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):
    # Handle missing or invalid inputs
    if not Gender:
        return "⚠️ Error: Please select a Gender."
    if not Embarked:
        return "⚠️ Error: Please select a Port of Embarkation."

    # 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 or Error Message")

# Launch Gradio interface
gr.Interface(fn=predict_survival, inputs=inputs, outputs=outputs, title="Titanic Survival Predictor By Ozan").launch()