dir = "result/Test-Dataset2"
+
+# Create the output directory if it doesn't exist
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+def save_single_image_prediction(model, image_path, output_dir):
+    # Load the image
+    original_img = cv2.imread(image_path)
+    resized_img = cv2.resize(original_img, (256, 256))
+
+    # Predict the mask
+    X = np.expand_dims(resized_img, 0)
+    y_pred = model.predict(X)
+    _, y_pred_thr = cv2.threshold(y_pred[0, :, :, 0] * 255, 127, 255, cv2.THRESH_BINARY)
+    y_pred = (y_pred_thr / 255).astype(int)
+
+    # Resize the predicted mask back to the original size
+    y_pred_original = cv2.resize(y_pred.astype(float), (original_img.shape[1], original_img.shape[0]), interpolation=cv2.INTER_LINEAR)
+
+    # Save the original image and predicted mask
+    output_image_path = os.path.join(output_dir, os.path.basename(image_path))
+    # cv2.imwrite(output_image_path, cv2.cvtColor(original_img, cv2.COLOR_BGR2RGB))
+    cv2.imwrite(os.path.join(output_dir, f"predicted_mask_{os.path.basename(image_path)}"), y_pred_original * 255)
+
+
+# for i in os.listdir("Submission_segmentation/data/Auto-WCEBleedGen Challenge Test Dataset/Test Dataset 2"):
+#     model_path = "Submission_segmentation/data/Auto-WCEBleedGen Challenge Test Dataset/Test Dataset 2/"+i
+#     save_single_image_prediction(model,model_path,output_dir)
+#     print(i)