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from nbdev.export import nb_export
nb_export(r"C:\Users\klath\Downloads\dogs-v-cats.ipynb", ".")
#| export
from fastai.vision.all import *
import gradio as gr
def is_cat(x): return x[0].isupper()
#| export
learn = load_learner('C:\Users\klath\Downloads\K.L\SAMFORD DRPH - SUMMER 2025\HIIM 661\model.pkl')
#| export
categories = ('Dog', 'Cat')
def classify_images(img):
#'Is it a car?', 'Is it a car? but as zero or one', 'probabillity of [dog, cat]'
pred, idx, probs = learn.predict(img)
#return dictionary
#zip together the categories and the
#turn probs to float
return dict(zip(categories, map(float, probs)))
#| export
image = gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
examples = ['C:\Users\klath\Downloads\K.L\SAMFORD DRPH - SUMMER 2025\HIIM 661\dog.jpg', 'C:\Users\klath\Downloads\K.L\SAMFORD DRPH - SUMMER 2025\HIIM 661\cat.jpg', 'C:\Users\klath\Downloads\K.L\SAMFORD DRPH - SUMMER 2025\HIIM 661\catdog.jpg', 'C:\Users\klath\Downloads\K.L\SAMFORD DRPH - SUMMER 2025\HIIM 661\he-s-a-catdog-or-dogcat.jpeg']
intf = gr.Interface(fn=classify_images, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False)
#| export
from fastai.vision.all import *
import gradio as gr
def is_cat(x): return x[0].isupper()
#| export
learn = load_learner('C:\Users\klath\Downloads\K.L\SAMFORD DRPH - SUMMER 2025\HIIM 661\model.pkl')
#| export
categories = ('Dog', 'Cat')
def classify_images(img):
#'Is it a car?', 'Is it a car? but as zero or one', 'probabillity of [dog, cat]'
pred, idx, probs = learn.predict(img)
#return dictionary
#zip together the categories and the
#turn probs to float
return dict(zip(categories, map(float, probs)))
#| export
image = gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
examples = ['C:\Users\klath\Downloads\K.L\SAMFORD DRPH - SUMMER 2025\HIIM 661\dog.jpg', 'C:\Users\klath\Downloads\K.L\SAMFORD DRPH - SUMMER 2025\HIIM 661\cat.jpg', 'C:\Users\klath\Downloads\K.L\SAMFORD DRPH - SUMMER 2025\HIIM 661\catdog.jpg', 'C:\Users\klath\Downloads\K.L\SAMFORD DRPH - SUMMER 2025\HIIM 661\he-s-a-catdog-or-dogcat.jpeg']
intf = gr.Interface(fn=classify_images, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False)
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