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from fastai.vision.all import * |
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from io import BytesIO |
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import requests |
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import streamlit as st |
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""" |
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# 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 |
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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。 |
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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。 |
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請上傳塑料污染、油污染、金屬廢棄物或乾淨海洋的圖片 |
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""" |
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def predict(img): |
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st.image(img, caption="Your image", use_column_width=True) |
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pred, key, probs = learn_inf.predict(img) |
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f""" |
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### Rediction result: {pred} |
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### Probability of {pred}: {probs[key].item()*100: .2f}% |
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""" |
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path = "./" |
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learn_inf = load_learner(path + "demo_model.pkl") |
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option = st.radio("", ["Upload Image", "Image URL"]) |
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if option == "Upload Image": |
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uploaded_file = st.file_uploader("Please upload an image.") |
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if uploaded_file is not None: |
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img = PILImage.create(uploaded_file) |
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predict(img) |
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else: |
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url = st.text_input("Please input a url.") |
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if url != "": |
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try: |
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response = requests.get(url) |
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pil_img = PILImage.create(BytesIO(response.content)) |
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predict(pil_img) |
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except: |
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st.text("Problem reading image from", url) |
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