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from jira.client import JIRA
import pandas as pd
def get_details(project_id):
# Specify a server key. It should be your
# domain name link. yourdomainname.atlassian.net
jiraOptions = {'server': "https://srikanthnm.atlassian.net"}
# Get a JIRA client instance, pass,
# Authentication parameters
# and the Server name.
# emailID = your emailID
# token = token you receive after registration
jira = JIRA(options=jiraOptions, basic_auth=(
"srikanth.nm@gmail.com", "ATATT3xFfGF09rugcjiT06v8xMayt5ggayMNiwz4b6w07PWQxPvpi4fMDzwwHxKt-v8dGx49uiulIMKHUUYroeS8cXvMKYfi7sQnFsYNfGslPVqSq1BQrzPhTio-xmYOHcit5ijzU9cSGGa7eLXUMxQTsSQjLhtZ-EQPI8h6aki690_-evLFZmU=3910FFD4"))
# Search all issues mentioned against a project name.
lstKeys = []
lstSummary = []
lstReporter = []
for singleIssue in jira.search_issues(jql_str=f'project = {project_id}'):
lstKeys.append(singleIssue.key)
lstSummary.append(singleIssue.fields.summary)
lstReporter.append(singleIssue.fields.assignee.displayName)
df_output = pd.DataFrame()
df_output['Key'] = lstKeys
df_output['Summary'] = lstSummary
df_output['Assignee'] = lstReporter
df_output.to_csv('jira.csv', index=False)
return df_output
def ask_question(strQuery):
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import pandas as pd
tokenizer = AutoTokenizer.from_pretrained("Yale-LILY/reastap-large")
model = AutoModelForSeq2SeqLM.from_pretrained("Yale-LILY/reastap-large")
table = pd.read_csv("jira.csv")
query = strQuery
encoding = tokenizer(table=table, query=query, return_tensors="pt")
outputs = model.generate(**encoding)
return (tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
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