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 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():
    global stable_diff_pipe, model
    
    # Load Stable Diffusion model pipeline
    stable_diff_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
    stable_diff_pipe.to("cuda" if torch.cuda.is_available() else "cpu")
    
    # Initialize MIDM model
    input_dim = 10  # Example dimension, adjust based on how you process the features
    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 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 render_template('index.html')

@app.route('/api/check-membership', methods=['POST'])
def check_membership():
    # Ensure models are loaded
    if stable_diff_pipe 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 Stable Diffusion
        image_features = extract_image_features(image)
        
        # Preprocess the features for MIDM model
        processed_features = image_features.reshape(1, -1)[:, :10]  # Select first 10 features (example)
        
        # 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)