Upload 3 files
Browse files- app.py +30 -0
- mein_modell.h5 +3 -0
- requirements.txt +19 -0
app.py
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import gradio as gr
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from PIL import Image
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import numpy as np
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from tensorflow.keras.preprocessing import image as keras_image
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from tensorflow.keras.applications.resnet50 import preprocess_input
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from tensorflow.keras.models import load_model
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# Load your trained model
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model = load_model('C:\Users\Kevin Keo\OneDrive - ZHAW\Dokumente\Desktop\ZHAW WIN\FS24\KI-Anwendung\Pokemon\Mandatory Sauber\Mandatory Sauber\mein_modell.h5') # Ensure this path is correct
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def predict_pokemon(img):
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img = Image.fromarray(img.astype('uint8'), 'RGB') # Ensure the image is in RGB
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img = img.resize((224, 224)) # Resize the image properly using PIL
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img_array = keras_image.img_to_array(img) # Convert the image to an array
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img_array = np.expand_dims(img_array, axis=0) # Expand dimensions to fit model input
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img_array = preprocess_input(img_array) # Preprocess the input as expected by ResNet50
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prediction = model.predict(img_array) # Predict using the model
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classes = ['Butterfree', 'Cubone', 'Dratini' ] # Specific Pokémon names
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return {classes[i]: float(prediction[0][i]) for i in range(3)} # Return the prediction
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# Define Gradio interface
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interface = gr.Interface(fn=predict_pokemon,
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inputs="image", # Simplified input type
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outputs="label", # Simplified output type
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title="Pokémon Classifier",
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description="Upload an image of a Pokémon and the classifier will predict its species.")
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# Launch the interface
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interface.launch()
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mein_modell.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:7f95d83c6b5aa515070432d124f46c6c7e8c13f9c1e86a9b87c221d5872b59df
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size 308621824
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requirements.txt
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blinker==1.7.0
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click==8.1.7
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Flask==3.0.2
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Flask-Cors==4.0.0
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itsdangerous==2.1.2
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Jinja2==3.1.3
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joblib==1.3.2
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MarkupSafe==2.1.5
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numpy==1.26.4
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pandas==2.2.1
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python-dateutil==2.8.2
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pytz==2024.1
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scikit-learn==1.4.1.post1
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scipy==1.12.0
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six==1.16.0
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threadpoolctl==3.3.0
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tzdata==2024.1
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Werkzeug==3.0.1
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tensorflow==2.16.1
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