Seeding and reusing Python random seeds

I'm using Python and Flask to display a randomized game board, and trying to allow people to return to the same game by using a seed.

However, whether I use a random seed, or specify a seed, I seem to get the same pseudorandom sequences.

I cut out the majority of my code (I do a lot of splitting and joining with numpy) but even the simple code below shows the bug: no matter what value of seed I give the form, the number displayed on submit is the same. Submitting the form without specifying the seed shows a different number, but despite showing different seed values on reloading, that other number is always the same as well.

Am I doing something wrong with seeding?

from flask import Flask, request, render_template
import numpy as np
import random

app = Flask(__name__)

@app.route( '/' )
def single_page():
   return render_template( 'page.html', title = 'empty form' )

@app.route( '/number', methods = [ 'POST', 'GET' ] )
def render_page( title = 'generated random number', error = [] ):
   error = []
   if request.method == 'POST':
      if request.form['seed'].isdigit():
         seed = int( request.form['seed'] )
         error.append( "seed set: " + str( seed ) + "." )
         np.random.seed( seed/100000 )
      else:
         seed = int( 100000 * random.random() )
         error.append( "seed not set, " + str( seed ) + " instead." )
         np.random.seed( seed/100000 )

      n = np.random.random() * 100;

      return render_template('page.html', title=title, error=error, n=n, seed=seed )

   else:
      return render_template( 'page.html', title = 'empty form' )

if __name__ == '__main__':
   app.debug = True
   app.run()

Here is the flask HTML template

<!doctype html>
<html>
<head><title>{{title}}</title>
</head>
<body>
{% if error != '' %}
{% for message in error %}
    <h2>{{message}}</h2>
{% endfor %}
{% endif %}

{% if n %}
    <h2>Random number is {{n}}</h2>

    <h6>seed = {{ seed }}</h6>
{% else %}
    <div id="form">
    <form id="the_form" method="POST" action="number">
    Seed: <input type="number" min="1" max="99999" id="seed" name="seed"><br>
    <button id="submit" type="submit">Submit</button>
    </form>
{% endif %}
</div>
</body>
</html>

I multiply and divide the seeds by 100,000 so as to give a more memorable value (say, 4231 instead of 4.231479094...). Is there is a better way to have usable integer seed values?

UPDATED: Yes, there is a better way to do integer seed values - not mess with dividing at all. For the time being this is what I'm doing:

import numpy as np
import random
.
.
.
      if request.form['seed'].isdigit():
         seed = int( request.form['seed'] )
         error.append( "seed set: " + str( seed ) + "." )
         random.seed( seed )
      else:
         seed = int( 100000 * np.random.random() )
         error.append( "seed not set, " + str( seed ) + " instead." )
         random.seed( seed )

      n = random.random() * 100;

      return render_template('page.html', title=title, error=error, n=n, seed=seed )

This works fine. np.random.seed() didn't seem to always get the same sequence, but random.seed() doesn't mind an integer, so I'm using the latter.

Your seed is probably an integer and integer division in early Python won't give a float. Thus

7078 / 100000 = 0

This always gives a seed of zero if seed is < 100000. With this:

np.random.seed( seed )

The seed should change. Without an argument np.random.seed should try to take a (system-dependent) seed.

If you want to read up on the PIP that "fixes" this the division: see PEP 238. In Python 3 this 2/5=0.4 in Python 2.X 2/5=0. You can force floating point upcasting at the top of your code by including the line:

from __future__ import division

Why use np.random instead of Python's random?

From the documentation:

The Python stdlib module “random” also contains a Mersenne Twister pseudo-random number generator with a number of methods that are similar to the ones available in RandomState. RandomState, besides being NumPy-aware, has the advantage that it provides a much larger number of probability distributions to choose from.