mirror of
https://github.com/believethehype/nostrdvm.git
synced 2025-06-12 07:00:51 +02:00
118 lines
4.4 KiB
Python
118 lines
4.4 KiB
Python
import io
|
|
import json
|
|
import os
|
|
import time
|
|
import zipfile
|
|
from pathlib import Path
|
|
|
|
import pandas as pd
|
|
import requests
|
|
import PIL.Image as Image
|
|
|
|
from utils.output_utils import upload_media_to_hoster
|
|
|
|
"""
|
|
This file contains basic calling functions for ML tasks that are outsourced to nova-server
|
|
(https://pypi.org/project/hcai-nova-server/). nova-server is an Open-Source backend that enables running models locally
|
|
based on preefined modules (nova-server-modules), by accepting a request form.
|
|
Modules are deployed in in separate virtual environments so dependencies won't conflict.
|
|
|
|
Setup nova-server:
|
|
https://hcmlab.github.io/nova-server/docbuild/html/tutorials/introduction.html
|
|
|
|
"""
|
|
|
|
"""
|
|
send_request_to_nova_server(request_form, address)
|
|
Function to send a request_form to the server, containing all the information we parsed from the Nostr event and added
|
|
in the module that is calling the server
|
|
|
|
"""
|
|
|
|
|
|
def send_request_to_nova_server(request_form, address):
|
|
print("Sending job to NOVA-Server")
|
|
url = ('http://' + address + '/process')
|
|
headers = {'Content-type': 'application/x-www-form-urlencoded'}
|
|
response = requests.post(url, headers=headers, data=request_form)
|
|
return response.text
|
|
|
|
|
|
def send_file_to_nova_server(filepath, address):
|
|
print("Sending file to NOVA-Server")
|
|
url = ('http://' + address + '/upload')
|
|
fp = open(filepath, 'rb')
|
|
response = requests.post(url, files={'file': fp})
|
|
result = response.content.decode('utf-8')
|
|
print(result)
|
|
return result
|
|
|
|
# headers = {'Content-type': 'application/x-www-form-urlencoded'}
|
|
|
|
|
|
"""
|
|
check_nova_server_status(request_form, address)
|
|
Function that requests the status of the current process with the jobID (we use the Nostr event as jobID).
|
|
When the Job is successfully finished we grab the result and depending on the type return the output
|
|
We throw an exception on error
|
|
"""
|
|
|
|
|
|
def check_nova_server_status(jobID, address) -> str | pd.DataFrame:
|
|
headers = {'Content-type': 'application/x-www-form-urlencoded'}
|
|
url_status = 'http://' + address + '/job_status'
|
|
url_log = 'http://' + address + '/log'
|
|
|
|
print("Sending Status Request to NOVA-Server")
|
|
data = {"jobID": jobID}
|
|
|
|
status = 0
|
|
length = 0
|
|
while status != 2 and status != 3:
|
|
response_status = requests.post(url_status, headers=headers, data=data)
|
|
response_log = requests.post(url_log, headers=headers, data=data)
|
|
status = int(json.loads(response_status.text)['status'])
|
|
log_content = str(json.loads(response_log.text)['message']).replace("ERROR", "").replace("INFO", "")
|
|
log = log_content[length:]
|
|
length = len(log_content)
|
|
if log != "":
|
|
print(log)
|
|
# WAITING = 0, RUNNING = 1, FINISHED = 2, ERROR = 3
|
|
time.sleep(1.0)
|
|
|
|
if status == 2:
|
|
try:
|
|
url_fetch = 'http://' + address + '/fetch_result'
|
|
print("Fetching Results from NOVA-Server...")
|
|
data = {"jobID": jobID, "delete_after_download": True}
|
|
response = requests.post(url_fetch, headers=headers, data=data)
|
|
content_type = response.headers['content-type']
|
|
print("Content-type: " + str(content_type))
|
|
if content_type == "image/jpeg":
|
|
image = Image.open(io.BytesIO(response.content))
|
|
image.save("./outputs/image.jpg")
|
|
result = upload_media_to_hoster("./outputs/image.jpg")
|
|
return result
|
|
os.remove("./outputs/image.jpg")
|
|
elif content_type == 'text/plain; charset=utf-8':
|
|
result = response.content.decode('utf-8')
|
|
return result
|
|
elif content_type == "application/x-zip-compressed":
|
|
zf = zipfile.ZipFile(io.BytesIO(response.content), "r")
|
|
|
|
for fileinfo in zf.infolist():
|
|
if fileinfo.filename.endswith(".annotation~"):
|
|
try:
|
|
anno_string = zf.read(fileinfo).decode('utf-8', errors='replace')
|
|
columns = ['from', 'to', 'name', 'conf']
|
|
result = pd.DataFrame([row.split(';') for row in anno_string.split('\n')],
|
|
columns=columns)
|
|
return result
|
|
except Exception as e:
|
|
print(e)
|
|
except Exception as e:
|
|
print("Couldn't fetch result: " + str(e))
|
|
|
|
elif status == 3:
|
|
return "error"
|