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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 cordana' if pred == 'cordana' else 'is pestalotiopsis' if pred == 'pestalotiopsis' else 'is sigatoka' if pred == 'sigatoka' else 'is healthy'}**.
### Prediction result: {pred}
### Probability of {pred}: {probs[key].item()*100: .2f}%
"""
img1 = Image.open('/home/user/app/img1.jpg')
img2 = Image.open('/home/user/app/img2.jpg')
img3 = Image.open('/home/user/app/img3.jpg')
col1, col2, col3 = st.columns(3)
with col1:
st.image(img1, caption="Image 1", use_column_width=True)
with col2:
st.image(img2, caption="Image 2", use_column_width=True)
with col3:
st.image(img3, caption="Image 3", use_column_width=True)
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)
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