| | from fastai.vision.all import * |
| | from io import BytesIO |
| | import requests |
| | import streamlit as st |
| |
|
| | """ |
| | # HeartNet |
| | This is a classifier for images of 12-lead EKGs. It will attempt to detect whether the EKG indicates an acute MI. It was trained on simulated images. |
| | """ |
| |
|
| | def predict(img): |
| | st.image(img, caption="Your image", use_column_width=True) |
| | pred, key, probs = learn_inf.predict(img) |
| | |
| |
|
| | f""" |
| | ## This **{'is ' if pred == 'mi' else 'is not'}** an MI (heart attack). |
| | ### Rediction result: {pred} |
| | ### Probability of {pred}: {probs[key].item()*100: .2f}% |
| | """ |
| |
|
| |
|
| | path = "./" |
| | learn_inf = load_learner(path + "demo_model.pkl") |
| |
|
| | option = st.radio("", ["Upload Image", "Image URL"]) |
| |
|
| | if option == "Upload Image": |
| | uploaded_file = st.file_uploader("Please upload an image.") |
| |
|
| | if uploaded_file is not None: |
| | img = PILImage.create(uploaded_file) |
| | predict(img) |
| |
|
| | else: |
| | url = st.text_input("Please input a url.") |
| |
|
| | if url != "": |
| | try: |
| | response = requests.get(url) |
| | pil_img = PILImage.create(BytesIO(response.content)) |
| | predict(pil_img) |
| |
|
| | except: |
| | st.text("Problem reading image from", url) |
| | |
