app.py

import base64
import requests
import constants
from PIL import Image
import streamlit as st

API_URL = "https://arpy8-plant-detection-api.hf.space/predict"


# def predict_class(image_path):
#     original_image = Image.open(image_path)

#     preprocessed_image = original_image.resize((256, 256))
#     preprocessed_image = np.array(preprocessed_image) / 255.0

#     preds = model.predict(np.expand_dims(preprocessed_image, axis = 0))
#     labels = ['Healthy', 'Powdery', 'Rust']

#     preds_class = np.argmax(preds)
#     preds_label = labels[preds_class]

#     return preds_label, round(preds[0][preds_class], 2)


def predict_class_from_api(image_path):
    files = {'file': image_path}
    headers = {'accept': 'application/json'}

    try:
        response = requests.post(API_URL, headers=headers, files=files)
        response.raise_for_status()
        result = response.json()
        
        return result
        
    except requests.exceptions.HTTPError as err:
        print(err)
        return "Error", 0.0

################## STREAMLIT APP ##################

@st.cache_data()
def get_base64_of_bin_file(bin_file):
    with open(bin_file, 'rb') as f:
        data = f.read()
    return base64.b64encode(data).decode()

def set_png_as_page_bg(png_file):
    bin_str = get_base64_of_bin_file(png_file)
    page_bg_img = '''
    <style>
    body {
    background-image: url("data:image/png;base64,%s") !important;
    background-size: cover !important;
    }
    </style>
    ''' % bin_str
    
    st.markdown(page_bg_img, unsafe_allow_html=True)
    return

set_png_as_page_bg('assets/bg.webp')

st.write("<h1>Plant Disease Detection using CNN</h1>", unsafe_allow_html=True)

with st.sidebar:
    st.image("assets/logo.png")
    
    uploaded_file = st.file_uploader("Upload an image", type = ['png', 'jpg', 'jpeg'])
    
    st.info("""
            # About
            This is a web app to predict the disease in a plant using Convolutional Neural Network (CNN).
            
            # Contact
            For inquiries, you can mail us [here](mailto:arpitsengar99@gmail.com).
            """)

home_page = st.empty()
home_page.write(constants.FRONT_PAGE, unsafe_allow_html=True)

display_image = st.empty()
classifying_text = st.empty()
button = st.empty()    

if uploaded_file is not None:
    home_page.empty()
    display_image.image(uploaded_file, width=450)
    
    predict_button = button.button("Predict", use_container_width=True)

    if predict_button and uploaded_file is not None: 
        classifying_text.empty()
        button.empty()
        with st.spinner("Predicting..."):
            label = predict_class_from_api(uploaded_file)

        st.toast("Prediction Complete!")
        st.info(f"""
##### Predicted Class:
{label}

##### Description: 
{constants.DISEASE_DESCRIPTION[label]}
        """)

    classifying_text.empty()