model.py

from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np

# ✅ Define your class names in the exact training order
class_names = [
    "Pepper__bell___Bacterial_spot",
    "Pepper__bell___healthy",
    "Potato___healthy",
    "Potato___Early_blight"
]

# ✅ Load your trained model
# Make sure this file is in the same folder as model.py
model = load_model("final_plantvillage_model.keras")

def predict(image: Image.Image):
    """
    Predict the class of a given PIL image.
    """
    image = image.resize((224, 224))
    img_array = np.array(image) / 255.0
    img_array = np.expand_dims(img_array, axis=0)

    predictions = model.predict(img_array)
    predicted_class = class_names[np.argmax(predictions)]
    confidence = float(np.max(predictions))

    return {predicted_class: confidence}