Upload 11 files
Browse files- .gitattributes +1 -0
- app.py +42 -0
- images/char1.png +0 -0
- images/char2.png +0 -0
- images/char3.png +0 -0
- images/mew1.jpg +0 -0
- images/mew2.jpg +0 -0
- images/mew3.jpg +0 -0
- images/squi1.jpg +0 -0
- images/squi2.png +0 -0
- images/squi3.png +0 -0
- pokemon_transferlearning.keras +3 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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pokemon_transferlearning.keras filter=lfs diff=lfs merge=lfs -text
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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 = "pokemon_transferlearning.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_pokemon(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, 2)
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# Separate the probabilities for each class
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p_charmander = prediction[0][0] # Probability for class 'articuno'
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p_mewtwo = prediction[0][1] # Probability for class 'moltres'
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p_squirtle = prediction[0][2] # Probability for class 'zapdos'
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return {'charmander': p_charmander, 'mewtwo': p_mewtwo, 'squirtle': p_squirtle}
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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_pokemon,
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inputs=input_image,
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outputs=gr.Label(),
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examples=["images/char1.png", "images/char2.png", "images/char3.png", "images/mew1.jpg", "images/mew2.jpg", "images/mew3.jpg", "images/squi1.jpg", "images/squi2.png", "images/squi3.png"],
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description="TEST.")
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iface.launch()
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images/char1.png
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images/char2.png
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images/char3.png
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images/mew1.jpg
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images/mew2.jpg
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images/mew3.jpg
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images/squi1.jpg
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images/squi2.png
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images/squi3.png
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pokemon_transferlearning.keras
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version https://git-lfs.github.com/spec/v1
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oid sha256:3047e9228f99f9b2b0dc8487cdc6f1e5d5252b6b096bcd5d24273e326a61e34b
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size 250560238
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