Update app.py

app.py

@@ -7,7 +7,10 @@ from sklearn.preprocessing import LabelEncoder
 
 # Load dataset
 df = pd.read_csv("titanic.csv")
-df = df[["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Survived"]].dropna()
+expected_cols = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Survived"]
+available_cols = [col for col in expected_cols if col in df.columns]
+df = df[available_cols].dropna()
+
 
 # Encode 'Sex'
 df["Sex"] = LabelEncoder().fit_transform(df["Sex"])  # male=1, female=0
@@ -21,9 +24,9 @@ model = RandomForestClassifier(n_estimators=100, random_state=42)
 model.fit(X, y)
 
 # Prediction function
-def predict_survival(pclass, sex, age, sibsp, parch, fare):
+def predict_survival(pclass, sex, age, fare):
     sex_encoded = 1 if sex == "male" else 0
-    input_data = np.array([[pclass, sex_encoded, age, sibsp, parch, fare]])
+    input_data = np.array([[pclass, sex_encoded, age, fare]])
     prediction = model.predict(input_data)[0]
     return "✅ Survived" if prediction == 1 else "❌ Did not survive"
 
@@ -34,8 +37,6 @@ iface = gr.Interface(
         gr.Dropdown([1, 2, 3], label="Passenger Class"),
         gr.Radio(["male", "female"], label="Sex"),
         gr.Slider(0, 80, step=1, label="Age"),
-        gr.Slider(0, 10, step=1, label="Siblings/Spouses Aboard"),
-        gr.Slider(0, 10, step=1, label="Parents/Children Aboard"),
         gr.Slider(0, 500, step=1, label="Fare"),
     ],
     outputs="text",