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)