Create app.py

app.py

@@ -0,0 +1,135 @@
+import streamlit as st
+import torch
+import numpy as np
+import plotly.graph_objs as go
+
+st.markdown("""
+    <style>
+    .stApp {
+        background-color: #f9f9f9;
+        font-family: 'Segoe UI', sans-serif;
+    }
+    h1 {
+        text-align: center;
+        color: #2C3E50;
+        font-size: 38px !important;
+        font-weight: bold;
+        margin-bottom: 20px;
+    }
+    .stTextInput > div > div > input {
+        border: 2px solid #3498DB;
+        border-radius: 8px;
+        padding: 8px;
+    }
+    div.stButton > button {
+        background-color: #3498DB;
+        color: white;
+        border-radius: 10px;
+        padding: 10px 24px;
+        font-size: 16px;
+        border: none;
+        transition: 0.3s;
+    }
+    div.stButton > button:hover {
+        background-color: #2980B9;
+        transform: scale(1.05);
+    }
+    .stAlert {
+        border-radius: 8px;
+    }
+    .block-container {
+        padding-top: 2rem;
+        padding-bottom: 2rem;
+        max-width: 1200px;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+st.title("Gradient Descent Visualizer")
+
+
+func_input = st.text_input("Enter Function of x", "x**2")
+start_point = float(st.text_input("Starting Point", "2"))
+learning_rate = float(st.text_input("Learning Rate", "0.01"))
+num_iterations = int(st.text_input("Number of Iterations", "10"))
+
+def make_function(expr: str):
+    """Dynamically create a function in torch"""
+    def func(x):
+        return eval(expr, {"x": x, "torch": torch})
+    return func
+
+if st.button("Set Up") or 'func' not in st.session_state or 'points' not in st.session_state:
+    try:
+        func = make_function(func_input)
+
+        st.session_state.func = func
+        st.session_state.points = [start_point]
+        st.session_state.step = 0
+        st.success("Function Set Up Successfully with PyTorch!")
+    except Exception as e:
+        st.error(f"Error setting up function: {e}")
+
+# ------------------------ ITERATION STEP ------------------------
+def gradient_step(x_val, func, lr):
+    x = torch.tensor([x_val], dtype=torch.float32, requires_grad=True)
+    y = func(x)
+    y.backward()
+    grad = x.grad.item()
+    new_x = x_val - lr * grad
+    return new_x, grad
+
+if 'func' in st.session_state:
+    if st.button("Next Iteration"):
+        try:
+            x_old = float(st.session_state.points[-1])
+            x_new, grad_val = gradient_step(x_old, st.session_state.func, learning_rate)
+            st.session_state.points.append(x_new)
+            st.session_state.step += 1
+            st.success(f"Iteration {st.session_state.step} Complete! (grad={grad_val:.6f})")
+        except Exception as e:
+            st.error(f"Error in iteration: {e}")
+
+    if st.button("Run Iterations"):
+        try:
+            for i in range(num_iterations):
+                x_old = float(st.session_state.points[-1])
+                x_new, grad_val = gradient_step(x_old, st.session_state.func, learning_rate)
+                st.session_state.points.append(x_new)
+                st.session_state.step += 1
+            st.success(f"Ran {st.session_state.step} Iterations in total")
+        except Exception as e:
+            st.error(f"Error in multiple iterations: {e}")
+
+# ------------------------ PLOTTING ------------------------
+if 'func' in st.session_state and len(st.session_state.points) > 0:
+    try:
+        x_val = np.linspace(-10, 10, 500)
+        x_torch = torch.tensor(x_val, dtype=torch.float32)
+        y_val = st.session_state.func(x_torch).detach().numpy()
+
+        iter_points = np.array(st.session_state.points)
+        iter_torch = torch.tensor(iter_points, dtype=torch.float32)
+        iter_y = st.session_state.func(iter_torch).detach().numpy()
+
+        trace1 = go.Scatter(x=x_val, y=y_val, mode="lines", name="Function", line=dict(color="blue"))
+        trace2 = go.Scatter(x=iter_points, y=iter_y, mode="markers+lines", 
+                            name="Gradient Descent Path", marker=dict(color="red"))
+        trace3 = go.Scatter(x=[iter_points[-1]], y=[iter_y[-1]], mode='markers+text', 
+                            marker=dict(color='green', size=15), 
+                            text=[f"{iter_points[-1]:.6f}"], textposition="top center", 
+                            name="Current Point")
+
+        layout = go.Layout(
+            title=f"Iteration {st.session_state.step}",
+            xaxis=dict(title="x - axis"),
+            yaxis=dict(title="y - axis"),
+            width=1000,
+            height=600
+        )
+
+        fig = go.Figure(data=[trace1, trace2, trace3], layout=layout)
+        st.plotly_chart(fig, use_container_width=True)
+        st.success(f"Current Point = {iter_points[-1]}")
+    except Exception as e:
+        st.error(f"Plot error: {e}")