first commit -- grad boost demo

app.py

@@ -0,0 +1,99 @@
+import gradio as gr
+import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, confusion_matrix
+
+# 1. Load dataset
+iris = load_iris()
+X, y = iris.data, iris.target
+feature_names = iris.feature_names
+class_names = iris.target_names
+
+# Split into train/test
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.3, random_state=42
+)
+
+# 2. Define a function that takes hyperparameters and returns model accuracy + confusion matrix
+def train_and_evaluate(learning_rate, n_estimators, max_depth):
+    # Train model
+    clf = GradientBoostingClassifier(
+        learning_rate=learning_rate,
+        n_estimators=n_estimators,
+        max_depth=int(max_depth),
+        random_state=42
+    )
+    clf.fit(X_train, y_train)
+
+    # Predict on test data
+    y_pred = clf.predict(X_test)
+
+    # Calculate metrics
+    accuracy = accuracy_score(y_test, y_pred)
+    cm = confusion_matrix(y_test, y_pred)
+
+    # Convert confusion matrix to a more display-friendly format
+    cm_display = ""
+    for row in cm:
+        cm_display += str(row) + "\n"
+
+    return f"Accuracy: {accuracy:.3f}\nConfusion Matrix:\n{cm_display}"
+
+# 3. Define a prediction function for user-supplied feature values
+def predict_species(sepal_length, sepal_width, petal_length, petal_width,
+                    learning_rate, n_estimators, max_depth):
+    # Train a new model using same hyperparams
+    clf = GradientBoostingClassifier(
+        learning_rate=learning_rate,
+        n_estimators=n_estimators,
+        max_depth=int(max_depth),
+        random_state=42
+    )
+    clf.fit(X_train, y_train)
+
+    # Predict species
+    user_sample = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+    prediction = clf.predict(user_sample)[0]
+    return f"Predicted species: {class_names[prediction]}"
+
+# 4. Build the Gradio interface
+
+# Inputs to tune hyperparameters
+hyperparam_inputs = [
+    gr.inputs.Slider(0.01, 1.0, step=0.01, default=0.1, label="learning_rate"),
+    gr.inputs.Slider(50, 300, step=50, default=100, label="n_estimators"),
+    gr.inputs.Slider(1, 10, step=1, default=3, label="max_depth")
+]
+
+# Button or automatic “live” updates
+training_interface = gr.Interface(
+    fn=train_and_evaluate,
+    inputs=hyperparam_inputs,
+    outputs="text",
+    title="Gradient Boosting Training and Evaluation",
+    description="Train a GradientBoostingClassifier on the Iris dataset with different hyperparameters."
+)
+
+# Inputs for real-time prediction
+feature_inputs = [
+    gr.inputs.Number(default=5.1, label=feature_names[0]),
+    gr.inputs.Number(default=3.5, label=feature_names[1]),
+    gr.inputs.Number(default=1.4, label=feature_names[2]),
+    gr.inputs.Number(default=0.2, label=feature_names[3])
+] + hyperparam_inputs
+
+prediction_interface = gr.Interface(
+    fn=predict_species,
+    inputs=feature_inputs,
+    outputs="text",
+    title="Iris Species Prediction",
+    description="Use a GradientBoostingClassifier to predict Iris species from user input."
+)
+
+demo = gr.TabbedInterface([training_interface, prediction_interface],
+                          ["Train & Evaluate", "Predict"])
+
+# Launch the Gradio app
+demo.launch()