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| import streamlit as st | |
| from fastai.vision.all import * | |
| def is_cat(x): | |
| return x[0].isupper() | |
| model = load_learner('model.pkl') | |
| categories = ('Dog', 'Cat') | |
| def clasify_image(img): | |
| img = PILImage.create(img) | |
| pred,idx,probs = model.predict(img) | |
| return dict(zip(categories, map(float,probs))) | |
| def main(): | |
| st.title("Dog or Cat predictor") | |
| #The input is an image | |
| image = st.file_uploader("Upload an image", "jpg") | |
| # Display the image once it has been uploaded | |
| if image: | |
| disp = Image.open(image) | |
| st.image(disp, width=150) | |
| # Make the prediction | |
| if st.button("Predict", use_container_width=True): | |
| result = clasify_image(image) | |
| for key, value in result.items(): | |
| st.progress(value, f"Probabiity that its a {key} is {value:.15f}%") | |
| #There should be examples you can pick from to put into the input interface | |
| images = [ | |
| Image.open('photos/cat/001.jpg'), | |
| Image.open('photos/dog/006.jpg'), | |
| Image.open('photos/dog/007.jpg'), | |
| Image.open('photos/cat/003.jpg') | |
| ] | |
| col1, col2, col3, col4 = st.columns(4) | |
| col1.image(images[0], width=150) | |
| col2.image(images[1], width=150) | |
| col3.image(images[2], width=150) | |
| col4.image(images[3], width=150) | |
| if __name__ == "__main__": | |
| main() |