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database.py

@@ -57,6 +57,19 @@ def init_db():
         )
     ''')
 
+    # --- MIGRATIONS ---
+    # Ensure security columns exist (backward compatibility)
+    try:
+        c.execute("ALTER TABLE users ADD COLUMN security_question TEXT DEFAULT 'Question?'")
+    except sqlite3.OperationalError:
+        pass # Column exists
+
+    try:
+        c.execute("ALTER TABLE users ADD COLUMN security_answer TEXT DEFAULT 'answer'")
+    except sqlite3.OperationalError:
+        pass # Column exists
+    # ------------------
+
     # Create Patients Table
     c.execute('''
         CREATE TABLE IF NOT EXISTS patients (

main.py

@@ -1110,14 +1110,16 @@ class MedSigClipWrapper:
             image_array = np.array(image)
             
             # Get image embedding for similar case detection
-            try:
-                with torch.no_grad():
-                    img_inputs = self.processor(images=image, return_tensors="pt")
-                    image_embedding = self.model.get_image_features(**img_inputs)
-                    image_embedding = image_embedding.cpu().numpy().flatten()
-            except Exception as e_emb:
-                logger.warning(f"Could not extract embedding: {e_emb}")
-                image_embedding = None
+            # OPT PERFORMANCE: Disabled to reduce inference time (<60s)
+            image_embedding = None
+            # try:
+            #     with torch.no_grad():
+            #         img_inputs = self.processor(images=image, return_tensors="pt")
+            #         image_embedding = self.model.get_image_features(**img_inputs)
+            #         image_embedding = image_embedding.cpu().numpy().flatten()
+            # except Exception as e_emb:
+            #     logger.warning(f"Could not extract embedding: {e_emb}")
+            #     image_embedding = None
             
             # Enhance result with all algorithms
             enhanced_result = enhance_analysis_result(