Update app.py

app.py

@@ -1,4 +1,62 @@
 import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+import sympy as sp
 
-st.title("👋 Hey There")
-st.header("Let's play with gradient descent.")
+# --- Streamlit App ---
+st.title("🔽 Gradient Descent Visualizer")
+
+# Function input
+st.subheader("Define Function")
+func_str = st.text_input("Enter a function in terms of x:", "x**2 + x")
+
+# Sympy setup
+x = sp.symbols("x")
+try:
+    func = sp.sympify(func_str)
+    derivative = sp.diff(func, x)
+except Exception as e:
+    st.error(f"Invalid function: {e}")
+    st.stop()
+
+# Parameters
+st.subheader("Parameters")
+start_point = st.number_input("Starting Point", value=5.0)
+learning_rate = st.number_input("Learning Rate", value=0.25, step=0.01)
+iterations = st.slider("Number of Iterations", 1, 50, 16)
+
+# Convert sympy to numpy function
+f_np = sp.lambdify(x, func, "numpy")
+fprime_np = sp.lambdify(x, derivative, "numpy")
+
+# Gradient descent iterations
+points = [start_point]
+for i in range(iterations):
+    grad = fprime_np(points[-1])
+    new_point = points[-1] - learning_rate * grad
+    points.append(new_point)
+
+# Final point
+current_point = points[-1]
+
+# Plot function and descent path
+st.subheader(f"Iteration {iterations}")
+x_vals = np.linspace(-6, 6, 400)
+y_vals = f_np(x_vals)
+
+fig, ax = plt.subplots()
+ax.plot(x_vals, y_vals, label=str(func))
+ax.axhline(0, color="brown", linewidth=1)
+ax.axvline(0, color="gray", linewidth=1)
+
+# Plot descent points
+y_points = f_np(np.array(points))
+ax.plot(points, y_points, "ro-")
+
+ax.set_xlabel("x - axis")
+ax.set_ylabel("y - axis")
+ax.legend()
+st.pyplot(fig)
+
+# Show current point
+st.success(f"📍 Current Point: {current_point}")