model.py

from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import GlobalAveragePooling2D, Dense
from tensorflow.keras.models import Model

import tensorflow as tf
import numpy as np
from PIL import Image  # <-- Add this import


def build_model(num_classes):
    base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
    base_model.trainable = False

    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    x = Dense(128, activation='relu')(x)
    output = Dense(num_classes, activation='softmax')(x)

    model = Model(inputs=base_model.input, outputs=output)
    return model

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}