diff --git a/src/veggies_recognition/marchew_118.jpg b/src/veggies_recognition/marchew_118.jpg
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diff --git a/src/veggies_recognition/predict.py b/src/veggies_recognition/predict.py
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+++ b/src/veggies_recognition/predict.py
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+import torch
+import torchvision
+import torchvision.transforms as transforms
+from PIL import Image
+
+classes = [
+        "bób", "brokuł", "brukselka", "burak", "cebula",
+        "cukinia", "dynia", "fasola", "groch", "jarmuż",
+        "kalafior", "kalarepa", "kapusta", "marchew", 
+        "ogórek", "papryka", "pietruszka", "pomidor",
+        "por", "rzepa", "rzodkiewka", "sałata", "seler",
+        "szpinak", "ziemniak"]
+
+model = torch.load("best_model.pth")
+
+mean = [0.5322, 0.5120, 0.3696]
+std = [0.2487, 0.2436, 0.2531]
+
+image_transforms = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(torch.Tensor(mean),torch.Tensor(std))
+])
+
+def predict(model, image_transforms, image_path, classes):
+    model = model.eval()
+    image = Image.open(image_path)
+    image = image_transforms(image).float()
+    image = image.unsqueeze(0)
+
+    output = model(image)
+    _, predicted = torch.max(output.data, 1)
+
+    print(classes[predicted.item()])
+
+predict(model, image_transforms, "marchew_118.jpg", classes)
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