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app.py

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+# Import libraries
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.datasets import fetch_olivetti_faces
+from sklearn.utils.validation import check_random_state
+
+from sklearn.ensemble import ExtraTreesRegressor
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import RidgeCV
+
+import gradio as gr
+
+# Load the faces datasets
+data, targets = fetch_olivetti_faces(return_X_y=True) 
+
+train = data[targets < 30]  
+
+n_pixels = data.shape[1] 
+
+# Training data
+# Upper half of the faces
+X_train = train[:, : (n_pixels + 1) // 2] 
+# Lower half of the faces
+y_train = train[:, n_pixels // 2 :] 
+
+# Fit estimators -> The problem (given half the image/features extrapolate the rest of the image/features)
+ESTIMATORS = {
+    "Extra trees": ExtraTreesRegressor(
+        n_estimators=10, max_features=32, random_state=0
+    ),
+    "K-nn": KNeighborsRegressor(),
+    "Linear regression": LinearRegression(),
+    "Ridge": RidgeCV(),
+}
+
+for name, estimator in ESTIMATORS.items():
+    estimator.fit(X_train, y_train)
+
+test = data[targets >= 30]  
+n_faces = 15
+rng = check_random_state(4)
+face_ids = rng.randint(test.shape[0], size=(n_faces,))
+test = test[face_ids, :]
+
+# Function for returning 64*64 image, given the image index
+def imageFromIndex(index):
+  return test[int(index)].reshape(1,-1).reshape(64, 64)
+
+# Function for extrapolating face
+def extrapolateFace(index, ESTIMATORS=ESTIMATORS):
+  image = test[int(index)].reshape(1,-1)
+  image_shape = (64, 64)
+  n_cols = 1 + len(ESTIMATORS)
+  n_faces = 1
+
+  n_pixels = image.shape[1]
+
+  # Upper half of the face
+  X_upper = image[:, : (n_pixels + 1) // 2]
+  # Lower half of the face
+  y_ground_truth = image[:, n_pixels // 2 :]
+
+  # y_predict: Dictionary of predicted lower-faces
+  y_predict = dict() 
+  for name, estimator in ESTIMATORS.items():
+    y_predict[name] = estimator.predict(X_upper)
+  
+
+  plt.figure(figsize=(2.0 * n_cols, 2.5 * n_faces))
+  # plt.suptitle("Face completion with multi-output estimators", size=16)
+
+
+  true_face = np.hstack((X_upper, y_ground_truth))
+
+  sub = plt.subplot(n_faces, n_cols, 1, title="true face")
+
+  sub.axis("off")
+  sub.imshow(
+      true_face.reshape(image_shape), cmap=plt.cm.gray, interpolation="nearest"
+  )
+
+  for j, est in enumerate(sorted(ESTIMATORS)):
+      completed_face = np.hstack((X_upper[0], y_predict[est][0]))
+
+      sub = plt.subplot(n_faces, n_cols, 2 + j, title=est)
+      sub.axis("off")
+      sub.imshow(
+          completed_face.reshape(image_shape),
+          cmap=plt.cm.gray,
+          interpolation="nearest",
+    )
+  
+  return plt
+
+with gr.Blocks() as demo:
+    link = "https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_multioutput_face_completion.html#sphx-glr-auto-examples-miscellaneous-plot-multioutput-face-completion-py"
+    title = "Face completion with a multi-output estimators"
+    gr.Markdown(f"# {title}")
+    gr.Markdown(f"This demo is based on this [scikit-learn example]({link}).")
+    gr.Markdown("In this demo, we compare 4 multi-output estimators to complete images. \
+    The goal is to predict the lower half of a face given its upper half.")
+
+    gr.Markdown("#### Use the below slider to choose a face's image. \
+    Consequently, observe how the four estimators complete the lower half of that face.")
+
+    with gr.Row():
+        with gr.Column(scale=1):  
+          image_index = gr.Slider(1,15,1,step=1, label="Image Index", info="Choose an image") 
+          face_image = gr.Image()   
+        with gr.Column(scale=2):
+          plot = gr.Plot(label=f"Face completion with multi-output estimators")
+
+    image_index.change(imageFromIndex, inputs=[image_index], outputs=[face_image])
+    image_index.change(extrapolateFace, inputs=[image_index], outputs=[plot])
+    demo.load(imageFromIndex, inputs=[image_index], outputs=[face_image])
+    demo.load(extrapolateFace, inputs=[image_index], outputs=[plot])
+
+if __name__ == "__main__":
+    demo.launch(debug=True)

requirements.txt

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+numpy
+matplotlib
+scikit-learn
+gradio