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Dockerfile.txt

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+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /code
+
+# Install OS-level dependencies
+RUN apt-get update && apt-get install -y git && rm -rf /var/lib/apt/lists/*
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app files
+COPY . .
+
+# Expose the port
+EXPOSE 7860
+
+# Run the Flask app
+CMD ["python", "server.py"]

requirements.txt

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+torch
+transformers
+flask
+flask-cors
+pillow

server.py

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+import torch
+import torch.nn as nn
+from flask import Flask, request, jsonify, render_template
+from flask_cors import CORS
+import io
+import os
+from PIL import Image
+from transformers import CLIPProcessor, CLIPModel
+import numpy as np
+
+# Define the MIDM model
+class MIDM(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(MIDM, self).__init__()
+        self.fc1 = nn.Linear(input_dim, hidden_dim)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_dim, output_dim)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.relu(out)
+        out = self.fc2(out)
+        out = self.sigmoid(out)
+        return out
+
+app = Flask(__name__, static_folder='static', template_folder='templates')
+CORS(app)
+
+# Load models once when the app starts to avoid reloading for each request
+processor = None
+clip_model = None
+model = None
+
+def load_models():
+    global processor, clip_model, model
+    
+    # Load CLIP model and processor
+    processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+    clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+    
+    # Initialize MIDM model
+    input_dim = 10  # Using first 10 features as in your notebook
+    hidden_dim = 64
+    output_dim = 1
+    model = MIDM(input_dim, hidden_dim, output_dim)
+    
+    # For a real application, you would load your trained weights here
+    # model.load_state_dict(torch.load('path/to/your/model.pth'))
+    model.eval()
+
+# Function to get image features using CLIP
+def get_image_features(image):
+    """
+    Extracts image features using the CLIP model.
+    """
+    # Preprocess the image and get features
+    inputs = processor(images=image, return_tensors="pt")
+    # Only use the image encoder to get the image features
+    with torch.no_grad():
+        image_features = clip_model.get_image_features(**inputs)
+    return image_features
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/api/check-membership', methods=['POST'])
+def check_membership():
+    # Ensure models are loaded
+    if processor is None or clip_model is None or model is None:
+        load_models()
+        
+    if 'image' not in request.files:
+        return jsonify({'error': 'No image found in request'}), 400
+    
+    try:
+        # Get the image from the request
+        file = request.files['image']
+        image_bytes = file.read()
+        image = Image.open(io.BytesIO(image_bytes))
+        
+        # Get image features using CLIP
+        image_features = get_image_features(image)
+        
+        # Preprocess the features for MIDM model
+        processed_features = image_features.reshape(1, -1)[:, :10]  # Select first 10 features
+        
+        # Perform inference
+        with torch.no_grad():
+            output = model(processed_features)
+            probability = output.item()
+            predicted = int(output > 0.5)
+            
+        return jsonify({
+            'probability': probability,
+            'predicted_class': predicted,
+            'message': f"Predicted membership probability: {probability}",
+            'is_in_training_data': "Likely" if predicted == 1 else "Unlikely"
+        })
+        
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    port = int(os.environ.get('PORT', 7860))
+    app.run(host='0.0.0.0', port=port)

static/script.js

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+document.addEventListener('DOMContentLoaded', function() {
+    const imageUpload = document.getElementById('image-upload');
+    const previewContainer = document.getElementById('preview-container');
+    const imagePreview = document.getElementById('image-preview');
+    const uploadPlaceholder = document.getElementById('upload-placeholder');
+    const submitButton = document.getElementById('submit-button');
+    const uploadForm = document.getElementById('upload-form');
+    const errorMessage = document.getElementById('error-message');
+    const resultContainer = document.getElementById('result-container');
+    const resultMessage = document.getElementById('result-message');
+    const membershipStatus = document.getElementById('membership-status');
+    const probabilityFill = document.getElementById('probability-fill');
+    const probabilityText = document.getElementById('probability-text');
+    const loading = document.getElementById('loading');
+    
+    let selectedFile = null;
+    
+    // Handle image selection
+    imageUpload.addEventListener('change', function(e) {
+        selectedFile = e.target.files[0];
+        
+        if (selectedFile) {
+            const reader = new FileReader();
+            
+            reader.onload = function(e) {
+                imagePreview.src = e.target.result;
+                previewContainer.classList.remove('hidden');
+                uploadPlaceholder.classList.add('hidden');
+                submitButton.disabled = false;
+                errorMessage.classList.add('hidden');
+                resultContainer.classList.add('hidden');
+            };
+            
+            reader.readAsDataURL(selectedFile);
+        }
+    });
+    
+    // Handle form submission
+    uploadForm.addEventListener('submit', function(e) {
+        e.preventDefault();
+        
+        if (!selectedFile) {
+            errorMessage.textContent = 'Please select an image first';
+            errorMessage.classList.remove('hidden');
+            return;
+        }
+        
+        // Show loading indicator
+        loading.classList.remove('hidden');
+        submitButton.disabled = true;
+        errorMessage.classList.add('hidden');
+        resultContainer.classList.add('hidden');
+        
+        const formData = new FormData();
+        formData.append('image', selectedFile);
+        
+        fetch('/api/check-membership', {
+            method: 'POST',
+            body: formData
+        })
+        .then(response => {
+            if (!response.ok) {
+                throw new Error(`Server responded with ${response.status}`);
+            }
+            return response.json();
+        })
+        .then(data => {
+            // Display results
+            resultMessage.textContent = data.message;
+            membershipStatus.innerHTML = `This image is <strong>${data.is_in_training_data}</strong> in the model's training data.`;
+            
+            const probability = data.probability * 100;
+            probabilityFill.style.width = `${probability}%`;
+            probabilityText.textContent = `${probability.toFixed(2)}%`;
+            
+            resultContainer.classList.remove('hidden');
+        })
+        .catch(error => {
+            errorMessage.textContent = `Error: ${error.message}`;
+            errorMessage.classList.remove('hidden');
+        })
+        .finally(() => {
+            loading.classList.add('hidden');
+            submitButton.disabled = false;
+        });
+    });
+});

