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import os
import streamlit as st
import numpy as np
import PIL.Image
#from PIL import Image
from fastai.vision.all import *
import pathlib
import matplotlib.pyplot as pt
plt = platform.system()
if plt == 'Windows': pathlib.PosixPath = pathlib.WindowsPath
model = load_learner('ksl_model.pkl')
def predict(image_path):
# load the image and convert into
# numpy array
#image= Image.open(image)
# image = Image.open(image)
# PIL images into NumPy arrays
pred_label= model.predict(image_path)
return pred_label
# def show_likelihood(pred_label):
# class_probs = pred_label[9].numpy()
# classes = pred_label[9]
# class_labels = [classes[i] for i in range(len(class_probs))]
# fig = pt.figure(figsize=(10, 10))
# pt.barh(class_labels, class_probs)
# pt.ylabel("Class")
# pt.xlabel("Probability")
# pt.title("Class Probabilities")
# pt.xlim(0, 1)
# pt.ylim(-1, len(class_probs))
# st.pyplot(fig)
def main():
# Add an image to your app
image = "ksl1.jpg"
st.image(image)
st.write("# KSL Image Classification App")
st.write("This app allows you to upload a KSL image and have it classified by a pre-trained machine learning model into one of 9 classes.")
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
image = PIL.Image.open(uploaded_file)
image_path = os.path.join("tempDir",uploaded_file.name)
with open(image_path, "wb") as f:
f.write(uploaded_file.getbuffer())
st.image(image, caption="Uploaded Image", use_column_width=True)
pred_label = predict(image_path)
st.write("The image was classified as:", pred_label[0])
# show_likelihood(pred_label)
if __name__ == '__main__':
main()
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