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app.py
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app.py
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import pathlib
from pathlib import Path
import streamlit as st
from fastai.vision.all import *
from fastai.vision.widgets import *
# for offline application
# temp = pathlib.PosixPath
# pathlib.PosixPath = pathlib.WindowsPath
learn_inf = load_learner('model.pkl')
class Predict:
def __init__(self, filename):
self.learn_inference = load_learner(Path()/filename)
self.img = self.get_image_from_upload()
if self.img is not None:
self.display_output()
self.get_prediction()
@staticmethod
def get_image_from_upload():
uploaded_file = st.file_uploader("Upload Files",type=['png','jpeg', 'jpg'])
if uploaded_file is not None:
return PILImage.create((uploaded_file))
return None
def display_output(self):
st.image(self.img.to_thumb(250,250), caption='Uploaded Image')
def get_prediction(self):
if st.button('Classify'):
pred, pred_idx, probs = self.learn_inference.predict(self.img)
st.write(f'**Prediction**: {pred}')
st.write(f'**Probability**: {probs[pred_idx]*100:.02f}%')
else:
st.write(f'Click the button to classify')
if __name__=='__main__':
file_name='model.pkl'
predictor = Predict(file_name)