model.py

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import cv2

class_names = [
    'Akshay Kumar', 'Alexandra Daddario', 'Alia Bhatt', 'Amitabh Bachchan',
    'Andy Samberg', 'Anushka Sharma', 'Billie Eilish', 'Brad Pitt', 'Camila Cabello',
    'Charlize Theron', 'Claire Holt', 'Courtney Cox', 'Dwayne Johnson', 'Elizabeth Olsen',
    'Ellen Degeneres', 'Henry Cavill', 'Hrithik Roshan', 'Hugh Jackman', 'Jessica Alba',
    'Lisa Kudrow', 'Margot Robbie', 'Natalie Portman', 'Priyanka Chopra', 'Robert Downey Jr',
    'Roger Federer', 'Tom Cruise', 'Vijay Deverakonda', 'Virat Kohli', 'Zac Efron'
]

model = load_model("my_29_30_model_97.h5", compile=False)
model.compile(optimizer=tf.keras.optimizers.Adam(), loss='categorical_crossentropy', metrics=['accuracy'])


def load_and_preprocess_image(img_array, target_size):
    if img_array is None:
        raise ValueError("The input image is None. Cannot preprocess an empty image.")

    img = cv2.resize(img_array, target_size)
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array = tf.keras.applications.vgg16.preprocess_input(img_array)  # Ensure correct preprocessing function
    return img_array

def detect_and_crop_faces(img_array, filter_path):
    """
    Detects faces in an image array using a Haar Cascade filter and returns cropped face images.

    Args:
        img_array (numpy.ndarray): The input image array.
        filter_path (str): The file path to the Haar Cascade XML file for face detection.

    Returns:
        tuple: A tuple containing a list of cropped face images and the original image array.
    """

    if img_array is None:
        raise ValueError("The input image array is None. Please provide a valid image.")

    if not isinstance(img_array, np.ndarray):
        raise TypeError("The input must be a NumPy ndarray.")

    if img_array.size == 0:
        raise ValueError("The input image array is empty. Please provide a non-empty image.")

    img_array = img_array.astype(np.uint8)

    if len(img_array.shape) == 2:
        gray = img_array
    elif len(img_array.shape) == 3 and img_array.shape[2] == 3:
        gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
    else:
        raise ValueError("Unexpected image format: the image should have 1 or 3 channels.")

    face_cascade = cv2.CascadeClassifier(filter_path)

    if face_cascade.empty():
        raise ValueError("Failed to load the Haar Cascade classifier. Please check the file path.")

    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    cropped_faces = [(img_array[y:y + h, x:x + w], (x, y, w, h)) for (x, y, w, h) in faces]

    return cropped_faces, img_array


def predict_faces(img, filter_path):
    img_array = np.array(img)

    if len(img_array.shape) == 2:
        img_array = cv2.cvtColor(img_array, cv2.COLOR_GRAY2RGB)
    elif len(img_array.shape) == 3 and img_array.shape[2] == 1:
        img_array = cv2.cvtColor(img_array, cv2.COLOR_GRAY2RGB)

    cropped_faces, original_img = detect_and_crop_faces(img_array, filter_path)

    if not cropped_faces:
        return "No faces detected.", None

    results = []
    for face, (x, y, w, h) in cropped_faces:
        preprocessed_face = load_and_preprocess_image(face, target_size=(224, 224))
        predictions = model.predict(preprocessed_face)
        pred_idx = np.argmax(predictions[0])
        predicted_label = class_names[pred_idx]
        results.append(predicted_label)

    return results, original_img


def predict_celebrity(image):
    if image is None:
        return "Unknown"

    predictions, _ = predict_faces(image, cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    if predictions == "No faces detected.":
        return "Unknown"
    return predictions[0]