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| import streamlit as st | |
| import pandas as pd | |
| from fastai import * | |
| from fastai.vision.all import * | |
| import pickle | |
| import pathlib | |
| header = st.container() | |
| inference = st.container() | |
| image_viewer = st.container() | |
| with header: | |
| st.title("Cuisine Classifier") | |
| st.text("Is your food Italian, French, Chinese, Indian, or Japanese?") | |
| with inference: | |
| plt = platform.system() | |
| if plt == 'Linux': | |
| pathlib.WindowsPath = pathlib.PosixPath | |
| if plt == 'Windows': | |
| pathlib.PosixPath = pathlib.WindowsPath | |
| path = Path() | |
| path.ls(file_exts='.pkl') | |
| learn_inf = load_learner(path/'export.pkl') | |
| st.header('Show me your food pic!') | |
| st.text("(I currently accept Italian, French, Chinese, Indian, or Japanese.)") | |
| uploaded_file = st.file_uploader("Show me your food pic!") | |
| if uploaded_file is not None: | |
| img = load_image(uploaded_file) | |
| #img = PILImage.create(uploaded_file) | |
| pred, pred_idx, probs = learn_inf.predict(img) | |
| prob_value = probs[pred_idx].item() | |
| rounded_prob_percentage = round(prob_value * 100) | |
| st.text(f"This is {pred}, isn't it? Believe me, I am {rounded_prob_percentage}% sure!") | |
| with image_viewer: | |
| st.header(f"Your food pic") | |
| st.image(image=img, caption='your pic will be shown here') |