app.py

import streamlit as st
import numpy as np
from PIL import Image
from fastai.vision.all import *
import fastbook
fastbook.setup_book()


# Read list of non venomous
with open('./arizona_non-venomous_snakes.txt', 'r') as file:
    non_ven = file.readlines()  
non_ven = [i.strip() for i in non_ven]

# Read list of venomous
with open('./arizona_venomous_snakes.txt', 'r') as file:
    ven = file.readlines()    
ven = [i.strip() for i in ven]

# Create UI
st.markdown('<h2><center>Arizona Snake Classifier</center></h2>', unsafe_allow_html=True)  # or try st.header()
uploaded_file = st.file_uploader("Choose an image file of an Arizona snake")

if uploaded_file is not None:
    
    img_path = Path(uploaded_file.name)
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded image')
    img_array = np.asarray(image)  # convert to array for learner

    model = load_learner('arizona_snake_classifier.pkl')
    # pred,pred_idx,probs = model.predict(img_array)
    # st.write(f'This is a: {pred} snake.')
    # st.write(f'Probability: {probs[pred_idx]:.4f}')

    # get prediction
    pred,pred_idx,probs = model.predict(img_array)
    # st.write(f'Prediction: {pred}, Probability: {probs[pred_idx]:.04f}')
 
    add = ''
    if pred[0] == 'A' or 'a':
        add = 'n'
    
    # output
    st.write(f'The model thinks this is a{add} {pred} with probability: {probs[pred_idx]:.4f}')
    
    if pred in ven:
        st.write(f'A{add} {pred} is a VENOMOUS snake')
    elif pred in non_ven:
        st.write(f'\nA{add} {pred} is a non-venomous snake')
    else:
        st.write('Unknown if this is venomous')

st.write('Have a great day!')