from fastai.vision.all import *
from io import BytesIO
from PIL import Image
from utils.cam import grad_cam
from utils.tfms import AlbTransform, get_augs
import requests
import streamlit as st


"""
# Histopathologic Cancer Detection
## Is this an cancerous cell?
this is a web app to predict whether a cell is cancerous or not.
"""

# List of example image image paths
images = {
    "Image 1": "example/c2a8ef295e49b8012e5bc83917a305057eaa1932.tif",
    "Image 2": "example/87aed2dd32c7fe17c3af56878abd62eb5f37f925.tif",
    "Image 3": "example/4ba0d1c62230781533c2df1d95e5b4c8f5b650a3.tif",
    "Image 4": "example/a24ce148f6ffa7ef8eefb4efb12ebffe8dd700da.tif",
    "Image 5": "example/d2dd0de8e583a5475a07c2f92fa3f06c7fabcd42.tif",
    # add more images as needed
}


def predict(learn, img):
    img = PILImage.create(img)
    pred, key, probs = learn.predict(img)
    grad_img = grad_cam(learn, img, pred)
    
    col1, col2 = st.columns(2)
    with col1:
        st.image(img, caption='Original Image', use_column_width=True)
    
    with col2:
        st.image(grad_img, caption='Grad-CAM Image', use_column_width=True)
        
    # st.write(learn_inf.predict(img))

    f"""
    ## This **{'is ' if pred == '1' else 'is not'}** an cancerous cell.
    ### Rediction result: {pred}
    ### Probability of {pred}: {probs[key].item()*100: .2f}%
    """


path = "./"
learn_inf = load_learner(path + "resnet50.pkl")

option = st.radio("", ["Upload Image", "Image URL", "Download Example Image", "Example"])

if option == "Upload Image":
    uploaded_file = st.file_uploader("Please upload an image.")

    if uploaded_file is not None:
        predict(learn_inf, uploaded_file)

elif option == "Image URL":
    url = st.text_input("Please input a url.")

    if url != "":
        try:
            response = requests.get(url)
            pil_img = PILImage.create(BytesIO(response.content))
            predict(learn_inf, pil_img)

        except:
            st.text("Problem reading image from", url)
elif option == "Download Example Image":
    # Create columns for the images
    cols = st.columns(len(images))
    
    
    # Initialize selected_image in session state if it doesn't exist
    if 'selected_image' not in st.session_state:
        st.session_state['selected_image'] = None
    
    for i, (img_name, img_path) in enumerate(images.items()):
        # Open the image file
        img = Image.open(img_path)
    
        # Display the image in a column
        cols[i].image(img, caption=img_name, use_column_width=True)
    
        # Create a button for selecting the image
        if cols[i].button(f"Select {img_name}", key=img_name):
            st.session_state['selected_image'] = img_path
    
    # If an image has been selected, show the download button
    if st.session_state['selected_image'] is not None:
        # Open the selected image file
        img = Image.open(st.session_state['selected_image'])
    
        # Convert the image to a byte array
        img_byte_arr = io.BytesIO()
        img.save(img_byte_arr, format='PNG')
        img_byte_arr = img_byte_arr.getvalue()
    
        # Create the download button for the selected image
        st.download_button(
            label="Download selected image",
            data=img_byte_arr,
            file_name="selected_image.png",
            mime="image/png",
        ) 
else:
    option_1 = st.selectbox(
    'Example Prediction',
    ('Positive Image', 'Negative Image'))

    if option_1 == 'Positive Image':
        predict(learn_inf, img='example/c2a8ef295e49b8012e5bc83917a305057eaa1932.tif')
    elif option_1 == 'Negative Image':
        predict(learn_inf, img='example/87aed2dd32c7fe17c3af56878abd62eb5f37f925.tif')