I'm trying to upload images to an Amazon S3 bucket from a Flask app. Here is my code:
def s3upload(image, acl='public-read'):
key = app.config['S3_KEY']
secret = app.config['S3_SECRET']
bucket = app.config['S3_BUCKET']
conn = S3Connection(key, secret)
mybucket = conn.get_bucket(bucket)
r = redis.StrictRedis(connection_pool = pool)
iid = r.incr('image')
now = time.time()
r.zadd('image:created_on', now, iid)
k = Key(mybucket)
k.key = iid
k.set_contents_from_string(image.read())
return iid
#app.route('/', methods = ['GET', 'POST'])
def index():
form = ImageForm(request.form)
print 'CHECKING REQUEST'
if request.method == 'POST' and form.image:
print 'VALID REQUEST'
image = form.image.read()
upload = s3upload(image)
print upload
else:
image = None
r = redis.StrictRedis(connection_pool = pool)
last_ten = r.zrange('image:created_on', 0, 9)
print last_ten
images = []
key = app.config['S3_KEY']
secret = app.config['S3_SECRET']
bucket = app.config['S3_BUCKET']
conn = S3Connection(key, secret)
mybucket = conn.get_bucket(bucket)
for image in last_ten:
images.append(mybucket.get_key(image, validate = False))
return render_template('index.html', form=form, images=images, image=image)
However I get an error at k.set_contents_from_string(image.read()) saying 'FileField' object has no attribute 'read'. Everything I've ready has indicated this is the proper way to upload an image to S3 and I've found several examples where they call .read() on a FileField object and it works fine. Thanks for your help.
FileField objects have a data attribute:
k.set_contents_from_string(image.data.read())
How about
import os
filestream = form.image.raw_data[0]
filestream.seek(0, os.SEEK_END)
read_data = filestream.tell()
or
read_data = form.image.raw_data[0].read()
Related
I'm uploading images to Digital Ocean Spaces using boto3. It's working really good until I add PILL.
In django view I have this code when I get the image:
from digitalocean_spaces import DigitalOceanSpaces
from helpers import resize_maintain_its_aspect_ratio
def images_view(request):
if request.method == "POST":
images = request.FILES.getlist('images')
for index, image in enumerate(images):
size = image.size
content_type = image.content_type
file_name = image.name
# TODO: fix method
# image = resize_maintain_its_aspect_ratio(image, 500)
DigitalOceanSpaces().upload_file(
key=key,
file=image,
content_type=content_type,
acl='private'
)
I can see all the information of each image.
To upload the image I use this method that is working too:
class DigitalOceanSpaces:
def default_session_client(self):
session = boto3.session.Session()
client = session.client(
's3',
region_name=REGION_NAME,
endpoint_url=ENDPOINT_URL,
aws_access_key_id=ACCESS_KEY_ID,
aws_secret_access_key=ACCESS_SECRET_KEY
)
return client
def upload_file(self, key, file, content_type, acl='private'):
client = self.default_session_client()
client.put_object(
Bucket=BUCKET_NAME,
Key=key,
Body=file,
ACL=acl,
ContentType=content_type,
Metadata={
'x-amz-meta-my-key': '*****'
}
)
The problem start when I call this another method to resize the image
from PIL import Image
def resize_maintain_its_aspect_ratio(image, base_width):
pillow_image = Image.open(image)
width_percent = (base_width / float(pillow_image.size[0]))
height_size = int((float(pillow_image.size[1]) * float(width_percent)))
resized_image = pillow_image.resize((base_width, height_size), Image.ANTIALIAS)
return resized_image
I see the error even if resize_maintain_its_aspect_ratio method just have:
pillow_image = Image.open(image)
So, the error is:
An error occurred (BadDigest) when calling the PutObject operation
(reached max retries: 4): Unknown
Does anyone know what the problem is ?
I have an Image object that is required to be passed to the client side and display on the page. Below is what I tried but to not avail.
The model_predict function is receiving the image from client for classification. After the classification has been done then the model will generate an image to be passed back to the client in function upload().
def model_predict(img_path, model):
img = image.load_img(img_path, target_size=(224, 224))
...
leaf_result = cv2.bitwise_and(img, img, mask=mask)
im = Image.fromarray(leaf_result)
# decoded_class = decode(img)
im.save(f"./output.png")
preds = model.predict(x)
return preds,im
#app.route('/predict', methods=['GET', 'POST'])
def upload():
if request.method == 'POST':
# Get the file from post request
f = request.files['file']
# Save the file to ./uploads
basepath = os.path.dirname(__file__)
file_path = os.path.join(
basepath, 'uploads', secure_filename(f.filename))
f.save(file_path)
prediction, im = model_predict(file_path, model)
....
result_list = list()
result_list.append(prediction1)
result_list.append(prediction2)
return jsonify(result_list, base64.decodebytes(im))
return None
Base64 Encoding is what you need: send base64 data to client, then your client will decode it. Flask to return image
For e.g:
import io
import base64
# import flask
from PIL import Image
def get_encoded_img(image_path):
img = Image.open(image_path, mode='r')
img_byte_arr = io.BytesIO()
img.save(img_byte_arr, format='PNG')
my_encoded_img = base64.encodebytes(img_byte_arr.getvalue()).decode('ascii')
return my_encoded_img
...
