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| import json | |
| import argparse | |
| import requests | |
| import numpy as np | |
| from sentence_transformers import SentenceTransformer | |
| from .defaults import OWNER, REPO, TOKEN | |
| model_id = "all-mpnet-base-v2" | |
| model = SentenceTransformer(model_id) | |
| def load_embeddings(): | |
| """ | |
| Function to load embeddings from file | |
| """ | |
| embeddings = np.load("issue_embeddings.npy") | |
| return embeddings | |
| def load_issue_information(issue_type="issue"): | |
| """ | |
| Function to load issue information from file | |
| """ | |
| with open(f"embedding_index_to_{issue_type}.json", "r") as f: | |
| embedding_index_to_issue = json.load(f) | |
| with open("issues_dict.json", "r") as f: | |
| issues = json.load(f) | |
| return embedding_index_to_issue, issues | |
| 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 get_issue(issue_no, token=TOKEN, owner=OWNER, repo=REPO): | |
| """ | |
| Function to get issue from GitHub | |
| """ | |
| url = f"https://api.github.com/repos/{owner}/{repo}/issues/{issue_no}" | |
| headers = { | |
| "Accept": "application/vnd.github+json", | |
| "Authorization": f"{token}", | |
| "X-GitHub-Api-Version": "2022-11-28", | |
| "User-Agent": "amyeroberts", | |
| } | |
| request = requests.get(url, headers=headers) | |
| if request.status_code != 200: | |
| raise ValueError(f"Request failed with status code {request.status_code}") | |
| return request.json() | |
| def get_similar_issues(issue_no, query, top_k=5, token=TOKEN, owner=OWNER, repo=REPO, issue_type="issue"): | |
| """ | |
| Function to find similar issues | |
| """ | |
| if issue_no is not None and query is not None: | |
| raise ValueError("Only one of issue_no or query can be provided") | |
| if issue_no is not None and query is not None: | |
| raise ValueError("Only one of issue_no or query can be provided") | |
| if issue_no is not None: | |
| issue = get_issue(issue_no, token=token, owner=owner, repo=repo) | |
| query = issue["title"] + "\n" +issue["body"] | |
| query_embedding = model.encode(query) | |
| query_embedding = query_embedding.reshape(1, -1) | |
| embeddings = load_embeddings() | |
| # 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] | |
| embedding_index_to_issue, issues = load_issue_information(issue_type=issue_type) | |
| similar_issues = [] | |
| for i in most_similar_indices[:top_k]: | |
| issue_no = embedding_index_to_issue[str(i)] | |
| similar_issues.append(issues[issue_no]) | |
| return similar_issues | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("") | |
| parser.add_argument("--issue_no", type=int, default=None) | |
| parser.add_argument("--query", type=str, default=None) | |
| parser.add_argument("--top_k", type=int, default=5) | |
| parser.add_argument("--token", type=str, default=TOKEN) | |
| parser.add_argument("--owner", type=str, default=OWNER) | |
| parser.add_argument("--repo", type=str, default=REPO) | |
| args = parser.parse_args() | |
| get_similar_issues(**vars(args)) | |