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from fastai.vision.all import *
import gradio as gr
import timm
import dill
import os
is_catan_learn = load_learner('./models/catan-model-paperspace-2022-11-29-05-17-46.pkl', pickle_module=dill)
catan_category_learn = load_learner('./models/categories-of-catan-3.pkl', pickle_module=dill)
# learn = load_learner('catan-model.pkl', pickle_module=dill)
def classify_image(img):
pred, pred_idx, probs = is_catan_learn.predict(img)
if float(probs[1]) < 0.2:
# categories = learn.dls.vocab
categories = ('Not Catan', 'Catan')
message = f'Did not detect Catan in this upload: *{probs[1]:.4f}%*. Choose another photo with Catan in it and we will categorize what kind of Catan we find.'
details = dict(zip(categories, map(float, probs)))
else:
pred, pred_idx, probs = catan_category_learn.predict(img)
message = f'Prediction: *{pred}*; Probability: *{probs[pred_idx]:.04f}%*'
categories = catan_category_learn.dls.vocab
details = dict(zip(categories, map(float, probs)))
return details, message
# Cell
image = gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
description = gr.Markdown()
examples_dir_path = './examples/'
examples = [(examples_dir_path + filename) for filename in os.listdir(examples_dir_path) if filename[:1] != '.']
# Cell
intf = gr.Interface(fn=classify_image, inputs=image, outputs=[label, description], examples=examples)
intf.launch()
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