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import gradio as gr
from fastai.vision.all import load_learner
from fastai import *
import torch
import os
from PIL import Image
model_path = 'model3-86%.pkl'
model = load_learner(model_path)
def result(path):
pred,_,probability = model.predict(path)
pred = str(pred)
pred = pred.upper()
return {pred: float(probability.max())}
path = 'test/'
image_path = []
for i in os.listdir(path):
image_path.append(path+i)
image = gr.inputs.Image(shape =(200,200),image_mode='L',invert_colors=False)
label = gr.outputs.Label()
iface = gr.Interface(fn=result, inputs=image, outputs=label, examples = image_path)
iface.launch(inline=False) |