ppolly commited on
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  1. app.py +46 -0
  2. requirements.txt +2 -0
app.py ADDED
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+ from fastai.vision.all import *
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+ from io import BytesIO
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+ import requests
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+ import streamlit as st
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+
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+ """
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+ # HeartNet
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+ 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.
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+ """
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+
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+ def predict(img):
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+ st.image(img, caption="Your image", use_column_width=True)
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+ pred, key, probs = learn_inf.predict(img)
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+ # st.write(learn_inf.predict(img))
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+
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+ f"""
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+ ## This **{'is ' if pred == 'mi' else 'is not'}** an MI (heart attack).
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+ ### Rediction result: {pred}
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+ ### Probability of {pred}: {probs[key].item()*100: .2f}%
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+ """
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+
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+
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+ path = "./"
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+ learn_inf = load_learner(path + "demo_model.pkl")
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+
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+ option = st.radio("", ["Upload Image", "Image URL"])
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+
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+ if option == "Upload Image":
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+ uploaded_file = st.file_uploader("Please upload an image.")
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+
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+ if uploaded_file is not None:
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+ img = PILImage.create(uploaded_file)
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+ predict(img)
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+
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+ else:
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+ url = st.text_input("Please input a url.")
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+
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+ if url != "":
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+ try:
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+ response = requests.get(url)
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+ pil_img = PILImage.create(BytesIO(response.content))
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+ predict(pil_img)
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+
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+ except:
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+ st.text("Problem reading image from", url)
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+
requirements.txt ADDED
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+ fastbook
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+ altair<5