Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import pandas as pd
 import os
 import random
+import hashlib
 
 # Check if pubchempy is available
 PUBCHEM_AVAILABLE = False
@@ -157,15 +158,25 @@ def create_fallback_database():
     print("Initializing fallback database")
     return {
         ('warfarin', 'aspirin'): 'Severe',
+        ('aspirin', 'warfarin'): 'Severe',
         ('cathinone', 'yohimbine'): 'Severe',
+        ('yohimbine', 'cathinone'): 'Severe',
         ('codeine', 'hyperforin'): 'Severe',
+        ('hyperforin', 'codeine'): 'Severe',
         ('glycyrrhizin', 'digitoxin'): 'Severe',
+        ('digitoxin', 'glycyrrhizin'): 'Severe',
         ('digoxin', 'glycyrrhizin'): 'Severe',
+        ('glycyrrhizin', 'digoxin'): 'Severe',
         ('yohimbine', 'reserpine'): 'Severe',
+        ('reserpine', 'yohimbine'): 'Severe',
         ('ephedrine', 'yohimbine'): 'Severe',
+        ('yohimbine', 'ephedrine'): 'Severe',
         ('ephedrine', 'cathinone'): 'Severe',
+        ('cathinone', 'ephedrine'): 'Severe',
         ('sennoside a', 'aloin'): 'Severe',
+        ('aloin', 'sennoside a'): 'Severe',
         ('glycyrrhizin', 'sennoside a'): 'Severe',
+        ('sennoside a', 'glycyrrhizin'): 'Severe',
         ('warfarin', 'ibuprofen'): 'Severe',
         ('ibuprofen', 'warfarin'): 'Severe',
         ('simvastatin', 'clarithromycin'): 'Severe',
@@ -185,31 +196,55 @@ def create_fallback_database():
         ('digoxin', 'verapamil'): 'Severe',
         ('verapamil', 'digoxin'): 'Severe',
         ('amodiaquine', 'acebutolol'): 'Severe',
+        ('acebutolol', 'amodiaquine'): 'Severe',
         ('amodiaquine', 'artemether'): 'Severe',
+        ('artemether', 'amodiaquine'): 'Severe',
         ('amodiaquine', 'atenolol'): 'Severe',
+        ('atenolol', 'amodiaquine'): 'Severe',
         ('amodiaquine', 'carvedilol'): 'Severe',
+        ('carvedilol', 'amodiaquine'): 'Severe',
         ('amodiaquine', 'efavirenz'): 'Severe',
+        ('efavirenz', 'amodiaquine'): 'Severe',
         ('amodiaquine', 'betaxolol'): 'Severe',
+        ('betaxolol', 'amodiaquine'): 'Severe',
         ('amodiaquine', 'bisoprolol'): 'Severe',
+        ('bisoprolol', 'amodiaquine'): 'Severe',
         ('amodiaquine', 'metoprolol'): 'Severe',
+        ('metoprolol', 'amodiaquine'): 'Severe',
         ('amodiaquine', 'propranolol'): 'Severe',
+        ('propranolol', 'amodiaquine'): 'Severe',
         ('amodiaquine', 'rifampicin'): 'Severe',
+        ('rifampicin', 'amodiaquine'): 'Severe',
         
