#|default_exp app
# %% [code] {"execution":{"iopub.status.busy":"2023-01-17T14:41:33.341222Z","iopub.execute_input":"2023-01-17T14:41:33.341652Z","iopub.status.idle":"2023-01-17T14:41:34.776787Z","shell.execute_reply.started":"2023-01-17T14:41:33.341595Z","shell.execute_reply":"2023-01-17T14:41:34.775663Z"}}
#|export
from fastai.vision.all import *
import gradio as gr

# %% [code] {"execution":{"iopub.status.busy":"2023-01-17T14:43:57.890571Z","iopub.execute_input":"2023-01-17T14:43:57.891069Z","iopub.status.idle":"2023-01-17T14:43:58.607922Z","shell.execute_reply.started":"2023-01-17T14:43:57.891028Z","shell.execute_reply":"2023-01-17T14:43:58.606710Z"}}
#|export
learn = load_learner('/minima/model.pkl')

# %% [code] {"execution":{"iopub.status.busy":"2023-01-17T14:45:36.880658Z","iopub.execute_input":"2023-01-17T14:45:36.881241Z","iopub.status.idle":"2023-01-17T14:45:36.890302Z","shell.execute_reply.started":"2023-01-17T14:45:36.881102Z","shell.execute_reply":"2023-01-17T14:45:36.889172Z"}}
labels = learn.dls.vocab
def predict(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

# %% [code] {"execution":{"iopub.status.busy":"2023-01-17T14:46:08.717339Z","iopub.execute_input":"2023-01-17T14:46:08.717913Z","iopub.status.idle":"2023-01-17T14:46:15.759750Z","shell.execute_reply.started":"2023-01-17T14:46:08.717858Z","shell.execute_reply":"2023-01-17T14:46:15.758401Z"}}
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)