app.py

import re
import json
import nltk
import joblib
import torch
import pandas as pd
import numpy as np
import streamlit as st
from pathlib import Path
from torch import nn
from docarray import DocList
from docarray.index import InMemoryExactNNIndex
from transformers import pipeline
from transformers import AutoTokenizer, AutoModel
from data.repo_doc import RepoDoc
from data.pair_classifier import PairClassifier
from nltk.stem import WordNetLemmatizer

nltk.download("wordnet")
KMEANS_MODEL_PATH = Path(__file__).parent.joinpath("data/kmeans_model_scibert.pkl")
SIMILARITY_CAL_MODEL_PATH = Path(__file__).parent.joinpath("data/SimilarityCal_model_NO1.pt")
device = (
    "cuda"
    if torch.cuda.is_available()
    else "mps"
    if torch.backends.mps.is_available()
    else "cpu"
)

# 1. Product environment
# INDEX_PATH = Path(__file__).parent.joinpath("data/index.bin")
# CLUSTER_PATH = Path(__file__).parent.joinpath("data/repo_clusters.json")
SCIBERT_MODEL_PATH = "allenai/scibert_scivocab_uncased"


# 2. Developing environment
INDEX_PATH = Path(__file__).parent.joinpath("data/index_test.bin")
CLUSTER_PATH = Path(__file__).parent.joinpath("data/repo_clusters_test.json")
# SCIBERT_MODEL_PATH = Path(__file__).parent.joinpath("data/scibert_scivocab_uncased")  # Download locally


@st.cache_resource(show_spinner="Loading repositories basic information...")
def load_index():
    """
    The function to load the index file and return a RepoDoc object with default value
    :return: index and a RepoDoc object with default value
    """
    default_doc = RepoDoc(
        name="",
        topics=[],
        stars=0,
        license="",
        code_embedding=None,
        doc_embedding=None,
        readme_embedding=None,
        requirement_embedding=None,
        repository_embedding=None
    )

    return InMemoryExactNNIndex[RepoDoc](index_file_path=INDEX_PATH), default_doc


@st.cache_resource(show_spinner="Loading repositories clusters...")
def load_repo_clusters():
    """
    The function to load the repo-clusters file
    :return: a dictionary with the repo-clusters
    """
    with open(CLUSTER_PATH, "r") as file:
        repo_clusters = json.load(file)

    return repo_clusters


@st.cache_resource(show_spinner="Loading RepoSim4Py pipeline model...")
def load_pipeline_model():
    """
    The function to load RepoSim4Py pipeline model
    :return: a HuggingFace pipeline
    """
    # Option 1 --- Download model by HuggingFace username/model_name
    model_path = "Henry65/RepoSim4Py"

    # Option 2 --- Download model locally
    # model_path = Path(__file__).parent.joinpath("data/RepoSim4Py")

    return pipeline(
        model=model_path,
        trust_remote_code=True,
        device_map="auto"
    )


@st.cache_resource(show_spinner="Loading SciBERT model...")
def load_scibert_model():
    """
    The function to load SciBERT model
    :return: tokenizer and model
    """
    tokenizer = AutoTokenizer.from_pretrained(SCIBERT_MODEL_PATH)
    scibert_model = AutoModel.from_pretrained(SCIBERT_MODEL_PATH).to(device)
    return tokenizer, scibert_model


@st.cache_resource(show_spinner="Loading KMeans model...")
def load_kmeans_model():
    """
    The function to load KMeans model
    :return: a KMeans model
    """
    return joblib.load(KMEANS_MODEL_PATH)


@st.cache_resource(show_spinner="Loading SimilarityCal model...")
def load_similaritycal_model():
    sim_cal_model = PairClassifier()
    sim_cal_model.load_state_dict(torch.load(SIMILARITY_CAL_MODEL_PATH, map_location=device))
    sim_cal_model = sim_cal_model.to(device)
    sim_cal_model = sim_cal_model.eval()
    return sim_cal_model


def generate_scibert_embedding(tokenizer, scibert_model, text):
    """
    The function for generating SciBERT embeddings based on topic text
    :param text: the topic text
    :return: topic embeddings
    """
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
    outputs = scibert_model(**inputs)
    # Use mean pooling for sentence representation
    embeddings = outputs.last_hidden_state.mean(dim=1).cpu().detach().numpy()
    return embeddings


