Update app.py

app.py

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+import gradio as gr
+import xgboost as xgb
+import pandas as pd
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("Ammok/hair_health")
+
+# Convert to Pandas DataFrame for exploration
+df = pd.DataFrame(dataset['train'])
+
+# Example: Train a simple XGBoost model
+X = df.drop(columns=["target_column"])  # Replace with your feature columns
+y = df["target_column"]  # Replace with your target column
+
+# Train a basic XGBoost model (replace with custom model training code)
+model = xgb.XGBClassifier()
+model.fit(X, y)
+
+# Function for making predictions
+def predict(input_data):
+    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")
+    input_data = {col: gr.Number(label=col) for col in X.columns}  # Adjust based on features
+    output = gr.Textbox(label="Prediction")
+    
+    submit_button = gr.Button("Predict")
+    submit_button.click(predict, inputs=[input_data], outputs=[output])
+
+demo.launch()