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  1. app.py +73 -0
  2. bear_classifier.pkl +3 -0
  3. black.jpg +0 -0
  4. cat.jpg +0 -0
  5. cat_dog.pkl +3 -0
  6. dog.jpg +0 -0
  7. grizzly.jpg +0 -0
  8. requirements.txt +3 -0
  9. teddy.jpg +0 -0
app.py ADDED
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+ # AUTOGENERATED! DO NOT EDIT! File to edit: ../drive/MyDrive/Codici/Python/Apps/Gradio_App/Image Classification/simlpe_image_models.ipynb.
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+
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+ # %% auto 0
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+ __all__ = ['cat_dog_model', 'bear_model', 'input_model', 'input_image', 'output_label', 'bear_examples', 'cat_examples',
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+ 'examples', 'intf', 'get_model', 'classify_image']
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+
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+ # %% ../drive/MyDrive/Codici/Python/Apps/Gradio_App/Image Classification/simlpe_image_models.ipynb 2
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+ !pip install -Uqq fastai
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+ !pip install -Uqq fastbook
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+ !pip install gradio
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+
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+ # %% ../drive/MyDrive/Codici/Python/Apps/Gradio_App/Image Classification/simlpe_image_models.ipynb 3
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+ import fastbook
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+ # fastbook.setup_book()
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+ from fastbook import *
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+ from fastai.vision.widgets import *
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+ from fastai.vision.all import *
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+ import gradio as gr
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+
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+ # %% ../drive/MyDrive/Codici/Python/Apps/Gradio_App/Image Classification/simlpe_image_models.ipynb 4
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+ cat_dog_model = load_learner('cat_dog.pkl')
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+ bear_model = load_learner("bear_classifier.pkl")
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+
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+ # %% ../drive/MyDrive/Codici/Python/Apps/Gradio_App/Image Classification/simlpe_image_models.ipynb 7
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+ # Define a function to load the appropriate model
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+ def get_model(model_name):
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+ if model_name == 'Cat vs Dog Model':
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+ return cat_dog_model
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+ elif model_name == 'Bear Model':
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+ return bear_model
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+ else:
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+ raise ValueError("Model not found")
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+
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+ # Classification Function
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+ def classify_image(model_name, img):
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+ model = get_model(model_name)
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+ categories = model.dls.vocab
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+ pred, idx, probs = model.predict(img)
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+ return dict(zip(categories, map(float, probs)))
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+
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+ # %% ../drive/MyDrive/Codici/Python/Apps/Gradio_App/Image Classification/simlpe_image_models.ipynb 10
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+ #INPUTS
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+ input_model = gr.Dropdown(['Cat vs Dog Model', 'Bear Model'])
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+ input_image = gr.components.Image(width=192, height=192)
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+
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+ #OUTPUT
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+ output_label = gr.components.Label()
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+
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+ #EXAMPLES
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+ bear_examples = ['black.jpg', 'grizzly.jpg', 'teddy.jpg']
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+ cat_examples = ['cat.jpg', 'dog.jpg']
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+
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+ examples = [
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+ ['Bear Model',bear_examples[0]], # added model name
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+ ['Bear Model',bear_examples[1]], # added model name
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+ ['Bear Model',bear_examples[2]], # added model name
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+ ['Cat vs Dog Model', cat_examples[0]], # added model name
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+ ['Cat vs Dog Model', cat_examples[1]], # added model name
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+ ]
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+
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+ #INTERFACE
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+ intf = gr.Interface(fn=classify_image,
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+ inputs=[
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+ input_model,
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+ input_image],
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+ outputs=output_label,
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+ examples=examples,
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+ theme=gr.themes.Ocean())
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+
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+ #LAUNCH APP
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+ intf.launch(inline=False,
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+ # share=True
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+ )
bear_classifier.pkl ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:9d8c34f0e2837c4c29df2c35f55b47a31c3c81b21cea2261046b8aa3742998f6
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+ size 46979320
black.jpg ADDED
cat.jpg ADDED
cat_dog.pkl ADDED
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+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:77ef29713bc84552a98d2db2faf48b75c4ab5db6303a4b7ce54a6ace7d4dc030
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+ size 46974608
dog.jpg ADDED
grizzly.jpg ADDED
requirements.txt ADDED
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+ fastai==2.7.19
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+ gradio==5.21.0
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+ gradio_client==1.7.2
teddy.jpg ADDED