@st.cache_data(show_spinner=False)
def run_pipeline_model(_model, repo_name, github_token):
    """
    The function to generate repo_info by using pipeline model
    :param _model: pipeline
    :param repo_name: the name of repository
    :param github_token: GitHub token
    :return: the information generated by the pipeline
    """
    with st.spinner(
            f"Downloading and extracting the {repo_name}, this may take a while..."
    ):
        extracted_infos = _model.preprocess(repo_name, github_token=github_token)

    if not extracted_infos:
        return None

    with st.spinner(f"Generating embeddings for {repo_name}..."):
        repo_info = _model.forward(extracted_infos)[0]

    return repo_info


def run_index_search(index, query, search_field, limit):
    """
    The function to search at index file based on query and limit
    :param index: the index
    :param query: query
    :param search_field: which field to search for
    :param limit: page limit
    :return: a dataframe with search results
    """
    top_matches, scores = index.find(
        query=query, search_field=search_field, limit=limit
    )

    search_results = top_matches.to_dataframe()
    search_results["scores"] = scores

    return search_results


def run_cluster_search(repo_clusters, repo_name_list):
    """
    The function to search cluster number for such repositories.
    :param repo_clusters: dictionary with repo-clusters
    :param repo_name_list: list or array represent repository names
    :return: cluster number list
    """
    clusters = []
    for repo_name in repo_name_list:
        clusters.append(repo_clusters[repo_name])
    return clusters


def run_similaritycal_search(index, repo_clusters, model, query_doc, query_cluster_number, limit, same_cluster=True):
    """
    The function to run SimilarityCal model.
    :param index: index file
    :param repo_clusters: repo-clusters json file
    :param model: SimilarityCal model
    :param query_doc: query repo doc
    :param query_cluster_number: query repo cluster number
    :param limit: limit
    :param same_cluster: whether searching for same cluster
    :return: result dataframe
    """
    docs = index._docs
    input_embeddings_list = []
    result_dl = DocList[RepoDoc]()
    for doc in docs:
        if same_cluster and query_cluster_number != repo_clusters[doc.name]:
            continue
        if doc.name != query_doc.name:
            e1, e2 = (torch.Tensor(query_doc.repository_embedding),
                      torch.Tensor(doc.repository_embedding))
            input_embeddings = torch.cat([e1, e2])
            input_embeddings_list.append(input_embeddings)
            result_dl.append(doc)

    input_embeddings_list = torch.stack(input_embeddings_list).to(device)
    softmax = nn.Softmax(dim=1).to(device)
    model_output = model(input_embeddings_list)
    similarity_scores = softmax(model_output)[:, 1].cpu().detach().numpy()
    df = result_dl.to_dataframe()
    df["scores"] = similarity_scores
    return df.sort_values(by='scores', ascending=False).reset_index(drop=True).head(limit)


if __name__ == "__main__":
    # Loading dataset and models
    index, default_doc = load_index()
    repo_clusters = load_repo_clusters()
    pipeline_model = load_pipeline_model()
    lemmatizer = WordNetLemmatizer()
    tokenizer, scibert_model = load_scibert_model()
    kmeans = load_kmeans_model()
    sim_cal_model = load_similaritycal_model()

    # Setting the sidebar
    with st.sidebar:
        st.text_input(
            label="GitHub Token",
            key="github_token",
            type="password",
            placeholder="Paste your GitHub token here",
            help="Consider setting GitHub token to avoid hitting rate limits: https://docs.github.com/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token",
        )

        st.slider(
            label="Search results limit",
            min_value=1,
            max_value=100,
            value=10,
            step=1,
            key="search_results_limit",
            help="Limit the number of search results",
        )

        st.multiselect(
            label="Display columns",
            options=["scores", "name", "topics", "cluster number", "stars", "license"],
            default=["scores", "name", "topics", "cluster number", "stars", "license"],
            help="Select columns to display in the search results",
            key="display_columns",
        )

