Delete Untitled-1.py
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Untitled-1.py
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# %%
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import gradio as gr
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import numpy as np
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from tensorflow.keras.models import load_model
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from tensorflow.keras.applications.resnet50 import preprocess_input
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from PIL import Image
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# Load the pre-trained Keras model
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model = load_model('pokemon-model.keras')
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# Define the class labels
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class_labels = ['Bulbasaur', 'Glumanda', 'Pikachu'] # Ensure this matches the training order
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# Define the image processing and prediction function
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def predict_image(img):
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# Ensure the image is a PIL image
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if not isinstance(img, Image.Image):
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img = Image.fromarray(img)
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# Resize the image to the size expected by ResNet50
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img = img.resize((224, 224))
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# Convert the image to a numpy array
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img_array = np.array(img)
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# Convert the image array to a batch of size 1 (1, 224, 224, 3)
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img_array = np.expand_dims(img_array, axis=0)
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# Preprocess the image array using ResNet50's preprocessing
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img_array = preprocess_input(img_array)
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# Make prediction
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prediction = model.predict(img_array)
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# Get the label with the highest probability
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predicted_index = int(np.argmax(prediction))
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predicted_label = class_labels[predicted_index]
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return predicted_label
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# Create the Gradio interface with multiple examples
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iface = gr.Interface(
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fn=predict_image,
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inputs=gr.Image(image_mode='RGB'),
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outputs='label',
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examples=[['00000015.jpg'], ['20.png'], ['glumanda.jpg'], ['j67j7.png'], ['pikachu.jpg']],
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title="Pokémon Classification",
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description="Upload an image of a Pokémon to classify it using the pre-trained model."
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)
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# Launch the interface inline in the Jupyter Notebook
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iface.launch(inline=True)
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# %%
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# Print model summary to verify input shape
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print(model.summary())
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