Create app.py

app.py

@@ -0,0 +1,51 @@
+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Set Streamlit page config
+st.set_page_config(page_title="Sigmoid Curve Visualizer", page_icon="📈")
+
+# Title
+st.title("📈 Sigmoid Activation Function Visualizer")
+
+st.write("""
+The **sigmoid function** is defined as:  
+\[
+\sigma(z) = \frac{1}{1 + e^{-z}}
+\]  
+It smoothly maps any real number to a value between 0 and 1, making it useful for **probability outputs** in neural networks.
+""")
+
+# Sidebar controls
+st.sidebar.header("⚙️ Controls")
+min_z = st.sidebar.slider("Minimum z value", -20, 0, -10)
+max_z = st.sidebar.slider("Maximum z value", 0, 20, 10)
+points = st.sidebar.slider("Number of points", 50, 1000, 400)
+
+# Sigmoid function
+def sigmoid(z):
+    return 1 / (1 + np.exp(-z))
+
+# Generate values
+z = np.linspace(min_z, max_z, points)
+sig = sigmoid(z)
+
+# Plot
+fig, ax = plt.subplots(figsize=(6,4))
+ax.plot(z, sig, label=r'$\sigma(z) = \frac{1}{1+e^{-z}}$', color='blue')
+ax.axhline(0.5, color='gray', linestyle='--', linewidth=0.8)
+ax.axvline(0, color='gray', linestyle='--', linewidth=0.8)
+ax.set_title("Sigmoid Activation Function", fontsize=14)
+ax.set_xlabel("z (Weighted Sum)")
+ax.set_ylabel("Output (Probability)")
+ax.grid(True, linestyle='--', alpha=0.6)
+ax.legend()
+
+st.pyplot(fig)
+
+# Example interpretation
+st.subheader("🔍 Interpretation Example")
+z_input = st.number_input("Enter a z value:", value=0.0, step=0.1)
+probability = sigmoid(z_input)
+st.write(f"Sigmoid({z_input}) = **{probability:.4f}** → This means {probability*100:.2f}% probability.")
+