remove unused files, add faiss-cpu

code_sim_index/index.faiss

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:acdf704e8ab7d216c5ab0582681c7dc13e2ab349faeb45b43d817b2b95effe3b
-size 3462189

code_sim_index/index.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:88ab0cf9c92a95f849f9b7a2c896949219a2b776dc26c4c1f87668a2d6d6e0c9
-size 1205614

codesearchdb.pickle

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:af8c505cc7289eda3880bb53050c55d0b6c901e34b61a936760b17b9eb5ce934
-size 504758467

github.py

@@ -1,67 +0,0 @@
-import os
-from dotenv import load_dotenv
-from langchain.document_loaders import GithubFileLoader
-# from langchain.embeddings import HuggingFaceEmbeddings
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain_text_splitters import CharacterTextSplitter
-
-load_dotenv()
-
-#get the GITHUB_ACCESS_TOKEN from the .env file
-GITHUB_ACCESS_TOKEN = os.getenv("GITHUB_ACCESS_TOKEN")
-USER = "heaversm"
-REPO = "gdrive-docker"
-GITHUB_BASE_URL = "https://github.com/"
-
-
-def get_similar_files(query, db, embeddings):
-  # embedding_vector = embeddings.embed_query(query)
-  # docs_and_scores = db.similarity_search_by_vector(embedding_vector, k = 10)
-  docs_and_scores = db.similarity_search_with_score(query)
-  return docs_and_scores
-
-def get_hugging_face_model():
-  model_name = "mchochlov/codebert-base-cd-ft"
-  hf = HuggingFaceEmbeddings(model_name=model_name)
-  return hf
-
-loader = GithubFileLoader(
-    #repo is USER/REPO
-    repo=f"{USER}/{REPO}",
-    access_token=GITHUB_ACCESS_TOKEN,
-    github_api_url="https://api.github.com",
-    file_filter=lambda file_path: file_path.endswith(
-        (".py", ".ts")
-    ),  # load all python and typescript files
-)
-documents = loader.load()
-text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-docs = text_splitter.split_documents(documents)
-embedding_vector = get_hugging_face_model()
-db = FAISS.from_documents(docs, embedding_vector)
-model_name = "mchochlov/codebert-base-cd-ft"
-
-query = """
-def create_app():
-    app = connexion.FlaskApp(__name__, specification_dir="../.openapi")
-    app.add_api(
-        API_VERSION, resolver=connexion.resolver.RelativeResolver("provider.app")
-    )
-"""
-results_with_scores = get_similar_files(query, db, embedding_vector)
-print ("retrieved!!!")
-print(f"Number of results: {len(results_with_scores)}")
-
-# score is a distance score, the lower the better
-for doc, score in results_with_scores:
-    print(f"Metadata: {doc.metadata}, Score: {score}")
-
-top_file_path = results_with_scores[0][0].metadata['path']
-top_file_content = results_with_scores[0][0].page_content
-top_file_score = results_with_scores[0][1]
-top_file_link = f"{GITHUB_BASE_URL}{USER}/{REPO}/blob/main/{top_file_path}"
-
-print(f"Top file link: {top_file_link}")
-
-

github_st.py

@@ -1,88 +0,0 @@
-import streamlit as st
-import os
-from dotenv import load_dotenv
-from langchain.document_loaders import GithubFileLoader
-# from langchain.embeddings import HuggingFaceEmbeddings
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain_text_splitters import CharacterTextSplitter
-
-load_dotenv()
-
-#get the GITHUB_ACCESS_TOKEN from the .env file
-GITHUB_ACCESS_TOKEN = os.getenv("GITHUB_ACCESS_TOKEN")
-GITHUB_BASE_URL = "https://github.com/"
-
-
-@st.cache_resource
-def get_hugging_face_model():
-  model_name = "mchochlov/codebert-base-cd-ft"
-  hf = HuggingFaceEmbeddings(model_name=model_name)
-  return hf
-
-def get_similar_files(query, db, embeddings):
-  # embedding_vector = embeddings.embed_query(query)
-  # docs_and_scores = db.similarity_search_by_vector(embedding_vector, k = 10)
-  docs_and_scores = db.similarity_search_with_score(query)
-  return docs_and_scores
-
-
-
-
-st.title("Find Similar Code")
-
-#streamlit text input for USER
-USER = st.text_input("Enter the Github User", value = "heaversm")
-#streamlit text input for REPO
-REPO = st.text_input("Enter the Github Repository", value = "gdrive-docker")
-
-#streamlit file type selector
-FILE_TYPES_TO_LOAD = st.multiselect("Select File Types", [".py", ".ts",".js",".css",".html"], default = [".py"])
-
-
-text_input = st.text_area("Enter a Code Example", value =
-"""
-def create_app():
-    app = connexion.FlaskApp(__name__, specification_dir="../.openapi")
-    app.add_api(
-        API_VERSION, resolver=connexion.resolver.RelativeResolver("provider.app")
-    )
-""", height = 330
-)
-
-button = st.button("Find Similar Code")
-
-
-if button:
-  loader = GithubFileLoader(
-    #repo is USER/REPO
-    repo=f"{USER}/{REPO}",
-    access_token=GITHUB_ACCESS_TOKEN,
-    github_api_url="https://api.github.com",
-    file_filter=lambda file_path: file_path.endswith(
-      tuple(FILE_TYPES_TO_LOAD)
-    )
-  )
-  documents = loader.load()
-  text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-  docs = text_splitter.split_documents(documents)
-  embedding_vector = get_hugging_face_model()
-  db = FAISS.from_documents(docs, embedding_vector)
-  query = text_input
-  results_with_scores = get_similar_files(query, db, embedding_vector)
-  for doc, score in results_with_scores:
-    print(f"Metadata: {doc.metadata}, Score: {score}")
-
-  top_file_path = results_with_scores[0][0].metadata['path']
-  top_file_content = results_with_scores[0][0].page_content
-  top_file_score = results_with_scores[0][1]
-  top_file_link = f"{GITHUB_BASE_URL}{USER}/{REPO}/blob/main/{top_file_path}"
-  # write a clickable link in streamlit
-  st.markdown(f"[Top file link]({top_file_link})")
-
-
-else:
-  st.info("Please Submit a Code Sample")
-
-
-

