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@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+iris_dataset_info.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,99 @@
+# Code source: Gaël Varoquaux
+# License: BSD 3 clause
+
+# This code is a MOD with Gradio Demo
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+
+from sklearn import decomposition
+from sklearn import datasets
+
+# unused but required import for doing 3d projections with matplotlib < 3.2
+import mpl_toolkits.mplot3d  # noqa: F401
+matplotlib.use('agg')
+
+import gradio as gr
+
+np.random.seed(5)
+
+## PCA
+def PCA_Pred(x1, x2, x3, x4):
+    #Load Data from iris dataset:
+    iris = datasets.load_iris()
+    X = iris.data
+    y = iris.target
+
+    fig, ax = plt.subplots(1, subplot_kw={'projection': '3d', 'elev': 48, 'azim': 134})
+    ax.set_position([0, 0, 0.95, 1])
+    plt.cla()
+
+    #Create the model with 3 principal components:
+    pca = decomposition.PCA(n_components=3)
+    
+    #Fit model and transform (decrease dimensions) iris dataset:
+    pca.fit(X)
+    X = pca.transform(X)
+
+    #Set labels to data clusters
+    for name, label in [("Setosa", 0), ("Versicolour", 1), ("Virginica", 2)]:
+        ax.text3D(
+            X[y == label, 0].mean(),
+            X[y == label, 1].mean() + 1.5,
+            X[y == label, 2].mean(),
+            name,
+            horizontalalignment="center",
+            bbox=dict(alpha=0.5, edgecolor="w", facecolor="w"),
+        )
+    # Reorder the labels to have colors matching the cluster results
+    y = np.choose(y, [1, 2, 0]).astype(float)
+    ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.nipy_spectral, edgecolor="k")
+
+    user_iris_data = np.array([[x1, x2, x3, x4]], ndmin=2)
+
+    #Perform reduction to user data
+    pc_output = pca.transform(user_iris_data)
+    ax.scatter(pc_output[0, 0], pc_output[0, 1], pc_output[0, 2], c='r', marker='*')
+
+    ax.xaxis.set_ticklabels([])
+    ax.yaxis.set_ticklabels([])
+    ax.zaxis.set_ticklabels([])
+
+    return [pc_output, fig]
+    
+title = "🌺 PCA example with Iris Data-set"
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(
+        """
+        ## The following app is a demo for PCA decomposition. It takes 4 dimensions as input, in reference \
+        to the Iris flower image (left), and returns the transformed first 3 principal components (feature \
+        reduction) taken from a pre-trained model with Iris dataset (Right).
+        """)
+    with gr.Row():
+        with gr.Column():
+            html1 = (
+                "<div >"
+                "<img  src='file/iris_flower_dimensions.jpg' width='597' height='460' alt='image One'>"
+                + "</div>"
+                )
+            gr.HTML(html1)
+            inp1 = gr.Slider(0, 5, value=1, step=0.1, label="Sepal Length (cm)")
+            inp2 = gr.Slider(0, 5, value=1, step=0.1, label="Sepal Width (cm)")
+            inp3 = gr.Slider(0, 5, value=1, step=0.1, label="Petal Length (cm)")
+            inp4 = gr.Slider(0, 5, value=1, step=0.1, label="Petal Width (cm)")
+            output = gr.Textbox(label="PCA Axes")
+        with gr.Column():
+            html2 = (
+                "<div >"
+                "<img  src='file/iris_dataset_info.png' alt='image two'>"
+                + "</div>"
+                )
+            gr.HTML(html2)
+            plot = gr.Plot(label="PCA 3D Space")
+
+    Reduction = gr.Button("PCA Transform")
+    Reduction.click(fn=PCA_Pred, inputs=[inp1, inp2, inp3, inp4], outputs=[output, plot])
+    demo.load(fn=PCA_Pred, inputs=[inp1, inp2, inp3, inp4], outputs=[output, plot])
+
+demo.launch()

requirements.txt

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+numpy==1.24.2
+matplotlib==3.7.1
+scikit-learn==1.2.2