# your api code
...
img_path = 'assets/test.png'
img = get_encoded_img(img_path)
# prepare the response: data
response_data = {"key1": value1, "key2": value2, "image": img}
# return flask.jsonify(response_data )
When your client receives base64 data, it can display that image by using naive JS. How to display Base64 images in HTML
For e.g:
<img id="image"/>
<script>
var ticket = {
"validationDataValidDate": "2019-05-30",
"validationImage": "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"
};
document.querySelectorAll('[id="image"]')[0].src = 'data:image/png;base64, '+ticket["validationImage"];
</script>
i am starting with Python and while i was working on a project found this issue:
I have 2 .py files. One that has a Login and one that has a chart built with the Streamlit library.
The thing is that i want to redirect the user to the chart once he logs in. I havent found any response to this issue or i just dont get it.
What i think i have to do is wrap the Streamlit Graph file into a function but i dont know how.
Thanks for anyone reading this.
Login File:
from flask import Flask, render_template, redirect, url_for,request, session
from flask_mysql_connector import MySQL
import pymysql
from templates.home import get_input
app = Flask(__name__)
app.secret_key = "120395"
app.config["MYSQL_HOST"] = "localhost"
app.config["MYSQL_USER"] = "root"
app.config["MYSQL_PASSWORD"] = "anto1203"
app.config["MYSQL_DB"] = "amsterdamdb"
db = MySQL(app)
#app.route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
if ('email_usuario_form' in request.form) and ('password_usuario_form' in request.form):
usuarios_email_py = request.form['email_usuario_form']
usuarios_password_py = request.form['password_usuario_form']
cursor = db.connection.cursor(pymysql.cursors.DictCursor)
cursor.execute("SELECT USUARIOS_EMAIL, USUARIOS_LOGIN_PASSWORD FROM amsterdamdb.usuarios WHERE "
"USUARIOS_EMAIL=%s AND USUARIOS_LOGIN_PASSWORD=%s", (usuarios_email_py,
usuarios_password_py))
info = cursor.fetchone()
print(info)
if info is None:
return "Usuario y contraseña incorrectos"
else:
if info[0] == usuarios_email_py and info[1] == usuarios_password_py:
session['loginsuccess'] = True
return redirect(url_for("home"))
return render_template("login.html")
#app.route('/registro')
def registro():
return render_template("register.html")
#app.route('/home')
def home():
if session['loginsuccess'] == True:
return get_input()
if __name__ == '__main__':
app.run(debug=True)
Streamlit Graph File
import streamlit as st
import pandas as pd
from PIL import Image
from datetime import datetime, timedelta
import json
import requests
import altair as alt
st.write("""
# Bienvenido a Ámsterdam
**Mostrando información del mercado de acciones**
""")
st.sidebar.header('Ingrese parámetros')
def get_input():
aux_datetime = datetime.now()
today_date = aux_datetime.date()
treinta_dias_date = today_date - timedelta(days=30)
get_fecha_desde = st.sidebar.text_input("Fecha Inicial", treinta_dias_date )
get_fecha_hasta = st.sidebar.text_input("Fecha Final", today_date)
get_nemotecnico = st.sidebar.text_input("Identificador", "goog" )
return get_fecha_desde, get_fecha_hasta, get_nemotecnico
def get_data(nemotecnico_empresa, fecha_desde, fecha_hasta):
nemotecnico = nemotecnico_empresa
from_input_fecha_desde,from_input_fecha_hasta,from_input_nemotecnico = get_input()
fecha_desde = datetime.strptime(from_input_fecha_desde, '%Y-%m-%d')
fecha_hasta = datetime.strptime(from_input_fecha_hasta, '%Y-%m-%d')
nemotecnico = from_input_nemotecnico.upper()
fecha_desde_tmsp = str(round(datetime.timestamp(fecha_desde)))
fecha_hasta_tmsp = str(round(datetime.timestamp(fecha_hasta)))
if nemotecnico=="":
r = requests.get(
'https://finnhub.io/api/v1/stock/candle?symbol=&resolution=1&from=0&to=0&token=btagn3v48v6vivh8p9n0')
accionesJson = r.json()
else:
r = requests.get(
'https://finnhub.io/api/v1/stock/candle?symbol=' + nemotecnico + '&resolution=D&from=' + fecha_desde_tmsp + '&to=' + fecha_hasta_tmsp + '&token=wathever')
accionesJson = r.json()
open_values = accionesJson["o"]
close_values = accionesJson["c"]
fecha_values = accionesJson["t"]
fecha_values_size = len(fecha_values)
fecha_values_int = []
fecha_values_datetime = []
for x in range(0, fecha_values_size):
fecha_values_int.append(int(fecha_values[x]))
for i in range(0, fecha_values_size):
valor_aux = datetime.fromtimestamp((fecha_values_int[i]))
fecha_values_datetime.append(valor_aux.strftime("%m/%d/%Y"))
data_ordenada = {'Apertura': open_values,
'Cierre': close_values,
'Fecha': fecha_values_datetime}
chart_data = pd.DataFrame(data_ordenada)
source = chart_data
alt_chart = alt.Chart(source).transform_fold(
['Apertura', 'Cierre']
).mark_line().encode(
x=alt.X('Fecha:O',axis=alt.Axis(title="Fechas")),
y=alt.Y('value:Q',scale=alt.Scale(zero=False), axis=alt.Axis(title="Valor")),
tooltip=['Fecha', 'Apertura', 'Cierre'],
color='key:N'
).interactive().properties(
width=800,
height=500
)
st.write("", "", alt_chart)
st.header('Puntos de interés')
st.write(source.describe())
print(accionesJson)
I am building a gallery for my Flask website and am having trouble generating thumbnails in my CMS. I have a function that takes the uploaded images (stored in a list), I duplicate the list, and use zip() to iterate through both of the lists. I make thumbnails with one list and I upload the full images with the other list and I send both to AWS S3. The issue is that I do make the thumbnails but the full images get uploaded as images with sizes of 0 bytes. I also have a function that renames the images. That is working, so I assume that I am doing something right.
