Update server.py

server.py

@@ -7,8 +7,6 @@ import os
 from PIL import Image
 from diffusers import StableDiffusionPipeline
 import os
-from tinydb import TinyDB, Query
-import uuid
 
 token = os.getenv("HF_TOKEN")
 
@@ -35,9 +33,6 @@ CORS(app)
 stable_diff_pipe = None
 model = None
 
-os.makedirs('/data', exist_ok=True)
-db = TinyDB('/data/artwork_results.json')
-
 def load_models(model_name="rupeshs/LCM-runwayml-stable-diffusion-v1-5"):
     global stable_diff_pipe, model
     
@@ -65,13 +60,6 @@ def extract_image_features(image):
 
     return generated_features
 
-# Helper to get or set a unique user_id via cookies
-def get_user_id():
-    user_id = request.cookies.get('user_id')
-    if not user_id:
-        user_id = str(uuid.uuid4())
-    return user_id
-
 @app.route('/')
 def index():
     return render_template('index.html')
@@ -80,10 +68,6 @@ def index():
 def check_membership():
     try:
         model_name = request.form.get('model', 'rupeshs/LCM-runwayml-stable-diffusion-v1-5')
-        contact_info = request.form.get('contact_info')  # Optional, if frontend sends it
-        opt_in = request.form.get('opt_in', 'false').lower() == 'true'
-        user_id = get_user_id()
-        # Ensure models are loaded with the selected model
         global stable_diff_pipe, model
         if stable_diff_pipe is None or model is None:
             load_models(model_name)
@@ -100,35 +84,12 @@ def check_membership():
             output = model(processed_features)
             probability = output.item()
             predicted = int(output > 0.5)
-        # Store in DB if likely and opt-in
-        if predicted == 1:
-            entry = {
-                'model': model_name,
-                'user_id': user_id,
-                'timestamp': int(uuid.uuid1().time),
-            }
-            if opt_in and contact_info:
-                entry['contact_info'] = contact_info
-            # Only store if not already present for this user/model
-            User = Query()
-            exists = db.search((User.model == model_name) & (User.user_id == user_id))
-            if not exists:
-                db.insert(entry)
-        # Count unique user_ids for this model
-        User = Query()
-        user_ids = set(r['user_id'] for r in db.search(User.model == model_name))
-        count = len(user_ids)
-        response = make_response(jsonify({
+        return jsonify({
             'probability': probability,
             'predicted_class': predicted,
             'message': f"Predicted membership probability: {probability}",
-            'is_in_training_data': "Likely" if predicted == 1 else "Unlikely",
-            'likely_count': count
-        }))
-        # Set user_id cookie if not present
-        if not request.cookies.get('user_id'):
-            response.set_cookie('user_id', user_id, max_age=60*60*24*365*5)  # 5 years
-        return response
+            '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