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| import gradio as gr | |
| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.ensemble import RandomForestClassifier | |
| from sklearn.metrics import accuracy_score | |
| import io # Keep io, though not strictly used in this version, it's harmless. | |
| # --- Data Loading and Preprocessing --- | |
| # Load the dataset named 'titanic.csv' | |
| # Make sure 'titanic.csv' is uploaded to your Hugging Face Space or is in the same directory | |
| try: | |
| df = pd.read_csv('titanic.csv') | |
| except FileNotFoundError: | |
| raise FileNotFoundError("titanic.csv not found. Please ensure it's downloaded and named 'titanic.csv', then uploaded to your Hugging Face Space.") | |
| # Drop irrelevant columns and 'PassengerId' which is not a feature | |
| # These columns are typically present in a standard Titanic dataset. | |
| df = df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1) | |
| # Handle missing 'Age' with median imputation | |
| df['Age'].fillna(df['Age'].median(), inplace=True) | |
| # Handle missing 'Fare' with median imputation (Fare can also have missing values sometimes) | |
| df['Fare'].fillna(df['Fare'].median(), inplace=True) | |
| # Handle missing 'Embarked' with mode imputation | |
| df['Embarked'].fillna(df['Embarked'].mode()[0], inplace=True) | |
| # Convert categorical features to numerical using one-hot encoding | |
| # We drop 'Embarked_C' to avoid multicollinearity (as per common practice) | |
| df = pd.get_dummies(df, columns=['Sex', 'Embarked'], drop_first=True) | |
| # Define features (X) and target (y) | |
| X = df.drop('Survived', axis=1) | |
| y = df['Survived'] | |
| # Split data into training and testing sets | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| # Train a RandomForestClassifier model | |
| model = RandomForestClassifier(n_estimators=100, random_state=42) | |
| model.fit(X_train, y_train) | |
| # --- Model Evaluation (for display in app) --- | |
| y_pred = model.predict(X_test) | |
| accuracy = accuracy_score(y_test, y_pred) | |
| accuracy_message = f"Model Accuracy on Test Set: {accuracy:.2f}" | |
| # --- Prediction Function for Gradio --- | |
| def predict_survival(pclass, sex, age, sibsp, parch, fare, embarked): | |
| # Create a dictionary for the input values | |
| input_dict = { | |
| 'Pclass': pclass, | |
| 'Age': age, | |
| 'SibSp': sibsp, | |
| 'Parch': parch, | |
| 'Fare': fare, | |
| # These match the one-hot encoded columns created during training | |
| 'Sex_male': 1 if sex == 'male' else 0, | |
| 'Embarked_Q': 1 if embarked == 'Q' else 0, # Assuming 'Q' is 'Embarked_Q' | |
| 'Embarked_S': 1 if embarked == 'S' else 0 # Assuming 'S' is 'Embarked_S' | |
| } | |
| # Create a DataFrame from the input values | |
| input_data = pd.DataFrame([input_dict]) | |
| # Ensure all columns expected by the model are present in the input_data, even if 0 | |
| # This handles cases where a category might not be present in a single input but was in training | |
| for col in X.columns: | |
| if col not in input_data.columns: | |
| input_data[col] = 0 | |
| # Reorder columns to match the training data's column order | |
| input_data = input_data[X.columns] | |
| # Make prediction | |
| prediction = model.predict(input_data)[0] | |
| prediction_proba = model.predict_proba(input_data)[0] | |
| if prediction == 1: | |
| return f"Prediction: Survived ({prediction_proba[1]:.2%} confidence)", "green" | |
| else: | |
| return f"Prediction: Did Not Survive ({prediction_proba[0]:.2%} confidence)", "red" | |
| # --- Gradio Interface --- | |
| # CSS to style the output textbox background | |
| with gr.Blocks(css=".green {background-color: #e6ffe6 !important;}.red {background-color: #ffe6e6 !important;}") as demo: | |
| gr.Markdown( | |
| """ | |
| # Titanic Survival Predictor | |
| Enter passenger details to predict their survival on the Titanic. | |
| """ | |
| ) | |
| gr.Markdown(f"### Model Performance: {accuracy_message}") | |
| with gr.Row(): | |
| pclass_input = gr.Radio(choices=[1, 2, 3], label="Pclass", value=3) | |
| sex_input = gr.Radio(choices=['male', 'female'], label="Sex", value='male') | |
| age_input = gr.Slider(minimum=0.5, maximum=80, value=30, label="Age", step=0.5) | |
| with gr.Row(): | |
| sibsp_input = gr.Number(label="SibSp (Siblings/Spouses Aboard)", value=0) | |
| parch_input = gr.Number(label="Parch (Parents/Children Aboard)", value=0) | |
| fare_input = gr.Number(label="Fare", value=30.0) | |
| with gr.Row(): | |
| embarked_input = gr.Radio(choices=['C', 'Q', 'S'], label="Embarked (Port of Embarkation)", value='S') | |
| predict_btn = gr.Button("Predict Survival") | |
| output_text = gr.Textbox(label="Survival Prediction", interactive=False) | |
| # This label is used internally to get the color, its content is not directly shown | |
| output_color_indicator = gr.Label(visible=False) | |
| # Function to update the textbox styling based on prediction | |
| def update_output_style(text, color): | |
| if color == "green": | |
| return gr.Textbox(value=text, label="Survival Prediction", interactive=False, elem_classes="green") | |
| elif color == "red": | |
| return gr.Textbox(value=text, label="Survival Prediction", interactive=False, elem_classes="red") | |
| else: | |
| return gr.Textbox(value=text, label="Survival Prediction", interactive=False) | |
| predict_btn.click( | |
| fn=predict_survival, | |
| inputs=[pclass_input, sex_input, age_input, sibsp_input, parch_input, fare_input, embarked_input], | |
| outputs=[output_text, output_color_indicator] | |
| ).then( | |
| fn=update_output_style, | |
| inputs=[output_text, output_color_indicator], | |
| outputs=output_text | |
| ) | |
| gr.Markdown( | |
| """ | |
| --- | |
| **Feature Definitions:** | |
| * **Pclass:** Passenger Class (1 = 1st, 2 = 2nd, 3 = 3rd) | |
| * **Sex:** Sex (male/female) | |
| * **Age:** Age in years | |
| * **SibSp:** Number of siblings/spouses aboard the Titanic | |
| * **Parch:** Number of parents/children aboard the Titanic | |
| * **Fare:** Passenger fare | |
| * **Embarked:** Port of Embarkation (C = Cherbourg, Q = Queenstown, S = Southampton) | |
| *Note: This app expects a `titanic.csv` file. Missing 'Age', 'Fare', and 'Embarked' values are imputed. Categorical features are one-hot encoded.* | |
| """ | |
| ) | |
| demo.launch() |