    # Setting the main content
    st.title("RepoSnipy")

    st.text_input(
        "Enter a GitHub repository URL or owner/repository (case-sensitive):",
        value="",
        max_chars=200,
        placeholder="numpy/numpy",
        key="repo_input",
    )

    st.checkbox(
        label="Add/Update this repository to the index",
        value=False,
        key="update_index",
        help="Encode the latest version of this repository and add/update it to the index",
    )

    # Setting the search button
    search = st.button("Search")
    # The regular expression for repository
    repo_regex = r"^((git@|http(s)?://)?(github\.com)(/|:))?(?P<owner>[\w.-]+)(/)(?P<repo>[\w.-]+?)(\.git)?(/)?$"

    if search:
        match_res = re.match(repo_regex, st.session_state.repo_input)
        # 1. Repository can be matched
        if match_res is not None:
            repo_name = f"{match_res.group('owner')}/{match_res.group('repo')}"
            records = index.filter({"name": {"$eq": repo_name}})
            # 1) Building the query information
            query_doc = default_doc.copy() if not records else records[0]
            # 2) Recording the cluster number
            cluster_number = -1 if not records else repo_clusters[repo_name]

            # Importance 1 ---- situation need to update repository information and cluster number
            if st.session_state.update_index or not records:
                # 1) Updating repository information by using RepoSim4Py pipeline
                repo_info = run_pipeline_model(pipeline_model, repo_name, st.session_state.github_token)
                if repo_info is None:
                    st.error("Repository not found or invalid GitHub token!")
                    st.stop()

                query_doc.name = repo_info["name"]
                query_doc.topics = repo_info["topics"]
                query_doc.stars = repo_info["stars"]
                query_doc.license = repo_info["license"]
                query_doc.code_embedding = None if np.all(repo_info["mean_code_embedding"] == 0) else repo_info[
                    "mean_code_embedding"].reshape(-1)
                query_doc.doc_embedding = None if np.all(repo_info["mean_doc_embedding"] == 0) else repo_info[
                    "mean_doc_embedding"].reshape(-1)
                query_doc.readme_embedding = None if np.all(repo_info["mean_readme_embedding"] == 0) else repo_info[
                    "mean_readme_embedding"].reshape(-1)
                query_doc.requirement_embedding = None if np.all(repo_info["mean_requirement_embedding"] == 0) else \
                    repo_info["mean_requirement_embedding"].reshape(-1)
                query_doc.repository_embedding = None if np.all(repo_info["mean_repo_embedding"] == 0) else repo_info[
                    "mean_repo_embedding"].reshape(-1)

                # 2) Updating cluster number
                topics_text = ' '.join(
                    [lemmatizer.lemmatize(topic.lower().replace('-', ' ')) for topic in query_doc.topics])
                topic_embeddings = generate_scibert_embedding(tokenizer, scibert_model, topics_text)
                cluster_number = int(kmeans.predict(topic_embeddings)[0])

            # Importance 2 ---- update index file and repository clusters file
            if st.session_state.update_index:
                if not query_doc.license:
                    st.warning(
                        "License is missing in this repository and will not be persisted!"
                    )
                elif (query_doc.code_embedding is None) and (query_doc.doc_embedding is None) and (
                        query_doc.requirement_embedding is None) and (query_doc.readme_embedding is None) and (
                        query_doc.repository_embedding is None):
                    st.warning(
                        "This repository has no such useful information (code, docstring, readme and requirement) extracted and will not be persisted!"
                    )
                else:
                    index.index(query_doc)
                    repo_clusters[query_doc.name] = cluster_number

                    with st.spinner("Persisting the index and repository clusters..."):
                        index.persist(str(INDEX_PATH))
                        with open(CLUSTER_PATH, "w") as file:
                            json.dump(repo_clusters, file, indent=4)
                        st.success("Repository updated to the index!")

                    load_index.clear()
                    load_repo_clusters.clear()

            st.session_state["query_doc"] = query_doc
            st.session_state["cluster_number"] = cluster_number

