Create app.py

app.py

@@ -0,0 +1,69 @@
+import streamlit as st
+import numpy as np
+from sklearn.datasets import make_circles
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.optimizers import Adam
+import matplotlib.pyplot as plt
+
+# Set Streamlit page config
+st.set_page_config(page_title="ML Playground", layout="centered")
+
+st.title("🧠 TensorFlow Playground Clone")
+st.markdown("Train a simple neural network on a synthetic dataset like circles")
+
+# Sidebar controls
+st.sidebar.header("Model Settings")
+
+# Dataset
+dataset = st.sidebar.selectbox("Dataset", ["Circle"])
+n_samples = st.sidebar.slider("Number of Samples", 100, 1000, 300)
+
+# Model settings
+layers = st.sidebar.text_input("Network Shape (comma-separated)", "4,2")
+activation = st.sidebar.selectbox("Activation", ["relu", "tanh", "sigmoid"])
+learning_rate = st.sidebar.slider("Learning Rate", 0.001, 0.1, 0.03, step=0.001)
+epochs = st.sidebar.slider("Epochs", 10, 200, 50)
+
+# Generate dataset
+X, y = make_circles(n_samples=n_samples, factor=0.5, noise=0.05, random_state=0)
+X = StandardScaler().fit_transform(X)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
+
+# Build model
+model = Sequential()
+layer_sizes = [int(n.strip()) for n in layers.split(",") if n.strip().isdigit()]
+input_dim = X.shape[1]
+
+# Input + hidden layers
+model.add(Dense(layer_sizes[0], input_dim=input_dim, activation=activation))
+for size in layer_sizes[1:]:
+    model.add(Dense(size, activation=activation))
+
+# Output layer
+model.add(Dense(1, activation='sigmoid'))
+
+optimizer = Adam(learning_rate=learning_rate)
+model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
+
+# Training
+with st.spinner("Training model..."):
+    history = model.fit(X_train, y_train, epochs=epochs, verbose=0, validation_data=(X_test, y_test))
+
+# Plotting
+fig, ax = plt.subplots()
+ax.plot(history.history['accuracy'], label='Train Accuracy')
+ax.plot(history.history['val_accuracy'], label='Val Accuracy')
+ax.set_title("Training Progress")
+ax.set_xlabel("Epoch")
+ax.set_ylabel("Accuracy")
+ax.legend()
+st.pyplot(fig)
+
+# Final Accuracy
+train_acc = model.evaluate(X_train, y_train, verbose=0)[1]
+test_acc = model.evaluate(X_test, y_test, verbose=0)[1]
+st.success(f"✅ Final Training Accuracy: {train_acc:.2f}")
+st.success(f"✅ Final Testing Accuracy: {test_acc:.2f}")