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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()
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