Create app.py

app.py

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+
+import pandas as pd
+import gradio as gr
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+
+# Load and preprocess the dataset
+data = pd.read_csv('data.csv')
+
+# Preprocessing
+data['Age'] = data['Age'].fillna(data['Age'].median())
+data['Embarked'] = data['Embarked'].fillna(data['Embarked'].mode()[0])
+data['Fare'] = pd.to_numeric(data['Fare'], errors='coerce')
+data['Fare'] = data['Fare'].fillna(data['Fare'].median())
+
+label_encoder = LabelEncoder()
+data['Gender'] = label_encoder.fit_transform(data['Gender'])
+data['Embarked'] = label_encoder.fit_transform(data['Embarked'])
+
+data.drop(['Name', 'Ticket', 'Cabin', 'PassengerId'], axis=1, inplace=True)
+
+# Feature selection
+features = ['Pclass', 'Gender', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
+X = data[features]
+y = data['Survived']
+
+# Train the model
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+model = RandomForestClassifier(random_state=42)
+model.fit(X_train, y_train)
+
+# Gradio interface function
+def predict_survival(Pclass, Gender, Age, SibSp, Parch, Fare, Embarked):
+    # Encode Gender and Embarked
+    Gender_encoded = 1 if Gender.lower() == 'female' else 0
+    Embarked_encoded = {'s': 0, 'c': 1, 'q': 2}.get(Embarked.lower(), 0)
+
+    # Create input DataFrame
+    input_data = pd.DataFrame([[Pclass, Gender_encoded, Age, SibSp, Parch, Fare, Embarked_encoded]],
+                              columns=features)
+    
+    # Predict
+    prediction = model.predict(input_data)
+    return "Survived" if prediction[0] == 1 else "Did Not Survive"
+
+# Gradio inputs and outputs
+inputs = [
+    gr.Slider(1, 3, step=1, label="Passenger Class (Pclass)"),
+    gr.Radio(["Male", "Female"], label="Gender"),
+    gr.Slider(0, 80, step=1, label="Age (in years)"),
+    gr.Slider(0, 10, step=1, label="Siblings/Spouses (SibSp)"),
+    gr.Slider(0, 10, step=1, label="Parents/Children (Parch)"),
+    gr.Slider(0, 500, step=1, label="Ticket Fare (in $)"),
+    gr.Radio(["S (Southampton)", "C (Cherbourg)", "Q (Queenstown)"], label="Port of Embarkation (Embarked)")
+]
+
+outputs = gr.Textbox(label="Prediction")
+
+# Launch Gradio interface
+gr.Interface(fn=predict_survival, inputs=inputs, outputs=outputs, title="Titanic Survival Predictor").launch()