First attempt

app.py

@@ -1,8 +1,92 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!"
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.inspection import permutation_importance
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def create_dataset():
+    X, y = make_classification(
+        n_samples=1000,
+        n_features=10,
+        n_informative=3,
+        n_redundant=0,
+        n_repeated=0,
+        n_classes=2,
+        random_state=0,
+        shuffle=False,
+    )
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)
+    return X_train, X_test, y_train, y_test 
+
+def train_model():
+    
+    X_train, X_test, y_train, y_test = create_dataset()
+
+    feature_names = [f"feature {i}" for i in range(X_train.shape[1])]
+    forest = RandomForestClassifier(random_state=0)
+    forest.fit(X_train, y_train)
+
+    return forest, feature_names, X_test, y_test
+
+
+def plot_mean_decrease(clf, feature_names):
+    importances = clf.feature_importances_
+    std = np.std([tree.feature_importances_ for tree in clf.estimators_], axis=0)
+
+    forest_importances = pd.Series(importances, index=feature_names)
+
+    fig, ax = plt.subplots()
+    forest_importances.plot.bar(yerr=std, ax=ax)
+    ax.set_title("Feature importances using MDI")
+    ax.set_ylabel("Mean decrease in impurity")
+    fig.tight_layout()
+
+    return fig
+
+def plot_feature_perm(clf, feature_names, X_test, y_test):
+    result = permutation_importance(
+        clf, X_test, y_test, n_repeats=10, random_state=42, n_jobs=2
+    )
+    forest_importances = pd.Series(result.importances_mean, index=feature_names)
+    
+    fig, ax = plt.subplots()
+    forest_importances.plot.bar(yerr=result.importances_std, ax=ax)
+    ax.set_title("Feature importances using permutation on full model")
+    ax.set_ylabel("Mean accuracy decrease")
+    fig.tight_layout()
+
+    return fig
+
+    
+
+title = "Feature importances with a forest of trees 🌳"
+description = """This example shows the use of a forest of trees to evaluate the importance of features on an artificial classification task. 
+                The blue bars are the feature importances of the forest, along with their inter-trees variability represented by the error bars.
+              """
+            
+with gr.Blocks() as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(description)
+
+    # with gr.Column():
+    clf, feature_names, X_test, y_test  = train_model()
+    
+    with gr.Row():
+        plot = gr.Plot(plot_mean_decrease(clf, feature_names))
+        plot2 = gr.Plot(plot_feature_perm(clf, feature_names, X_test, y_test))
+            
+            # input_data = gr.Dropdown(choices=feature_names, label="Feature", value="body-mass index")
+            # coef = gr.Textbox(label="Coefficients")
+            # mse = gr.Textbox(label="Mean squared error (MSE)")
+            # r2 = gr.Textbox(label="R2 score")
+
+    # input_data.change(fn=train_model, inputs=[input_data], outputs=[plot, coef, mse, r2], queue=False)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
     
-demo.launch()
+demo.launch(enable_queue=True)