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app.py
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app.py
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import requests
import json
import numpy as np
import streamlit as st
import os
import matplotlib.pyplot as plt
URL = 'http://127.0.0.1:5000/' # Change to path of the development server on your local machine
st.title('Neural Network Visualizer')
st.sidebar.markdown('## Input Image ##')
if st.button('Get Predictions'):
response = requests.post(URL, data={})
response = json.loads(response.text)
# load predictions and image from server
preds = response.get('prediction')
image = response.get('image')
image = np.reshape(image, (28, 28))
# input image displayed in sidebar
st.sidebar.image(image, width=150)
for ps_layer, p in enumerate(preds):
numbers = np.squeeze(np.array(p))
plt.figure(figsize=(32, 4))
layer = 0
if ps_layer == 0:
continue
elif ps_layer == 1:
row = 4
col = 16
elif ps_layer == 2:
continue
elif ps_layer == 3:
row = 2
col = 16
layer += 1
elif ps_layer == 4:
row = 1
col = 16
layer += 1
else:
row = 1
col = 10
layer += 1
for i, number in enumerate(numbers):
plt.subplot(row, col, i + 1)
plt.imshow((number * np.ones((8, 8, 3))).astype('float32'), cmap='binary')
plt.xticks([])
plt.yticks([])
if ps_layer == 5:
plt.xlabel(str(i), fontsize=40)
plt.subplots_adjust(wspace=0.05, hspace=0.05)
plt.tight_layout()
st.text('Layer {}'.format(layer + 1), )
st.pyplot()