Create app.py

app.py

@@ -0,0 +1,56 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import neighbors
+
+def train_and_plot(weights, n_neighbors):
+    np.random.seed(0)
+    X = np.sort(5 * np.random.rand(40, 1), axis=0)
+    T = np.linspace(0, 5, 500)[:, np.newaxis]
+    y = np.sin(X).ravel()
+
+    # Add noise to targets
+    y[::5] += 1 * (0.5 - np.random.rand(8))
+
+    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)
+    fit = knn.fit(X, y)
+    y_ = knn.predict(T)
+    score = knn.score(T, y_)
+
+    plt.scatter(X, y, color="darkorange", label="data")
+    plt.plot(T, y_, color="navy", label="prediction")
+    plt.axis("tight")
+    plt.legend()
+    plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors, weights))    
+
+    plt.tight_layout()
+    return plt, score
+
+
+with gr.Blocks() as demo:
+    link = "https://scikit-learn.org/stable/auto_examples/neighbors/plot_regression.html#sphx-glr-auto-examples-neighbors-plot-regression-py"
+    gr.Markdown("## Nearest Neighbors regression")
+    gr.Markdown(f"This demo is based on this [scikit-learn example]({link}).")
+    gr.HTML("<hr>")
+    gr.Markdown("In this demo, we learn a noise-infused sine function using k-Nearest Neighbor and observe how the function learned varies as we change the following hyperparameters:")
+    gr.Markdown("""1. Weight function
+    2. Number of neighbors""")            
+    
+    with gr.Row():
+        weights = gr.Radio(['uniform', "distance"], label="Weights", info="Choose the weight function")
+        n_neighbors = gr.Slider(label="Neighbors", info="Choose the number of neighbors", minimum =1, maximum=15, step=1) 
+    
+    btn = gr.Button(value="Submit")
+            
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            plot = gr.Plot(label="KNeighborsRegressor Plot")
+        with gr.Column(scale=1):
+            num = gr.Textbox(label="Test Accuracy")
+
+        
+    btn.click(train_and_plot,  inputs=[weights, n_neighbors], outputs=[plot, num])
+    
+
+if __name__ == "__main__":
+    demo.launch()