Fix .np by .jnp

app.py

@@ -23,8 +23,8 @@ cost_function = st.sidebar.radio('What cost function you want to use for the fit
 # Generate random data
 X = np.column_stack((jnp.ones(number_of_observations),
                      jax.random.uniform(key, shape=(number_of_observations,), minval=0., maxval=1.))
-w = np.array([3.0, -20.0, 32.0])  # coefficients                                    
-X = np.column_stack((X, X[:,1] ** 2))   # add x**2 column
+w = jnp.array([3.0, -20.0, 32.0])  # coefficients                                    
+X = jnp.column_stack((X, X[:,1] ** 2))   # add x**2 column
 additional_noise = 8 * jax.random.bernoulli(key, p=0.08, shape=[number_of_observations,])
 y = jnp.dot(X, w) + noise_standard_deviation * jax.random.normal(key, shape=[number_of_observations,]) \
         + additional_noise