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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+chrome.exe filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,46 @@
+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)
+    # st.write(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)
+            

chrome.exe

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:453db7bcc09fc1264a07953975cf87c58eacb3880a5c49d1e0aaa06919e79d0c
+size 3133720