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| author | Alexander_Kabui | 2024-05-16 16:34:34 +0300 |
|---|---|---|
| committer | Alexander_Kabui | 2024-05-16 16:34:34 +0300 |
| commit | f30300a82f605fa96130fbcbdcd17c53296d2372 (patch) | |
| tree | 46739a71836cc7b36cc2c61d84607fd453c3b08b /gn3 | |
| parent | 3913374700521647e93bf9afabb9943746ac5d5b (diff) | |
| download | genenetwork3-f30300a82f605fa96130fbcbdcd17c53296d2372.tar.gz | |
Minor code refactoring related
Diffstat (limited to 'gn3')
| -rw-r--r-- | gn3/llms/process.py | 23 |
1 files changed, 16 insertions, 7 deletions
diff --git a/gn3/llms/process.py b/gn3/llms/process.py index d080acb..11961eb 100644 --- a/gn3/llms/process.py +++ b/gn3/llms/process.py @@ -4,9 +4,9 @@ import os import string import json import logging +from urllib.parse import quote import requests -from urllib.parse import quote from gn3.llms.client import GeneNetworkQAClient @@ -106,15 +106,24 @@ def fetch_pubmed(references, file_name, data_dir=""): return references -def get_gnqa(query, auth_token, tmp_dir=""): - """entry function for the gn3 api endpoint()""" +def get_gnqa(query, auth_token, data_dir=""): + """entry function for the gn3 api endpoint() + ARGS: + query: what is a gene + auth_token: token to connect to api_client + data_dir: base datirectory for gn3 data + Returns: + task_id: fahamu unique identifier for task + answer + references: contains doc_name,reference,pub_med_info + """ - api_client = GeneNetworkQAClient(requests.Session(), api_key=auth_token) + api_client = GeneNetworkQAClient(requests.Session(), auth_token) res, task_id = api_client.ask('?ask=' + quote(query), auth_token) if task_id == 0: raise RuntimeError(f"Error connecting to Fahamu Api: {str(res)}") - res, success = api_client.get_answer(task_id) - if success == 1: + res, status = api_client.get_answer(task_id) + if status == 1: resp_text = filter_response_text(res.text) if resp_text.get("data") is None: return task_id, "Please try to rephrase your question to receive feedback", [] @@ -122,7 +131,7 @@ def get_gnqa(query, auth_token, tmp_dir=""): context = resp_text['data']['context'] references = parse_context( context, DocIDs().get_info, format_bibliography_info) - references = fetch_pubmed(references, "pubmed.json", tmp_dir) + references = fetch_pubmed(references, "pubmed.json", data_dir) return task_id, answer, references else: |