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updated PyPi version

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root 2021-08-17 20:42:07 +00:00
parent 4e1b0a6158
commit fc3e151fdd
12 changed files with 61 additions and 32 deletions
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1
.gitignore vendored
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@ -32,3 +32,4 @@ build/
gradio/launches.json
gradio/frontend/static
workspace.code-workspace
*.db

4
demo/calculator.py
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@ -4,7 +4,6 @@ import time
def calculator(num1, operation, num2):
print(num1, operation, num2)
time.sleep(10)
if operation == "add":
return num1 + num2
elif operation == "subtract":
@ -26,8 +25,7 @@ iface = gr.Interface(calculator,
title="test calculator",
description="heres a sample toy calculator. enjoy!",
flagging_options=["this", "or", "that"],
enable_queue=True
)
if __name__ == "__main__":
iface.launch(share=True)
iface.launch()

2
demo/gender_sentence_custom_interpretation.py
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@ -24,6 +24,6 @@ def interpret_gender(sentence):
iface = gr.Interface(
fn=gender_of_sentence, inputs=gr.inputs.Textbox(default="She went to his house to get her keys."),
outputs="label", interpretation=interpret_gender)
outputs="label", interpretation=interpret_gender, enable_queue=True)
if __name__ == "__main__":
iface.launch()

BIN
demo/gradio_queue.db
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2
frontend/src/gradio.jsx
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@ -165,7 +165,7 @@ export class GradioInterface extends React.Component {
}
this.setState({ submitting: true, has_changed: false, error: false });
this.props
.fn(input_state, "interpret")
.fn(input_state, "interpret", this.queueCallback)
.then((output) => {
this.setState({
interpretation: output["interpretation_scores"],

4
frontend/src/index.jsx
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@ -22,9 +22,9 @@ let postData = async (url, body) => {
}
let fn = async (queue, data, action, queue_callback) => {
if (queue && action == "predict") {
if (queue && ["predict", "interpret"].includes(action)) {
const output = await postData(
"api/queue/push/", { data: data },
"api/queue/push/", { data: data, action:action },
);
let hash = await output.text();
let status = "UNKNOWN";

2
gradio.egg-info/PKG-INFO
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@ -1,6 +1,6 @@
Metadata-Version: 1.0
Name: gradio
Version: 2.2.11
Version: 2.2.12
Summary: Python library for easily interacting with trained machine learning models
Home-page: https://github.com/gradio-app/gradio-UI
Author: Abubakar Abid

3
gradio/interface.py
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@ -10,6 +10,7 @@ from gradio import networking, strings, utils
from gradio.interpretation import quantify_difference_in_label, get_regression_or_classification_value
from gradio.external import load_interface
from gradio import encryptor
from gradio import queue
import requests
import random
import time
@ -462,6 +463,8 @@ class Interface:
print("Keyboard interruption in main thread... closing server.")
thread.keep_running = False
networking.url_ok(path_to_local_server) # Hit the server one more time to close it
if self.enable_queue:
queue.close()
def test_launch(self):
for predict_fn in self.predict:

30
gradio/networking.py
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@ -396,8 +396,9 @@ def file(path):
@app.route("/api/queue/push/", methods=["POST"])
@login_check
def queue_push():
data = request.json
job_hash = queue.push(data)
data = request.json["data"]
action = request.json["action"]
job_hash = queue.push({"data": data}, action)
return job_hash
@app.route("/api/queue/status/", methods=["POST"])
@ -409,17 +410,22 @@ def queue_status():
def queue_thread(path_to_local_server):
while True:
next_job = queue.pop()
if next_job is not None:
_, hash, input_data = next_job
queue.start_job(hash)
response = requests.post(path_to_local_server + "/api/predict/", json=input_data)
if response.status_code == 200:
queue.pass_job(hash, response.json())
try:
next_job = queue.pop()
if next_job is not None:
_, hash, input_data, task_type = next_job
queue.start_job(hash)
response = requests.post(
path_to_local_server + "/api/" + task_type + "/", json=input_data)
if response.status_code == 200:
queue.pass_job(hash, response.json())
else:
queue.fail_job(hash, response.text)
else:
queue.fail_job(hash, response.text)
else:
time.sleep(1)
except Exception as e:
time.sleep(1)
pass
def start_server(interface, server_name, server_port=None, auth=None, ssl=None):
if server_port is None:
@ -440,6 +446,8 @@ def start_server(interface, server_name, server_port=None, auth=None, ssl=None):
log = logging.getLogger('werkzeug')
log.setLevel(logging.ERROR)
if app.interface.enable_queue:
if auth is not None or app.interface.encrypt:
raise ValueError("Cannot queue with encryption or authenitcation enabled.")
queue.init()
app.queue_thread = threading.Thread(target=queue_thread, args=(path_to_local_server,))
app.queue_thread.start()

