Working python implementation for wambugu71 model.

models/test1.py

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+from PIL import Image, UnidentifiedImageError
+from transformers import ViTImageProcessor, ViTForImageClassification
+
+# Load the image processor and model
+model_name = 'wambugu71/crop_leaf_diseases_vit'
+image_processor = ViTImageProcessor.from_pretrained(model_name)
+model = ViTForImageClassification.from_pretrained(
+    model_name,
+    ignore_mismatched_sizes=True
+)
+
+# Load your image
+try:
+    image = Image.open('/home/overnion/Status200/potato2.png')  # Replace with the actual path to your image
+    # Convert the image to RGB if it's not already
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+except FileNotFoundError:
+    print("Error: Image file not found.")
+    exit()
+except UnidentifiedImageError:
+    print("Error: Unable to open image. Check the file type.")
+    exit()
+except Exception as e:
+    print(f"An error occurred: {e}")
+    exit()
+
+# Prepare the image for the model
+inputs = image_processor(images=image, return_tensors="pt")
+
+# Make the prediction
+outputs = model(**inputs)
+logits = outputs.logits
+predicted_class_idx = logits.argmax(-1).item()
+
+# Print the predicted class
+print("Predicted class:", model.config.id2label[predicted_class_idx])
+