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| import gradio as gr | |
| from tensorflow import keras | |
| from keras import models | |
| from keras.preprocessing import image | |
| title = "Image Classifier" | |
| description = '''This Project is used to predict gender - male/female & hand-written digit in an image. There are 2 models - | |
| - Gender Model has 6-layered architecture. Model is trained using 4-CNN, 2-Dense-Fully connected layer Dataset - https://www.kaggle.com/datasets/gpiosenka/gender-classification-from-an-image. Notebook link - https://github.com/jivaniyash/image_classifier_app/blob/main/colab-notebook/Gender_Classifier.ipynb | |
| - Digit Classifier Model has 3-layered architecture - trained using dataset - https://www.tensorflow.org/datasets/catalog/mnist. Notebook Link - https://github.com/jivaniyash/image_classifier_app/blob/main/colab-notebook/Digit_Classifier.ipynb | |
| ''' | |
| article="<p style='text-align: center'><a href='https://github.com/jivaniyash/image_classifier_app' target='_blank'>Link to Git Repository</a <p> There are 2 different classification tasks merged over single endpoint function. This practice should not be adpoted in real use -case scenarios. Try creating different endpoint for each classification. This project is just for learning purposes.</p>" | |
| def image_classifier(model_name, img): | |
| if model_name == "Gender Classifier": | |
| img_np = image.img_to_array(image.load_img(img, target_size=(64,64))) | |
| test_img = img_np.reshape((1, 64, 64, 3)) | |
| pipeline = models.load_model('./models/gender-classifier.keras') | |
| y = pipeline(test_img/255., training=False) # output prob between 0 to 1 , 0 indicates female & 1 indicates male | |
| prob = y.numpy()[0][0] | |
| predictions = [prob,1-prob] | |
| labels = ["Male", "Female"] | |
| return {labels[i]:float(predictions[i]) for i in range(len(labels))} | |
| if model_name == "Digit Classifier": | |
| img_np = image.img_to_array(image.load_img(img, target_size=(28,28)).convert('L')) | |
| test_img = img_np.reshape((1,28,28,1)) | |
| pipeline = models.load_model('./models/digit-classifier.keras') | |
| y = pipeline(test_img/255., training=False) # output list of 10 tensors | |
| labels = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] | |
| return {labels[i]:float(y[0][i]) for i in range(len(labels))} | |
| demo = gr.Interface(fn=image_classifier, | |
| inputs=[gr.Dropdown(["Gender Classifier","Digit Classifier"], label="Select Model to predict", info="models"), | |
| gr.Image(type='filepath')], | |
| outputs=gr.Label(num_top_classes=2), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[["Gender Classifier","./images/gender/male01.jpg"], | |
| ["Gender Classifier","./images/gender/female01.jpg"], | |
| ["Digit Classifier", "./images/digit/1.png"], | |
| ["Digit Classifier", "./images/digit/3.png"]]) | |
| demo.launch(share=True, debug=True) |