app.py

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
import subprocess

from sklearn.metrics import classification_report




# Function to show value counts of 'Hair Loss'
def show_hair_loss_counts():
    return df['Hair Loss'].value_counts().to_json()  # Convert to JSON for display

# Function to run commands
def run_command(command):
    try:
        result = subprocess.run(command, shell=True, capture_output=True, text=True)
        return result.stdout if result.returncode == 0 else result.stderr
    except Exception as e:
        return str(e)

# 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(*inputs):
    # Convert the list of inputs into a dictionary
    input_data = {col: val for col, val in zip(X.columns, inputs)}

    # 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])

    gr.Markdown("## Hair Loss Value Counts")

    value_counts_output = gr.JSON(label="Hair Loss Value Counts")
    value_counts_button = gr.Button("Show Hair Loss Counts")
    value_counts_button.click(show_hair_loss_counts, outputs=[value_counts_output])

    gr.Markdown("## Command Runner")

    command_input = gr.Textbox(label="Enter Command")
    command_output = gr.Textbox(label="Command Output")

    run_button = gr.Button("Run Command")
    run_button.click(run_command, inputs=command_input, outputs=command_output)




y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))


demo.launch()