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import torch
from torchvision import transforms as tt
from PIL import Image
import cv2
def predict_potato(image_path, model):
# Define the pre-processing transform
transforms = tt.Compose([
tt.Resize((224, 224)),
tt.ToTensor()
])
image = Image.open(image_path).convert("RGB")
# Pre-process the image
image_tensor = transforms(image).unsqueeze(0)
# Set the model to evaluation mode
model.eval()
# Make a prediction
with torch.no_grad():
output = model(image_tensor)
# Convert the output to probabilities using softmax
probabilities = torch.nn.functional.softmax(output[0], dim=0)
# Get the predicted class
predicted_class = torch.argmax(probabilities).item()
# Get the probability for the predicted class
predicted_probability = probabilities[predicted_class].item()
# Define class labels
class_labels = ['Potato Early Blight', 'Potato Late Blight', 'Potato Healthy']
return class_labels[predicted_class], predicted_probability, image |