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import os
from flask import Flask, render_template, jsonify, request, redirect, url_for
from tensorflow.keras.models import load_model
import numpy as np
from numpy.random import randn
from matplotlib import pyplot
import base64
from io import BytesIO
app = Flask(__name__)
GENERATED_FOLDER = 'static/generated'
app.config['GENERATED_FOLDER'] = GENERATED_FOLDER
# Load your GAN model from the H5 file
model = load_model('gan.h5')
def generate_latent_points(latent_dim, n_samples):
# generate points in the latent space
x_input = randn(latent_dim * n_samples)
# reshape into a batch of inputs for the network
z_input = x_input.reshape(n_samples, latent_dim)
return z_input
def generate_images(model, latent_points):
generated_images = model.predict(latent_points)
return generated_images
# create a plot of generated images
def plot_generated(examples, n, image_size=(80, 80)):
# plot images
fig, axes = pyplot.subplots(n, n, figsize=(8, 8))
for i in range(n * n):
# turn off axis
axes.flatten()[i].axis('off')
# plot raw pixel data
axes.flatten()[i].imshow(examples[i, :, :])
# Save the plot to BytesIO
buf = BytesIO()
fig.savefig(buf, format='png')
buf.seek(0)
pyplot.close(fig)
return base64.b64encode(buf.read()).decode('utf-8')
@app.route('/')
def index():
return render_template('index.html')
@app.route('/generate', methods=['POST'])
def generate():
latent_dim = 100
n_samples = 4
latent_points = generate_latent_points(latent_dim, n_samples)
generated_images = generate_images(model, latent_points)
generated_images = (generated_images + 1) / 2.0
img_data = plot_generated(generated_images, int(np.sqrt(n_samples)))
return jsonify({'success': True, 'generated_image': img_data})
if __name__ == '__main__':
app.run(debug=True)
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