Move to utils

app.py

@@ -1,17 +1,16 @@
 import datetime
-import gradio as gr
 import os
-from find_similar_issues import get_similar_issues
-import requests
-
-from defaults import OWNER, REPO
-
-import build_issue_dict
-import build_embeddings
 import shutil
-from fetch import get_issues
-from update_stored_issues import update_issues
 
+import gradio as gr
+import requests
+
+from utils import build_issue_dict
+from utils import build_embeddings
+from utils.defaults import OWNER, REPO
+from utils.fetch import get_issues
+from utils.find_similar_issues import get_similar_issues
+from utils.update_stored_issues import update_issues
 
 
 def get_query_issue_information(issue_no, token):

retrieval.py

@@ -1,80 +0,0 @@
-"""
-Module which contains functionality to retrieve the most similar issues for a given query
-"""
-
-
-import argparse
-import json
-
-import numpy as np
-from sentence_transformers import SentenceTransformer
-
-def cosine_similarity(a, b):
-    if a.ndim == 1:
-        a = a.reshape(1, -1)
-
-    if b.ndim == 1:
-        b = b.reshape(1, -1)
-
-    return np.dot(a, b.T) / (np.linalg.norm(a, axis=1) * np.linalg.norm(b, axis=1))
-
-
-def retrieve_issue_rankings(
-    query: str,
-    model_id: str,
-    input_embedding_filename: str,
-):
-    """
-    Given a query returns the list of issues sorted by similarity to the query
-    according to their embedding index
-    """
-    model = SentenceTransformer(model_id)
-
-    embeddings = np.load(input_embedding_filename)
-
-    query_embedding = model.encode(query)
-
-    # Calculate the cosine similarity between the query and all the issues
-    cosine_similarities = cosine_similarity(query_embedding, embeddings)
-
-    # Get the index of the most similar issue
-    most_similar_indices = np.argsort(cosine_similarities)
-    most_similar_indices = most_similar_indices[0][::-1]
-    return most_similar_indices
-
-
-def print_issue(issues, issue_id):
-    # Get the issue id of the most similar issue
-    issue_info = issues[issue_id]
-
-    print(f"#{issue_id}", issue_info["title"])
-    print(issue_info["body"])
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("query", type=str)
-    parser.add_argument("--model_id", type=str, default="all-mpnet-base-v2")
-    parser.add_argument("--input_embedding_filename", type=str, default="issue_embeddings.npy")
-    parser.add_argument("--input_index_filename", type=str, default="embedding_index_to_issue.json")
-
-    args = parser.parse_args()
-
-    issue_rankings = retrieve_issue_rankings(
-        query=args.query,
-        model_id=args.model_id,
-        input_embedding_filename=args.input_embedding_filename,
-    )
-
-    with open("issues_dict.json", "r") as f:
-        issues = json.load(f)
-
-    with open(args.input_index_filename, "r") as f:
-        embedding_index_to_issue = json.load(f)
-
-    issue_ids = [embedding_index_to_issue[str(i)] for i in issue_rankings]
-
-    for issue_id in issue_ids[:3]:
-        print(issue_id)
-        print_issue(issues, issue_id)
-        print("\n\n\n")

fetch.py → utils/fetch.py

@@ -22,7 +22,7 @@ import os
 import requests
 import numpy as np
 
-from defaults import OWNER, REPO, GITHUB_API_VERSION, TOKEN, ISSUE_JSON_FILE
+from .defaults import OWNER, REPO, GITHUB_API_VERSION, TOKEN, ISSUE_JSON_FILE
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)

update_stored_issues.py → utils/update_stored_issues.py

@@ -18,7 +18,7 @@ import os
 import numpy as np
 import requests
 
-from defaults import TOKEN, OWNER, REPO, GITHUB_API_VERSION, ISSUE_JSON_FILE
+from .defaults import TOKEN, OWNER, REPO, GITHUB_API_VERSION, ISSUE_JSON_FILE
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)