Upload 14 files
Browse files- .gitattributes +1 -0
- Dog_transfer_learning_NASNetLarge.keras +3 -0
- app.py +42 -0
- images/husky_1.jpg +0 -0
- images/husky_2.jpg +0 -0
- images/husky_3.jpg +0 -0
- images/pomeranian_1.jpg +0 -0
- images/pomeranian_2.jpg +0 -0
- images/pomeranian_3.jpg +0 -0
- images/rottwiler_1.jpg +0 -0
- images/rottwiler_2.jpg +0 -0
- images/rottwiler_3.jpg +0 -0
- images/shiba_1.jpg +0 -0
- images/shiba_2.jpg +0 -0
- images/shiba_3.jpg +0 -0
.gitattributes
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Dog_transfer_learning_NASNetLarge.keras filter=lfs diff=lfs merge=lfs -text
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Dog_transfer_learning_NASNetLarge.keras
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version https://git-lfs.github.com/spec/v1
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oid sha256:b800b240bd4e19b82660ac867a7ffffd9be63ec2d9b88a446002959ced9ad46a
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size 1021796704
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app.py
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import gradio as gr
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import tensorflow as tf
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import numpy as np
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from PIL import Image
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model_path = "Dog_transfer_learning_NASNetLarge.keras"
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model = tf.keras.models.load_model(model_path)
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# Define the core prediction function
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def predict_dog(image):
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# Preprocess image
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print(type(image))
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image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image
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image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale
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image = np.array(image)
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image = np.expand_dims(image, axis=0) # same as image[None, ...]
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# Predict
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prediction = model.predict(image)
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# No need to apply sigmoid, as the output layer already uses softmax
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# Convert the probabilities to rounded values
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prediction = np.round(prediction, 3)
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# Separate the probabilities for each class
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p_husky = prediction[0][0] # Probability for class 'articuno'
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p_pomeranian = prediction[0][1] # Probability for class 'moltres'
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p_rottwiler = prediction[0][2] # Probability for class 'zapdos'
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p_shiba = prediction[0][3] # Probability for class 'zapdos'
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return {'husky': p_husky, 'pomeranian': p_pomeranian, 'rottwiler': p_rottwiler, 'shiba': p_shiba}
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# Create the Gradio interface
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input_image = gr.Image()
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iface = gr.Interface(
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fn=predict_dog,
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inputs=input_image,
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outputs=gr.Label(),
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examples=["images/husky_1.jpg", "images/husky_2.jpg", "images/husky_3.jpg", "images/pomeranian_1.jpg", "images/pomeranian_2.jpg", "images/pomeranian_3.jpg", "images/rottwiler_1.jpg", "images/rottwiler_2.jpg", "images/rottwiler_3.jpg", "images/shiba_1.jpg", "images/shiba_2.jpg", "images/shiba_3.jpg"],
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description="TEST.")
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iface.launch()
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images/husky_1.jpg
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images/husky_2.jpg
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images/husky_3.jpg
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images/pomeranian_1.jpg
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images/pomeranian_2.jpg
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images/pomeranian_3.jpg
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images/rottwiler_1.jpg
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images/rottwiler_2.jpg
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images/rottwiler_3.jpg
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images/shiba_1.jpg
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images/shiba_2.jpg
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images/shiba_3.jpg
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