Update app.py

app.py

@@ -1,33 +1,36 @@
 import gradio as gr
 import pickle
 import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn import __version__ as sklearn_version
 
-# load the data
-heart=pd.read_csv('heart.dat', header=None, sep=' ', names=['age', 'sex', 'cp', 'trestbps', 'chol',
+# Check scikit-learn version
+print(f"scikit-learn version: {sklearn_version}")
+
+# Load the data
+heart = pd.read_csv('heart.dat', header=None, sep=' ', names=['age', 'sex', 'cp', 'trestbps', 'chol',
                                                             'fbs', 'restecg', 'thalach', 'exang',
                                                             'oldpeak', 'slope', 'ca', 'thal', 'heart disease'])
 
-# load the saved models
-with open('Tree.pkl', 'rb') as f:
-    tree_model = pickle.load(f)
-
-with open('svm.pkl', 'rb') as f:
-    svm_model = pickle.load(f)
-
-with open('QDA.pkl', 'rb') as f:
-    qda_model = pickle.load(f)
-
-with open('MLP.pkl', 'rb') as f:
-    mlp_model = pickle.load(f)
+# Load the saved models with error handling
+def load_model(filename):
+    try:
+        with open(filename, 'rb') as f:
+            return pickle.load(f)
+    except Exception as e:
+        print(f"Error loading {filename}: {e}")
+        return None
 
-with open('Log.pkl', 'rb') as f:
-    log_model = pickle.load(f)
-
-with open('LDA.pkl', 'rb') as f:
-    lda_model = pickle.load(f)
-
-with open('For.pkl', 'rb') as f:
-    for_model = pickle.load(f)
+models = {
+    'Tree': load_model('Tree.pkl'),
+    'SVM': load_model('svm.pkl'),
+    'QDA': load_model('QDA.pkl'),
+    'MLP': load_model('MLP.pkl'),
+    'Log': load_model('Log.pkl'),
+    'LDA': load_model('LDA.pkl'),
+    'For': load_model('For.pkl')
+}
 
 # Define the function to make predictions
 def make_prediction(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal, model_name):
@@ -48,27 +51,20 @@ def make_prediction(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang,
         'thal': [thal]
     })
 
-    # feature scaling
-    from sklearn.model_selection import train_test_split
+    # Feature scaling
     X = heart.drop('heart disease', axis=1)
     y = heart['heart disease']
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)
-    from sklearn.preprocessing import StandardScaler
     scaler = StandardScaler()
     X_train_std = scaler.fit_transform(X_train)
 
-    # choose the model and make prediction
-    model_dict = {'Decision_Tree': tree_model,
-                  'QDA': qda_model,
-                  'Artificial_Neural_Networks': mlp_model,
-                  'Logistic_Regression': log_model,
-                  'LDA': lda_model,
-                  'Random_Forest': for_model,
-                  'SVM': svm_model}
-    model = model_dict[model_name]
+    # Choose the model and make prediction
+    model = models.get(model_name)
+    if model is None:
+        return "Model not found or failed to load."
+
     input_data_std = scaler.transform(input_data)
     probas = model.predict_proba(input_data_std)
-    outtext={1:'no heart_disease', 2:'heart disease'}  
     return {f"Probability of Class {i+1}": proba for i, proba in enumerate(probas[0])}
 
 # Create the Gradio interface
@@ -86,9 +82,9 @@ inputs = [
     gr.inputs.Dropdown(choices=[1,2,3], label='slope ST'),
     gr.inputs.Dropdown(choices=[0,1,2,3], label='major vessels'),
     gr.inputs.Dropdown(choices=[3,6,7], label='thal'),
-    gr.inputs.Dropdown(choices=['Decision_Tree', 'QDA', 'Artificial_Neural_Networks', 'Logistic_Regression', 'LDA', 'Random_Forest', 'SVM'], label='Select the model')
+    gr.inputs.Dropdown(choices=['Tree', 'QDA', 'MLP', 'Log', 'LDA', 'For', 'SVM'], label='Select the model')
 ]
 
 outputs = gr.outputs.Label(label='Predicted class probabilities')
 
-gr.Interface(fn=make_prediction, inputs=inputs, outputs=outputs).launch()
+gr.Interface(fn=make_prediction, inputs=inputs, outputs=outputs).launch()