import torch
from torchvision import transforms
from PIL import Image
import json

# Load class names
with open("class_names.json") as f:
    class_names = json.load(f)

# Load model
checkpoint = torch.load("best_model.pth", map_location="cpu")

# Preprocess image
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

image = Image.open("your_leaf.jpg").convert("RGB")
tensor = transform(image).unsqueeze(0)  # add batch dimension

# Predict
with torch.no_grad():
    outputs = model(tensor)
    _, predicted = outputs.max(1)
    print(f"Prediction: {class_names[predicted.item()]}")

erindKotobelliAISWE26/plant-disease-classifier

🌿 Plant Disease Classifier

Model Details

Dataset

Usage

erindKotobelliAISWE26/
plant-disease-classifier