server.py

import torch
import torch.nn as nn
from flask import Flask, request, jsonify, render_template, make_response, send_from_directory
from flask_cors import CORS
import io
import os
from PIL import Image
from diffusers import StableDiffusionPipeline
import os

token = os.getenv("HF_TOKEN")

# 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
stable_diff_pipe = None
model = None

def load_models(model_name="CompVis/stable-diffusion-v1-4"):
    global stable_diff_pipe, model
    
    # Load Stable Diffusion model pipeline
    stable_diff_pipe = StableDiffusionPipeline.from_pretrained(model_name)
    stable_diff_pipe.to("cuda" if torch.cuda.is_available() else "cpu")
    
    # Initialize MIDM model
    input_dim = 10  
    hidden_dim = 64
    output_dim = 1
    model = MIDM(input_dim, hidden_dim, output_dim)
    
    model.eval()

# Function to extract features from the image using Stable Diffusion
def extract_image_features(image):
    #Extracts image features using the Stable Diffusion pipeline.
    # Preprocess the image and get the feature vector
    image_input = stable_diff_pipe.feature_extractor(image, return_tensors="pt").pixel_values.to(stable_diff_pipe.device)
    
    # Generate the image embedding using the model
    with torch.no_grad():
        generated_features = stable_diff_pipe.vae.encode(image_input).latent_dist.mean

    return generated_features

@app.route('/')
def index():
    return send_from_directory('.', 'index.html')

@app.route('/resources')
def resources():
    return send_from_directory('.', 'resources.html')

@app.route('/get-organized')
def get_organized():
    return send_from_directory('.', 'get-organized.html')

@app.route('/static/<path:filename>')
def static_files(filename):
    return send_from_directory('static', filename)

@app.route('/api/check-membership', methods=['POST'])
def check_membership():
    try:
        model_name = request.form.get('model', 'CompVis/stable-diffusion-v1-4')
        global stable_diff_pipe, model
        if stable_diff_pipe is None or model is None:
            load_models(model_name)
        elif stable_diff_pipe.name_or_path != model_name:
            load_models(model_name)
        if 'image' not in request.files:
            return jsonify({'error': 'No image found in request'}), 400
        file = request.files['image']
        image_bytes = file.read()
        image = Image.open(io.BytesIO(image_bytes))
        image_features = extract_image_features(image)
        processed_features = image_features.reshape(1, -1)[:, :10]
        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:
        print(f"Error processing request: {str(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)