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