Harimitra

pip install -r requirement.txt

from tensorflow import keras
import numpy as np
from PIL import Image

# Load the model
model = keras.models.load_model('trained_model.keras')

# Load and preprocess image
img = Image.open('plant_leaf.jpg')
img = img.resize((256, 256))  # Dataset standard size
img_array = np.array(img) / 255.0  # Normalize to [0, 1]
img_array = np.expand_dims(img_array, axis=0)

# Make prediction
predictions = model.predict(img_array)
predicted_class = np.argmax(predictions, axis=1)

print(f"Predicted class: {predicted_class[0]}")
print(f"Confidence: {np.max(predictions)*100:.2f}%")