Create app.py

app.py

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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/parkinsons_pred0.1')
+
+# 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;
+ }
+ .caption_c{
+  position: relative;
+  display: flex;
+  flex-directon: column;
+  align-items: center;
+  top: calc(99vh - 370px);
+ }
+ .caption {
+  
+ text-align: center;
+ color: #646464;
+ font-size: 14px;
+ }
+  button:hover {
+ background-color: lightblue !important;
+ outline-color: lightblue !important;
+ }
+ button:focus {
+ background-color: lightblue !important;
+ outline-color: lightblue !important;
+ }
+ </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"])
+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, use_column_width=True)
+    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')
+
+# Add a caption at the bottom of the page
+st.markdown('<div class="caption_c"><p class="caption">Made with love by Jayant</p></div>', unsafe_allow_html=True)