Duplicate from dhhd255/parkinsons_docker

Co-authored-by: Jay <[email protected]>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

.streamlit/config.toml

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+[theme]
+primaryColor="#e5ab00"
+backgroundColor="#0e1117"
+secondaryBackgroundColor="#282929"
+textColor = "#ffffff"
+font="sans serif"
+
+[server]
+maxUploadSize = 200
+enableXsrfProtection = false

Dockerfile

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+FROM python:3.8.9
+
+WORKDIR /app
+
+COPY ./requirements.txt /app/requirements.txt
+COPY ./packages.txt /app/packages.txt
+
+RUN apt-get update && xargs -r -a /app/packages.txt apt-get install -y && rm -rf /var/lib/apt/lists/*
+RUN pip3 install --no-cache-dir -r /app/requirements.txt
+
+# User
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME /home/user
+ENV PATH $HOME/.local/bin:$PATH
+
+WORKDIR $HOME
+RUN mkdir app
+WORKDIR $HOME/app
+COPY . $HOME/app
+
+EXPOSE 8501
+CMD streamlit run app.py

README.md

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+---
+sdk: docker
+app_port: 8501
+duplicated_from: dhhd255/parkinsons_docker
+---

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/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)

requirements.txt

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+streamlit
+numpy
+pandas
+pillow
+torch
+transformers