41
gradio/queue.py
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@ -5,6 +5,20 @@ import json
DB_FILE = "gradio_queue.db"
def generate_hash():
generate = True
while generate:
hash = uuid.uuid4().hex
conn = sqlite3.connect(DB_FILE)
c = conn.cursor()
c.execute("""
SELECT hash FROM queue
WHERE hash = ?;
""", (hash,))
conn.commit()
generate = c.fetchone() is not None
return hash
def init():
if os.path.exists(DB_FILE):
os.remove(DB_FILE)
@ -15,6 +29,7 @@ def init():
queue_index integer PRIMARY KEY,
hash text,
input_data text,
action text,
popped integer DEFAULT 0
);""")
c.execute("""
@ -27,12 +42,16 @@ def init():
""")
conn.commit()
def close():
if os.path.exists(DB_FILE):
os.remove(DB_FILE)
def pop():
conn = sqlite3.connect(DB_FILE)
c = conn.cursor()
c.execute("BEGIN EXCLUSIVE")
c.execute("""
SELECT queue_index, hash, input_data FROM queue
SELECT queue_index, hash, input_data, action FROM queue
WHERE popped = 0 ORDER BY queue_index ASC LIMIT 1;
""")
result = c.fetchone()
@ -44,19 +63,19 @@ def pop():
UPDATE queue SET popped = 1, input_data = '' WHERE queue_index = ?;
""", (queue_index,))
conn.commit()
return result[0], result[1], json.loads(result[2])
return result[0], result[1], json.loads(result[2]), result[3]
def push(input_data):
def push(input_data, action):
input_data = json.dumps(input_data)
hash = uuid.uuid4().hex
hash = generate_hash()
conn = sqlite3.connect(DB_FILE)
c = conn.cursor()
c.execute("BEGIN EXCLUSIVE")
c.execute("""
INSERT INTO queue (hash, input_data)
VALUES (?, ?);
""", (hash, input_data))
INSERT INTO queue (hash, input_data, action)
VALUES (?, ?, ?);
""", (hash, input_data, action))
conn.commit()
return hash
@ -65,10 +84,10 @@ def get_status(hash):
c = conn.cursor()
c.execute("BEGIN EXCLUSIVE")
c.execute("""
SELECT queue_index FROM queue WHERE hash = ?;
SELECT queue_index, popped FROM queue WHERE hash = ?;
""", (hash,))
result = c.fetchone()
if result is None: # not in queue
if result[1] == 1: # in jobs
c.execute("""
SELECT status, output_data, error_message FROM jobs WHERE hash = ?;
""", (hash,))
@ -94,7 +113,7 @@ def get_status(hash):
else:
queue_index = result[0]
c.execute("""
SELECT COUNT(*) FROM queue WHERE queue_index < ?;
SELECT COUNT(*) FROM queue WHERE queue_index < ? and popped = 0;
""", (queue_index,))
result = c.fetchone()
conn.commit()
@ -105,7 +124,7 @@ def start_job(hash):
c = conn.cursor()
c.execute("BEGIN EXCLUSIVE")
c.execute("""
DELETE FROM queue WHERE hash = ?;
UPDATE queue SET popped = 1 WHERE hash = ?;
""", (hash,))
c.execute("""
INSERT INTO jobs (hash, status) VALUES (?, 'PENDING');

2
gradio/version.txt
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@ -1 +1 @@
2.2.11
2.2.12

2
setup.py
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@ -5,7 +5,7 @@ except ImportError:
setup(
name='gradio',
version='2.2.11',
version='2.2.12',
include_package_data=True,
description='Python library for easily interacting with trained machine learning models',
author='Abubakar Abid',