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import torch
from transformers import AutoModel
import torch.nn as nn
from PIL import Image
import numpy as np
import streamlit as st
# Set the device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Load the trained model from the Hugging Face Hub
model = AutoModel.from_pretrained('dhhd255/EfficientNet_ParkinsonsPred')
# Move the model to the device
model = model.to(device)
# Add custom CSS to use the Inter font, define custom classes for healthy and parkinsons results, increase the font size, make the text bold, and define the footer styles
st.markdown("""
<style>
@import url('https://fonts.googleapis.com/css2?family=Inter&display=swap');
body {
font-family: 'Inter', sans-serif;
}
.result {
font-size: 24px;
font-weight: bold;
}
.healthy {
color: #007E3F;
}
.parkinsons {
color: #C30000;
}
.social-links {
display: flex;
text-decoration:none;
justify-content: center;
}
.social-links a {
text-decoration:none;
padding: 0 10px;
}
</style>
""", unsafe_allow_html=True)
st.title("Parkinson's Disease Prediction")
uploaded_file = st.file_uploader("Upload your :blue[Spiral] drawing here", type=["png", "jpg", "jpeg"])
st.empty()
if uploaded_file is not None:
col1, col2 = st.columns(2)
# Load and resize the image
image_size = (224, 224)
new_image = Image.open(uploaded_file).convert('RGB').resize(image_size)
col1.image(new_image, width=255)
new_image = np.array(new_image)
new_image = torch.from_numpy(new_image).transpose(0, 2).float().unsqueeze(0)
# Move the data to the device
new_image = new_image.to(device)
# Make predictions using the trained model
with torch.no_grad():
predictions = model(new_image)
logits = predictions.last_hidden_state
logits = logits.view(logits.shape[0], -1)
num_classes=2
feature_reducer = nn.Linear(logits.shape[1], num_classes)
logits = logits.to(device)
feature_reducer = feature_reducer.to(device)
logits = feature_reducer(logits)
predicted_class = torch.argmax(logits, dim=1).item()
confidence = torch.softmax(logits, dim=1)[0][predicted_class].item()
if(predicted_class == 0):
col2.markdown('<span class="result parkinsons">Predicted class: Parkinson\'s</span>', unsafe_allow_html=True)
col2.caption(f'{confidence*100:.0f}% sure')
else:
col2.markdown('<span class="result healthy">Predicted class: Healthy</span>', unsafe_allow_html=True)
col2.caption(f'{confidence*100:.0f}% sure')
uploaded_file = st.file_uploader("Upload your :blue[Wave] drawing here", type=["png", "jpg", "jpeg"])
st.divider()
st.markdown("""
<div class="social-links">
<a href="https://twitter.com/your_twitter_handle">Twitter</a>
<a href="https://facebook.com/your_facebook_page">Facebook</a>
<a href="https://instagram.com/your_instagram_handle">Instagram</a>
</div>
""", unsafe_allow_html=True)
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