        # 2. Repository cannot be matched
        else:
            st.error("Invalid input!")

    # Starting to query
    if "query_doc" in st.session_state:
        query_doc = st.session_state.query_doc
        cluster_number = st.session_state.cluster_number
        limit = st.session_state.search_results_limit

        # Showing the query repository information
        st.dataframe(
            pd.DataFrame(
                [
                    {
                        "name": query_doc.name,
                        "topics": query_doc.topics,
                        "cluster number": cluster_number,
                        "stars": query_doc.stars,
                        "license": query_doc.license,
                    }
                ],
            )
        )

        display_columns = st.session_state.display_columns
        code_sim_tab, doc_sim_tab, readme_sim_tab, requirement_sim_tab, repo_sim_tab, same_cluster_tab, diff_cluster_tab = st.tabs(
            ["Code_sim", "Docstring_sim", "Readme_sim", "Requirement_sim",
             "Repository_sim", "Same_cluster", "Different_cluster"])

        if query_doc.code_embedding is not None:
            code_sim_res = run_index_search(index, query_doc, "code_embedding", limit)
            cluster_numbers = run_cluster_search(repo_clusters, code_sim_res["name"])
            code_sim_res["cluster number"] = cluster_numbers
            code_sim_tab.dataframe(code_sim_res[display_columns])
        else:
            code_sim_tab.error("No function code was extracted for this repository!")

        if query_doc.doc_embedding is not None:
            doc_sim_res = run_index_search(index, query_doc, "doc_embedding", limit)
            cluster_numbers = run_cluster_search(repo_clusters, doc_sim_res["name"])
            doc_sim_res["cluster number"] = cluster_numbers
            doc_sim_tab.dataframe(doc_sim_res[display_columns])
        else:
            doc_sim_tab.error("No function docstring was extracted for this repository!")

        if query_doc.readme_embedding is not None:
            readme_sim_res = run_index_search(index, query_doc, "readme_embedding", limit)
            cluster_numbers = run_cluster_search(repo_clusters, readme_sim_res["name"])
            readme_sim_res["cluster number"] = cluster_numbers
            readme_sim_tab.dataframe(readme_sim_res[display_columns])
        else:
            readme_sim_tab.error("No readme file was extracted for this repository!")

        if query_doc.requirement_embedding is not None:
            requirement_sim_res = run_index_search(index, query_doc, "requirement_embedding", limit)
            cluster_numbers = run_cluster_search(repo_clusters, requirement_sim_res["name"])
            requirement_sim_res["cluster number"] = cluster_numbers
            requirement_sim_tab.dataframe(requirement_sim_res[display_columns])
        else:
            requirement_sim_tab.error("No requirement file was extracted for this repository!")

        if query_doc.repository_embedding is not None:
            repo_sim_res = run_index_search(index, query_doc, "repository_embedding", limit)
            cluster_numbers = run_cluster_search(repo_clusters, repo_sim_res["name"])
            repo_sim_res["cluster number"] = cluster_numbers
            repo_sim_tab.dataframe(repo_sim_res[display_columns])
        else:
            repo_sim_tab.error("No such useful information was extracted for this repository!")

        if cluster_number is not None and query_doc.repository_embedding is not None:
            same_cluster_df = run_similaritycal_search(index, repo_clusters, sim_cal_model,
                                                       query_doc, cluster_number, limit,
                                                       same_cluster=True)
            diff_cluster_df = run_similaritycal_search(index, repo_clusters, sim_cal_model,
                                                       query_doc, cluster_number, limit,
                                                       same_cluster=False)
            same_cluster_numbers = run_cluster_search(repo_clusters, same_cluster_df["name"])
            same_cluster_df["cluster number"] = same_cluster_numbers

            diff_cluster_numbers = run_cluster_search(repo_clusters, diff_cluster_df["name"])
            diff_cluster_df["cluster number"] = diff_cluster_numbers

            same_cluster_tab.dataframe(same_cluster_df[display_columns])
            diff_cluster_tab.dataframe(diff_cluster_df[display_columns])

        else:
            same_cluster_tab.error("No such useful information was extracted for this repository!")
            diff_cluster_tab.error("No such useful information was extracted for this repository!")