import cv2
import numpy as np
from tensorflow.keras.models import load_model


# Load the model
model_output = '/content/drive/MyDrive/saved_models/gender_model/gender_compiled_model'
model = load_model(model_output)

# Load and preprocess an image (assuming 'image_path' is the path to your image)
def preprocess_image(image_path):
    # Load the image using OpenCV
    img = cv2.imread(image_path)

    # Resize the image to 160x160, which is the expected input size for InceptionResNetV1
    img = cv2.resize(img, (224, 224))

    # Convert the image to RGB
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    # Normalize the image (the model expects pixel values between -1 and 1)
    img = img.astype('float32') / 127.5 - 1

    # Add a batch dimension
    img = np.expand_dims(img, axis=0)

    return img

# Path to the image you want to test
image_path = '/content/pic.jpeg'

# Preprocess the image
input_image = preprocess_image(image_path)

# Perform inference to get the face embedding
pred = model.predict(input_image)


# Labels for the genders
labels = ["Woman", "Man"]

predicted_label_index = np.argmax(pred)

# Get the corresponding label
predicted_gender = labels[predicted_label_index]

print(f"Predicted Gender: {predicted_gender}")