app.py

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, pred_idx, probs = learn_inf.predict(img)  # 修改此行，添加了 pred_idx
    # st.write(learn_inf.predict(img))

    f"""
    ## This leaf is affected by **{pred}**.
    ### Prediction result: {pred}
    ### Probability of {pred}: {probs[pred_idx].item() * 100:.2f}%
    """
st.title("Rice Leaf Disease Classifier")
st.write("Upload a rice leaf image to predict the disease.")

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 Exception as e:
            st.text("Problem reading image from " + url)
            st.text(str(e))