from fastai.vision.all import *
from io import BytesIO
import requests
import streamlit as st

"""
# 使用卷積神經網路分類海洋污染影像以支持水下生物保護
此模型使用 ResNet34 卷積神經網路來分類海洋污染影像，識別四種類別：塑料污染、油污染、金屬廢棄物和乾淨海洋。
模型支持聯合國可持續發展目標（SDG）中的“水下生物”目標，旨在識別和減少海洋污染，保護海洋生態系統。
請上傳塑料污染、油污染、金屬廢棄物或乾淨海洋的圖片
"""

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"""
    ### 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)