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)