Update app.py

app.py

@@ -17,7 +17,7 @@ from sklearn.tree import DecisionTreeClassifier
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.naive_bayes import GaussianNB
 from sklearn.metrics import accuracy_score
-
+from sklearn.preprocessing import LabelEncoder
 
 # Suppress TensorFlow warnings
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # No GPU available, use CPU only
@@ -87,6 +87,10 @@ y = df['prognosis']
 X_test = tr[l1]
 y_test = tr['prognosis']
 
+# Encode the target variable
+le = LabelEncoder()
+y_encoded = le.fit_transform(y)  # Encode string labels into integers
+
 def train_models():
     models = {
         "Decision Tree": DecisionTreeClassifier(),
@@ -95,7 +99,7 @@ def train_models():
     }
     trained_models = {}
     for model_name, model_obj in models.items():
-        model_obj.fit(X, y)
+        model_obj.fit(X, y_encoded)  # Use encoded labels
         acc = accuracy_score(y_test, model_obj.predict(X_test))
         trained_models[model_name] = (model_obj, acc)
     return trained_models