Create app.py

app.py

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+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import streamlit as st
+
+# Function definition
+def loss_function(x):
+    return x**2
+
+# Gradient of the loss function
+def gradient(x):
+    return 2 * x
+
+# Gradient descent algorithm
+def gradient_descent(starting_point, learning_rate, iterations):
+    x_values = [starting_point]
+    for _ in range(iterations):
+        gradient_value = gradient(x_values[-1])
+        new_x = x_values[-1] - learning_rate * gradient_value
+        x_values.append(new_x)
+    return x_values
+
+# Streamlit layout
+st.title("Gradient Descent Visualization using Seaborn")
+
+# Sidebar inputs
+st.sidebar.header("Parameters")
+starting_point = st.sidebar.slider("Starting Point", -10.0, 10.0, 5.0, 0.1)
+learning_rate = st.sidebar.slider("Learning Rate", 0.01, 1.0, 0.1, 0.01)
+iterations = st.sidebar.slider("Number of Iterations", 1, 50, 10, 1)
+
+# Perform gradient descent
+x_values = gradient_descent(starting_point, learning_rate, iterations)
+y_values = [loss_function(x) for x in x_values]
+
+# Generate plot using Seaborn
+sns.set(style="whitegrid")
+fig, ax = plt.subplots(figsize=(8, 5))
+x_range = np.linspace(-10, 10, 500)
+y_range = loss_function(x_range)
+
+# Plot the loss function
+sns.lineplot(x=x_range, y=y_range, ax=ax, label="Loss Function", color="blue")
+# Plot gradient descent steps
+sns.scatterplot(x=x_values, y=y_values, ax=ax, color="red", label="Gradient Steps", zorder=5)
+sns.lineplot(x=x_values, y=y_values, ax=ax, color="orange", linestyle="--", label="Path")
+
+# Annotate points
+for i, (x, y) in enumerate(zip(x_values, y_values)):
+    ax.text(x, y, f"{i}", fontsize=8, ha="right")
+
+ax.set_title("Gradient Descent")
+ax.set_xlabel("x")
+ax.set_ylabel("Loss")
+ax.legend()
+
+# Display in Streamlit
+st.pyplot(fig)
+
+# Display final results
+st.write("### Gradient Descent Results")
+st.write(f"**Starting Point:** {starting_point}")
+st.write(f"**Learning Rate:** {learning_rate}")
+st.write(f"**Number of Iterations:** {iterations}")
+st.write(f"**Final x Value:** {x_values[-1]:.4f}")
+st.write(f"**Final Loss Value:** {loss_function(x_values[-1]):.4f}")