static/style.css

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+* {
+    box-sizing: border-box;
+    margin: 0;
+    padding: 0;
+}
+
+body {
+    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
+    line-height: 1.6;
+    color: #333;
+    background-color: #f5f5f5;
+}
+
+.app {
+    max-width: 800px;
+    margin: 0 auto;
+    padding: 20px;
+}
+
+.app-header {
+    text-align: center;
+    margin-bottom: 30px;
+}
+
+.app-header h1 {
+    margin-bottom: 10px;
+    color: #2c3e50;
+}
+
+.upload-container {
+    margin-bottom: 20px;
+}
+
+.upload-label {
+    display: block;
+    cursor: pointer;
+}
+
+.upload-placeholder {
+    border: 2px dashed #ccc;
+    border-radius: 8px;
+    padding: 60px;
+    text-align: center;
+    color: #777;
+    transition: all 0.3s ease;
+}
+
+.upload-placeholder:hover {
+    border-color: #4CAF50;
+    color: #4CAF50;
+}
+
+.image-preview {
+    max-width: 100%;
+    max-height: 300px;
+    border-radius: 8px;
+    display: block;
+    margin: 0 auto;
+}
+
+.file-input {
+    display: none;
+}
+
+.submit-button {
+    display: block;
+    width: 100%;
+    padding: 12px;
+    background-color: #4CAF50;
+    color: white;
+    border: none;
+    border-radius: 4px;
+    font-size: 16px;
+    cursor: pointer;
+    margin-bottom: 20px;
+    transition: background-color 0.3s;
+}
+
+.submit-button:hover {
+    background-color: #45a049;
+}
+
+.submit-button:disabled {
+    background-color: #cccccc;
+    cursor: not-allowed;
+}
+
+.error-message {
+    color: #e74c3c;
+    margin-bottom: 20px;
+    padding: 10px;
+    background-color: #fadbd8;
+    border-radius: 4px;
+}
+
+.result-container {
+    background-color: #f9f9f9;
+    border-radius: 8px;
+    padding: 20px;
+    margin-top: 20px;
+    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+}
+
+.result-container h2 {
+    margin-bottom: 15px;
+    color: #2c3e50;
+}
+
+.result-message {
+    margin-bottom: 15px;
+}
+
+.membership-status {
+    font-size: 18px;
+    margin-bottom: 15px;
+}
+
+.probability-bar {
+    height: 24px;
+    background-color: #e0e0e0;
+    border-radius: 12px;
+    position: relative;
+    overflow: hidden;
+    margin-top: 15px;
+}
+
+.probability-fill {
+    height: 100%;
+    background-color: #4CAF50;
+    transition: width 0.3s ease;
+    width: 0%;
+}
+
+.probability-text {
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    color: black;
+    font-weight: bold;
+}
+
+.loading {
+    text-align: center;
+    margin: 20px 0;
+}
+
+.spinner {
+    border: 4px solid #f3f3f3;
+    border-top: 4px solid #4CAF50;
+    border-radius: 50%;
+    width: 40px;
+    height: 40px;
+    animation: spin 1s linear infinite;
+    margin: 0 auto 10px;
+}
+
+@keyframes spin {
+    0% { transform: rotate(0deg); }
+    100% { transform: rotate(360deg); }
+}
+
+.hidden {
+    display: none;
+}

templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Image Membership Inference</title>
+    <link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+</head>
+<body>
+    <div class="app">
+        <header class="app-header">
+            <h1>Image Membership Inference</h1>
+            <p>Check if an image is likely within the model's training data</p>
+        </header>
+        
+        <main>
+            <form id="upload-form">
+                <div class="upload-container">
+                    <label for="image-upload" class="upload-label">
+                        <div id="preview-container" class="hidden">
+                            <img id="image-preview" src="" alt="Preview">
+                        </div>
+                        <div id="upload-placeholder" class="upload-placeholder">
+                            <span>Click to upload an image</span>
+                        </div>
+                    </label>
+                    <input
+                        id="image-upload"
+                        type="file"
+                        accept="image/*"
+                        class="file-input"
+                    />
+                </div>
+                
+                <button 
+                    type="submit" 
+                    id="submit-button"
+                    class="submit-button" 
+                    disabled
+                >
+                    Check Membership
+                </button>
+            </form>
+            
+            <div id="error-message" class="error-message hidden"></div>
+            
+            <div id="result-container" class="result-container hidden">
+                <h2>Result</h2>
+                <p id="result-message" class="result-message"></p>
+                <p id="membership-status" class="membership-status"></p>
+                <div class="probability-bar">
+                    <div id="probability-fill" class="probability-fill"></div>
+                    <span id="probability-text" class="probability-text"></span>
+                </div>
+            </div>
+            
+            <div id="loading" class="loading hidden">
+                <div class="spinner"></div>
+                <p>Processing image...</p>
+            </div>
+        </main>
+    </div>
+    
+    <script src="{{ url_for('static', filename='script.js') }}"></script>
+</body>
+</html>