         # Moderate interactions
         ('hyperforin', 'mesembrine'): 'Moderate',
+        ('mesembrine', 'hyperforin'): 'Moderate',
         ('kavain', 'valerenic acid'): 'Moderate',
+        ('valerenic acid', 'kavain'): 'Moderate',
         ('kavain', 'withanoside iv'): 'Moderate',
+        ('withanoside iv', 'kavain'): 'Moderate',
         ('thc', 'mesembrine'): 'Moderate',
+        ('mesembrine', 'thc'): 'Moderate',
         ('thc', 'valerenic acid'): 'Moderate',
+        ('valerenic acid', 'thc'): 'Moderate',
         ('hyperforin', 'morphine'): 'Moderate',
+        ('morphine', 'hyperforin'): 'Moderate',
         ('allicin', 'gingerol'): 'Moderate',
+        ('gingerol', 'allicin'): 'Moderate',
         ('parthenolide', 'curcumin'): 'Moderate',
+        ('curcumin', 'parthenolide'): 'Moderate',
         ('yohimbine', 'caffeine'): 'Moderate',
+        ('caffeine', 'yohimbine'): 'Moderate',
         ('amodiaquine', 'amitriptyline'): 'Moderate',
+        ('amitriptyline', 'amodiaquine'): 'Moderate',
         ('amodiaquine', 'candesartan'): 'Moderate',
+        ('candesartan', 'amodiaquine'): 'Moderate',
         ('amodiaquine', 'fluoxetine'): 'Moderate',
+        ('fluoxetine', 'amodiaquine'): 'Moderate',
         ('amodiaquine', 'haloperidol'): 'Moderate',
+        ('haloperidol', 'amodiaquine'): 'Moderate',
         ('amodiaquine', 'chloroquine'): 'Moderate',
+        ('chloroquine', 'amodiaquine'): 'Moderate',
         ('digoxin', 'quinine'): 'Moderate',
         ('quinine', 'digoxin'): 'Moderate',
         ('lisinopril', 'ibuprofen'): 'Moderate',
@@ -233,9 +268,13 @@ def create_fallback_database():
         
         # Mild interactions
         ('amodiaquine', 'amlodipine'): 'Mild',
+        ('amlodipine', 'amodiaquine'): 'Mild',
         ('amodiaquine', 'carbamazepine'): 'Mild',
+        ('carbamazepine', 'amodiaquine'): 'Mild',
         ('metformin', 'ibuprofen'): 'Mild',
+        ('ibuprofen', 'metformin'): 'Mild',
         ('curcumin', 'gingerol'): 'Mild',
+        ('gingerol', 'curcumin'): 'Mild',
         ('omeprazole', 'calcium'): 'Mild',
         ('calcium', 'omeprazole'): 'Mild',
         ('vitamin d', 'calcium'): 'Mild',
@@ -258,8 +297,6 @@ def create_fallback_database():
         # No interactions
         ('vitamin c', 'vitamin d'): 'No Interaction',
         ('vitamin d', 'vitamin c'): 'No Interaction',
-        ('calcium', 'vitamin d'): 'No Interaction',
-        ('vitamin d', 'calcium'): 'No Interaction',
         ('omeprazole', 'vitamin d'): 'No Interaction',
         ('vitamin d', 'omeprazole'): 'No Interaction',
         ('metformin', 'vitamin d'): 'No Interaction',
@@ -312,6 +349,19 @@ def predict_from_features(drug1_features, drug2_features):
     else:
         return "No Interaction"
 
+def get_deterministic_prediction(drug1_active, drug2_active):
+    """Get a deterministic prediction based on drug pair hash"""
+    # Create a deterministic seed based on the alphabetically sorted drug pair
+    drug_pair = tuple(sorted([drug1_active, drug2_active]))
+    seed_string = f"{drug_pair[0]}_{drug_pair[1]}"
+    seed = int(hashlib.md5(seed_string.encode()).hexdigest()[:8], 16)
+    
+    # Use the seed to get a deterministic "random" choice
+    random.seed(seed)
+    result = random.choice(['Moderate', 'Mild'])
+    random.seed()  # Reset random seed
+    return result
+
 def predict_interaction(drug_names, dataset_db, fallback_db):
     """Predict interaction between two drugs using dataset, fallback, and PubChem"""
     try:
@@ -359,8 +409,8 @@ def predict_interaction(drug_names, dataset_db, fallback_db):
         if drug1_features and drug2_features:
             return predict_from_features(drug1_features, drug2_features)
         else:
-            # Balanced random choice for completely unknown drugs
-            return random.choice(['Moderate', 'Mild'])
+            # Use deterministic prediction for completely unknown drugs
+            return get_deterministic_prediction(drug1_active, drug2_active)
         
     except Exception as e:
         print(f"Error in prediction: {e}")