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import numpy as np | |
import gradio as gr | |
from keras.models import load_model | |
from keras.preprocessing.image import load_img, img_to_array | |
# Load the pre-trained model | |
model = load_model('best_model.h5') | |
# Define class names for Boy or Girl classification | |
class_names = ['Boy', 'Girl'] | |
# Function to preprocess the image and make predictions | |
def classify_image(image): | |
# Load and preprocess the image | |
img = load_img(image, target_size=(150, 150)) # Ensure this matches the input size of your model | |
img_array = img_to_array(img) / 255.0 # Normalize pixel values | |
img_array = np.expand_dims(img_array, axis=0) # Reshape for the model input (1, 150, 150, 3) | |
# Log the shape of the image array for debugging | |
print(f"Image shape for prediction: {img_array.shape}") | |
# Get prediction | |
prediction = model.predict(img_array) | |
# Determine the predicted class based on the model output | |
predicted_class_index = np.argmax(prediction[0]) # Get index of the highest prediction score | |
predicted_class = class_names[predicted_class_index] # Map index to class name | |
return predicted_class | |
# Gradio interface | |
iface = gr.Interface(fn=classify_image, | |
inputs=gr.Image(type="filepath"), | |
outputs=gr.Textbox(), | |
title="Boy or Girl Classifier", | |
description="Upload an image to classify it as either a Boy or a Girl.") | |
# Launch the app | |
iface.launch() | |