Initial commit: Gradio regression visualization

LICENSE

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+MIT License
+
+Copyright (c) 2025 Tanittha
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

linear_regression_gradio.py

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+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+
+# Synthetic dataset
+np.random.seed(42)
+x = np.linspace(0, 5, 100).reshape(-1, 1)
+y = 2 * x + np.random.normal(0, 0.5, x.shape)
+
+# Explicit solution model
+lin_reg = LinearRegression()
+lin_reg.fit(x, y)
+
+def visualize_model(lr=0.01, epochs=50):
+    # Gradient Descent training
+    w, b = 0.0, 0.0
+    n = len(x)
+    losses = []
+    for epoch in range(epochs):
+        y_pred = w * x + b
+        loss = np.mean((y - y_pred)**2)
+        losses.append(loss)
+        dw = (-2/n) * np.sum((y - y_pred) * x)
+        db = (-2/n) * np.sum(y - y_pred)
+        w -= lr * dw
+        b -= lr * db
+
+    # Plot predictions + loss curve
+    fig, axs = plt.subplots(1, 2, figsize=(10, 4))
+
+    # Left: data + predictions
+    axs[0].scatter(x, y, label="Data", alpha=0.6)
+    axs[0].plot(x, lin_reg.predict(x), label="Explicit Solution", color="green")
+    axs[0].plot(x, w * x + b, label="Gradient Descent", color="red")
+    axs[0].legend()
+    axs[0].set_title("Model Predictions")
+
+    # Right: loss curve
+    axs[1].plot(range(epochs), losses, color="blue")
+    axs[1].set_title("Loss vs Epoch")
+    axs[1].set_xlabel("Epoch")
+    axs[1].set_ylabel("Loss")
+
+    return fig
+
+demo = gr.Interface(
+    fn=visualize_model,
+    inputs=[
+        gr.Slider(0.001, 0.1, value=0.01, label="Learning Rate"),
+        gr.Slider(10, 200, value=50, step=10, label="Epochs")
+    ],
+    outputs=gr.Plot(),
+    title="Linear Regression Visualization",
+    description="Adjust learning rate and epochs to see Gradient Descent vs Explicit Solution."
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)
+

requirements.txt

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+numpy
+matplotlib
+scikit-learn
+mlflow
+wandb
+gradio