Update app.py

app.py

@@ -60,24 +60,24 @@ def load_data():
     except FileNotFoundError:
         raise RuntimeError("Data files not found. Please ensure `Training.csv` and `Testing.csv` are uploaded correctly.")
 
-    # Encode diseases
+    # Encode diseases in a dictionary
     disease_dict = {
         'Fungal infection': 0, 'Allergy': 1, 'GERD': 2, 'Chronic cholestasis': 3, 'Drug Reaction': 4,
-        'Peptic ulcer diseae': 5, 'AIDS': 6, 'Diabetes ': 7, 'Gastroenteritis': 8, 'Bronchial Asthma': 9,
+        'Peptic ulcer disease': 5, 'AIDS': 6, 'Diabetes ': 7, 'Gastroenteritis': 8, 'Bronchial Asthma': 9,
         'Hypertension ': 10, 'Migraine': 11, 'Cervical spondylosis': 12, 'Paralysis (brain hemorrhage)': 13,
-        'Jaundice': 14, 'Malaria': 15, 'Chicken pox': 16, 'Dengue': 17, 'Typhoid': 18, 'hepatitis A': 19,
+        'Jaundice': 14, 'Malaria': 15, 'Chicken pox': 16, 'Dengue': 17, 'Typhoid': 18, 'Hepatitis A': 19,
         'Hepatitis B': 20, 'Hepatitis C': 21, 'Hepatitis D': 22, 'Hepatitis E': 23, 'Alcoholic hepatitis': 24,
-        'Tuberculosis': 25, 'Common Cold': 26, 'Pneumonia': 27, 'Dimorphic hemmorhoids(piles)': 28,
+        'Tuberculosis': 25, 'Common Cold': 26, 'Pneumonia': 27, 'Dimorphic hemorrhoids(piles)': 28,
         'Heart attack': 29, 'Varicose veins': 30, 'Hypothyroidism': 31, 'Hyperthyroidism': 32,
-        'Hypoglycemia': 33, 'Osteoarthritist': 34, 'Arthritis': 35,
-        '(vertigo) Paroymsal  Positional Vertigo': 36, 'Acne': 37, 'Urinary tract infection': 38,
+        'Hypoglycemia': 33, 'Osteoarthritis': 34, 'Arthritis': 35,
+        '(vertigo) Paroxysmal Positional Vertigo': 36, 'Acne': 37, 'Urinary tract infection': 38,
         'Psoriasis': 39, 'Impetigo': 40
     }
 
     # Replace prognosis values with numerical categories
     df.replace({'prognosis': disease_dict}, inplace=True)
 
-    # Check unique values in prognosis for debugging
+    # Print unique values for debugging
     print("Unique values in prognosis after mapping:", df['prognosis'].unique())
 
     # Ensure prognosis is purely numerical after mapping
@@ -91,8 +91,10 @@ def load_data():
     # Similar process for the testing data
     tr.replace({'prognosis': disease_dict}, inplace=True)
 
-    # Ensure it is also numerical
-    if tr['prognosis'].dtype == 'object':
+    # Print unique values for testing data
+    print("Unique values in prognosis for testing data after mapping:", tr['prognosis'].unique())
+    
+    if tr['prognosis'].dtype == 'object':  # Check for unmapped entries
         raise ValueError(f"Testing data prognosis contains unmapped values: {tr['prognosis'].unique()}")
 
     tr['prognosis'] = tr['prognosis'].astype(int)  # Convert to integer if necessary
@@ -109,7 +111,7 @@ y_test = tr['prognosis']
 
 # Encode the target variable with LabelEncoder if still in string format
 le = LabelEncoder()
-y_encoded = le.fit_transform(y)  # Needs to be string labels, assuming df['prognosis'] has no numerical labels
+y_encoded = le.fit_transform(y)  # Encode string labels into integers
 
 def train_models():
     models = {
@@ -233,7 +235,6 @@ def get_health_professionals_and_map(location, query):
                     popup=f"{place['name']}"
                 ).add_to(map_)
             return professionals, map_._repr_html_()
-
         return [], ""  # Return empty list if no professionals found
     except Exception as e:
         return [], ""  # Return empty list on exception
@@ -293,12 +294,6 @@ textarea:focus, input:focus {
     outline: none;
 }
 
-textarea:hover, input:hover {
-    background: transparent;
-    color: black;
-    border: 2px solid orange;
-}
-
 .df-container {
     background: white;
     color: black;