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app.py
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app.py
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import re
from collections import defaultdict, deque
from flask import Flask, Markup, render_template, request
from qiskit import IBMQ, BasicAer
from code import *
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
import base64
import io
backend = BasicAer.get_backend("qasm_simulator")
app = Flask(__name__)
app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0
@app.route("/")
def index():
return render_template("index.html")
preset_user_categories = ["USER_TEXT"]
@app.route("/preset", methods=["GET", "POST"])
def preset():
if request.method != "POST":
user_categories = preset_user_categories
return render_template(
"preset_generator.html", categories=user_categories, answered=False,
)
else:
user_categories = preset_user_categories
user_replies = [request.form.get(category) for category in user_categories]
# Generate six possibilities: Ideal, 0.01, 0.05, etc.
### Ideal
Art1 = QuantumArt(text=user_replies[0])
buffer_image = Art1.get_buffer_image()
im = Image.open(buffer_image)
data = io.BytesIO()
im.save(data, "PNG")
encoded_ideal_img_data = base64.b64encode(data.getvalue())
### 0.01
Art1.noise_art(custom_noise_vals=[0.01,0.01],fig_identifier='1')
buffer_image = Art1.get_buffer_image()
im = Image.open(buffer_image)
data = io.BytesIO()
im.save(data, "PNG")
encoded_noise_img1_data = base64.b64encode(data.getvalue())
### 0.05
Art1.noise_art(custom_noise_vals=[0.05,0.05],fig_identifier='2')
buffer_image = Art1.get_buffer_image()
im = Image.open(buffer_image)
data = io.BytesIO()
im.save(data, "PNG")
encoded_noise_img2_data = base64.b64encode(data.getvalue())
### 0.1
Art1.noise_art(custom_noise_vals=[0.1,0.1],fig_identifier='3')
buffer_image = Art1.get_buffer_image()
im = Image.open(buffer_image)
data = io.BytesIO()
im.save(data, "PNG")
encoded_noise_img3_data = base64.b64encode(data.getvalue())
### 0.2
Art1.noise_art(custom_noise_vals=[0.2,0.2],fig_identifier='4')
buffer_image = Art1.get_buffer_image()
im = Image.open(buffer_image)
data = io.BytesIO()
im.save(data, "PNG")
encoded_noise_img4_data = base64.b64encode(data.getvalue())
### 0.5
Art1.noise_art(custom_noise_vals=[0.5,0.5],fig_identifier='5')
buffer_image = Art1.get_buffer_image()
im = Image.open(buffer_image)
data = io.BytesIO()
im.save(data, "PNG")
encoded_noise_img5_data = base64.b64encode(data.getvalue())
return render_template(
"preset_generator.html",
ideal_img = encoded_ideal_img_data.decode('utf-8'),
noise_img1 = encoded_noise_img1_data.decode('utf-8'),
noise_img2 = encoded_noise_img2_data.decode('utf-8'),
noise_img3 = encoded_noise_img3_data.decode('utf-8'),
noise_img4 = encoded_noise_img4_data.decode('utf-8'),
noise_img5 = encoded_noise_img5_data.decode('utf-8'),
text = user_replies[0],
answered=True,
)
custom_user_categories = ["text", "p_meas", "p_gate1"]
@app.route("/custom", methods=["GET", "POST"])
def custom():
if request.method != "POST":
return render_template(
"custom_generator.html", categories=custom_user_categories, answered=False,
)
else:
user_categories = custom_user_categories
user_replies = [request.form.get(category) for category in user_categories]
Art2 = QuantumArt(text=user_replies[0])
Art2.noise_art(custom_noise_vals=[float(user_replies[1]),float(user_replies[2])],fig_identifier='3')
buffer_image = Art2.get_buffer_image()
im = Image.open(buffer_image)
data = io.BytesIO()
im.save(data, "PNG")
encoded_img_data_with_noise1 = base64.b64encode(data.getvalue())
return render_template(
"custom_generator.html",
noise_img = encoded_img_data_with_noise1.decode('utf-8'),
text = user_replies[0],
p_meas=user_replies[1],
p_gate1=user_replies[2],
answered=True,
)
if __name__ == "__main__":
# Threaded option to enable multiple instances for multiple user access support
app.run(threaded=True, port=5000)