import pprint
import json
import argparse
import requests
from defaults import OWNER, REPO, TOKEN
from sentence_transformers import SentenceTransformer
import numpy as np

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():
    """
    Function to load issue information from file
    """
    with open("embedding_index_to_issue.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_similar_issues(issue_no, top_k=5, token=TOKEN, owner=OWNER, repo=REPO):
    """
    Function to find similar issues
    """
    url = f"https://api.github.com/repos/{owner}/{repo}/issues"
    headers = {
        "Accept": "application/vnd.github+json",
        f"Authorization": "{token}",
        "X-GitHub-Api-Version": "2022-11-28",
        "User-Agent": "amyeroberts",
    }
    request = requests.get(
        f"https://api.github.com/repos/{OWNER}/{REPO}/issues/{issue_no}",
        headers=headers,
    )

    if request.status_code != 200:
        raise ValueError(f"Request failed with status code {request.status_code}")

    query_embedding = model.encode(request.json()["body"])
    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()

    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("issue_no", type=int)
    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(args.issue_no, args.top_k, args.token, args.owner, args.repo)