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import gradio as gr
import xgboost as xgb
import pandas as pd
from datasets import load_dataset
from sklearn.model_selection import train_test_split
# Load the dataset
dataset = load_dataset("Ammok/hair_health")
# Convert to Pandas DataFrame for exploration
df = pd.DataFrame(dataset['train'])
### PREPROCESSING
# Replace "No Data" entries with NaN for missing values handling
df.replace("No Data", pd.NA, inplace=True)
# Handle missing numerical values with mean
df.fillna(df.select_dtypes(include=['number']).mean(), inplace=True)
# Handle missing categorical values with mode
for col in df.select_dtypes(include=['object']).columns:
df[col] = df[col].fillna(df[col].mode()[0])
# One-hot encoding for categorical variables
categorical_cols = [
'Genetics', 'Hormonal Changes', 'Medical Conditions',
'Medications & Treatments', 'Nutritional Deficiencies ', 'Stress',
'Poor Hair Care Habits ', 'Environmental Factors', 'Smoking', 'Weight Loss '
]
df = pd.get_dummies(df, columns=categorical_cols, drop_first=True)
# Extract features and target
X = df.drop(columns=["Hair Loss"])
y = df["Hair Loss"]
# Split the dataset into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train a basic XGBoost model
model = xgb.XGBClassifier()
model.fit(X_train, y_train)
def predict(input_data):
# Handle missing values or intentionally omitted fields
for col in X.columns:
if input_data.get(col) is None:
if X[col].dtype == 'float64': # For numerical features
input_data[col] = X[col].mean() # Use the mean for missing numerical values
else: # For categorical features
input_data[col] = X[col].mode()[0] # Use the mode for missing categorical values
# Convert input data to a DataFrame
data = pd.DataFrame([input_data], columns=X.columns)
prediction = model.predict(data)
return prediction[0]
# Set up Gradio interface for data exploration
def explore_data(row_number):
return df.iloc[row_number].to_dict()
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("# Hair Health Dataset Exploration")
row_number_input = gr.Number(label="Row Number")
data_output = gr.JSON(label="Row Data")
row_number_input.change(explore_data, inputs=[row_number_input], outputs=[data_output])
gr.Markdown("## Make a Prediction")
# Create a dictionary for input components
input_components = {col: gr.Number(label=col) for col in X.columns} # Generate number inputs for each column
output = gr.Textbox(label="Prediction")
submit_button = gr.Button("Predict")
# Unpack the dictionary values into a list of input components
submit_button.click(predict, inputs=list(input_components.values()), outputs=[output])
demo.launch()
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