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"""this module contains code for processing response from fahamu client.py"""
# pylint: disable=C0301
import os
import string
import json
import logging
from urllib.parse import quote
from gn3.llms.client import GeneNetworkQAClient
BASE_URL = 'https://genenetwork.fahamuai.com/api/tasks'
BASEDIR = os.path.abspath(os.path.dirname(__file__))
class DocIDs():
""" Class Method to Parse document id and names from files"""
def __init__(self):
"""
init method for Docids
* doc_ids.json: opens doc)ids for gn references
* sugar_doc_ids: open doci_ids for diabetes references
"""
self.doc_ids = load_file("doc_ids.json", BASEDIR)
self.sugar_doc_ids = load_file("all_files.json", BASEDIR)
self.format_doc_ids(self.sugar_doc_ids)
def format_doc_ids(self, docs):
"""method to format doc_ids for list items doc_id and doc_name"""
for _key, val in docs.items():
if isinstance(val, list):
for doc_obj in val:
doc_name = doc_obj["filename"].removesuffix(".pdf").removesuffix(".txt").replace("_", "")
self.doc_ids.update({doc_obj["id"]: doc_name})
def get_info(self, doc_id):
""" interface to make read from doc_ids
and extract info data else returns
doc_id
Args:
doc_id: str: a search key for doc_ids
Returns:
an object with doc_info if doc_id in doc_ids
"""
if doc_id in self.doc_ids.keys():
return self.doc_ids[doc_id]
else:
return doc_id
def format_bibliography_info(bib_info):
"""Utility function for formatting bibliography info
"""
if isinstance(bib_info, str):
return bib_info.removesuffix('.txt')
elif isinstance(bib_info, dict):
return f"{bib_info['author']}.{bib_info['title']}.{bib_info['year']}.{bib_info['doi']} "
return bib_info
def parse_context(context, get_info_func, format_bib_func):
"""Function to parse doc_ids content
Args:
context: raw references from fahamu api
get_info_func: function to get doc_ids info
format_bib_func: function to foramt bibliography info
Returns:
an list with each item having (doc_id,bib_info,
combined reference text)
"""
results = []
for doc_ids, summary in context.items():
combo_txt = ""
for entry in summary:
combo_txt += "\t" + entry["text"]
doc_info = get_info_func(doc_ids)
bib_info = doc_ids if doc_ids == doc_info else format_bib_func(
doc_info)
results.append(
{"doc_id": doc_ids, "bibInfo": bib_info, "comboTxt": combo_txt})
return results
def load_file(filename, dir_path):
"""Utility function to read json file
Args:
filename: file name to read
dir_path: base directory for the file
Returns: json data read to a dict
"""
file_path = os.path.join(dir_path, f"{filename}")
if not os.path.isfile(file_path):
raise FileNotFoundError(f"{filename} was not found or is a directory")
with open(file_path, "rb") as file_handler:
return json.load(file_handler)
def fetch_pubmed(references, file_name, data_dir=""):
"""
Fetches PubMed data from a JSON file and populates the\
references dictionary.
Args:
references (dict): Dictionary with document IDs as keys\
and reference data as values.
filename (str): Name of the JSON file containing PubMed data.
data_dir (str): Base directory where the data files are located.
Returns:
dict: Updated references dictionary populated with the PubMed data.
"""
try:
pubmed = load_file(file_name, os.path.join(data_dir, "gn-meta/lit"))
for reference in references:
if pubmed.get(reference["doc_id"]):
reference["pubmed"] = pubmed.get(reference["doc_id"])
return references
except FileNotFoundError:
logging.error("failed to find pubmed_path for %s/%s",
data_dir, file_name)
return references
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(api_key=auth_token)
res, task_id = api_client.ask('?ask=' + quote(query), query=query)
res, _status = api_client.get_answer(task_id)
resp_text = json.loads(''.join([str(char)
for char in res.text if char in string.printable]))
answer = resp_text['data']['answer']
context = resp_text['data']['context']
return task_id, answer, fetch_pubmed(parse_context(
context, DocIDs().get_info,
format_bibliography_info),
"pubmed.json", data_dir)
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