Why am I getting files of 0 bytes? How does that happen? What is 'emptying' the file?
#application.route("/upload", methods=['GET', 'POST'])
#login_required
def upload():
form = UploadForm()
uploaded_files = request.files.getlist("photo")
conn = boto3.client(
's3',
region_name="region",
aws_access_key_id='aws_access_key_id',
aws_secret_access_key='aws_secret_access_key'
)
bucket_name = "bucket"
if form.validate_on_submit():
i = 1
thumbs_list = list(uploaded_files)
for z, w in zip(thumbs_list, uploaded_files):
x = photo_rename(w)
thumb = save_thumbnail(z)
conn.upload_fileobj(x.stream, bucket_name, 'gallery/fulls/'+w.filename)
conn.upload_fileobj(thumb, bucket_name, 'gallery/thumbs/'+w.filename)
form_commit = Gallery(event=form.event.data,
date=form.date.data,
image_order=i,
image_file_fl=w.filename,
image_file_th=w.filename
)
db.session.add(form_commit)
i += 1
db.session.commit()
return '', 204
I was able to get both uploads to work using io.BytesIO() in my for loop:
for z, w in zip(thumbs_list, uploaded_files):
x = photo_rename(w)
c = Image.open(x)
in_mem_file = io.BytesIO()
c.save(in_mem_file, "JPEG")
in_mem_file.seek(0)
thumb = save_thumbnail(z)
conn.upload_fileobj(in_mem_file, bucket_name, 'gallery/fulls/'+w.filename)
I am trying to run python with html using flask locally, i try to render a template within a class object, it compiles but when i try to access it on http://localhost:5000 it gives me 404. Could anyone tell what I'm doing wrong here?
I am trying to display values from a json format using chart.js library.
from flask import Flask
from flask import render_template
import os.path
import json
app = Flask(__name__)
#import the json file
_player_json_file = os.path.join(os.path.dirname(__file__), 'players.json')
def read_players(player_file=None):
if player_file is None:
player_file = _player_json_file
try:
data = json.loads(open(player_file).read())
except IOError:
return {}
#make player dictionary
players = {}
#get player ID from json file = data.
for playerID in data:
players[playerID] = Player_data(data[playerID])
return players
class Players_Data (object):
#app.route("/")
def __init__(self, data):
"""
Assign all the values from the json file to new variables
the values are birth date age weight...
"""
self.player_ID = data['gsis_id']
self.gsis_name = data.get('gsis_name', '')
self.player_fullname = data.get('full_name', '')
self.player_first_name = data.get('first_name', '')
self.player_last_name = data.get('last_name', '')
self.player_weight = data.get('weight','')
self.player_height = data.get('height' , '')
self.player_birth = data.get('birthdate', '')
self.player_pro_years = data.get('years_pro', '')
self.player_team = data.get('data', '')
values = [player_ID,gsis_name,player_fullname,player_first_name,player_last_name,player_weight]
return render_template('chart.html', data=data, values=values)
if __name__ == "__main__":
app.run(host='localhost')
I don't think template rendering is the issue here. You can just return a string from your view as an example.
You get a 404 because that's not how you do classy views in flask. You can have a look here: http://flask.pocoo.org/docs/0.12/views/
But essentially you have to extend from flask.views.View and then manually attach your route to app instance, so it looks something like this:
from flask import Flask
from flask.views import View
app = Flask(__name__)
class Players_Data(View):
def dispatch_request(self):
return 'LOL'
app.add_url_rule('/', view_func=Players_Data.as_view('show_users'))
if __name__ == "__main__":
app.run(host='localhost')