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import streamlit as st | |
import tensorflow as tf | |
from PIL import Image | |
import numpy as np | |
# from streamlit_extras import open_browser | |
def load_and_prep_image(image, img_shape=224): | |
img = Image.open(image) | |
img = img.resize((img_shape, img_shape)) | |
img = np.array(img) / 255.0 | |
return img | |
def pksn(img_path): | |
class_names = ['healthy', 'parkinson'] | |
loaded_model = tf.keras.models.load_model("parkinson.h5") | |
img = load_and_prep_image(img_path) | |
img = np.expand_dims(img, axis=0) | |
pred = loaded_model.predict(img) | |
pred_class = class_names[int(tf.round(pred))] | |
return 0 if pred_class == "healthy" else 1 | |
st.title("Medverse AI") | |
# if st.sidebar.button('Return to Home Page'): | |
# open_browser("https://pmp438.pythonanywhere.com/") | |
uploaded_file = st.file_uploader("Choose an image...", type="jpg") | |
if uploaded_file is not None: | |
# Display uploaded image | |
image = Image.open(uploaded_file) | |
st.image(image, caption='Uploaded Image', use_column_width=False, width=150) | |
# Save uploaded image to a temporary path | |
img_path = "temp_image.jpg" | |
with open(img_path, "wb") as f: | |
f.write(uploaded_file.getbuffer()) | |
st.write("Classifying...") | |
prediction = pksn(img_path) | |
dic = {0: "Healthy", 1: "Patient"} | |
st.write(f"Prediction: {dic[prediction]}") | |
st.markdown( | |
""" | |
<div style="text-align: center; padding-top: 20px;"> | |
<a href="https://pmp438.pythonanywhere.com/" target="_blank"> | |
<button style="padding: 10px 20px; font-size: 16px; cursor: pointer;">Return to Home Page</button> | |
</a> | |
</div> | |
""", | |
unsafe_allow_html=True | |
) | |