requirements.txt

@@ -5,4 +5,5 @@ langchain-community
 langchain_huggingface
 langchain_text_splitters
 sentence-transformers
+faiss-cpu
 altair==4.0

search-pickle.py

@@ -1,99 +0,0 @@
-import streamlit as st
-from bs4 import BeautifulSoup
-from langchain.embeddings import HuggingFaceEmbeddings
-import pickle
-import torch
-import io
-from langchain.vectorstores import FAISS
-import json
-
-class CPU_Unpickler(pickle.Unpickler):
-    def find_class(self, module, name):
-        if module == 'torch.storage' and name == '_load_from_bytes':
-            return lambda b: torch.load(io.BytesIO(b), map_location='cpu')
-        else: return super().find_class(module, name)
-
-
-@st.cache_resource
-def get_hugging_face_model():
-  model_name = "mchochlov/codebert-base-cd-ft"
-  hf = HuggingFaceEmbeddings(model_name=model_name)
-  return hf
-
-
-@st.cache_resource
-def get_db():
-  with open("codesearchdb.pickle", "rb") as f:
-    db = CPU_Unpickler(f).load()
-  print("Loaded db")
-  # save_as_json(db, "codesearchdb.json")  # Save as JSON
-  return db
-
-def save_as_json(data, filename):
-    # Convert the data to a JSON serializable format
-    serializable_data = data_to_serializable(data)
-    with open(filename, "w") as json_file:
-        json.dump(serializable_data, json_file)
-
-def data_to_serializable(data):
-    if isinstance(data, dict):
-        return {k: data_to_serializable(v) for k, v in data.items() if not callable(v) and not isinstance(v, type)}
-    elif isinstance(data, list):
-        return [data_to_serializable(item) for item in data]
-    elif isinstance(data, (str, int, float, bool)) or data is None:
-        return data
-    elif hasattr(data, '__dict__'):
-        return data_to_serializable(data.__dict__)
-    elif hasattr(data, '__slots__'):
-        return {slot: data_to_serializable(getattr(data, slot)) for slot in data.__slots__}
-    else:
-        return str(data)  # Convert any other types to string
-
-def get_similar_links(query, db, embeddings):
-  embedding_vector = embeddings.embed_query(query)
-  docs_and_scores = db.similarity_search_by_vector(embedding_vector, k = 10)
-  hrefs = []
-  for docs in docs_and_scores:
-    html_doc = docs.page_content
-    soup = BeautifulSoup(html_doc, 'html.parser')
-    href = [a['href'] for a in soup.find_all('a', href=True)]
-    hrefs.append(href)
-  links = []
-  for href_list in hrefs:
-      for link in href_list:
-        links.append(link)
-  return links
-
-
-embedding_vector = get_hugging_face_model()
-db = FAISS.load_local("code_sim_index", embedding_vector, allow_dangerous_deserialization=True)
-save_as_json(db, "code_sim_index.json")  # Save as JSON
-
-st.title("Find Similar Code")
-text_input = st.text_area("Enter a Code Example", value =
-"""
-class Solution:
-    def subsets(self, nums: List[int]) -> List[List[int]]:
-        outputs = []
-        def backtrack(k, index, subSet):
-            if index == k:
-                outputs.append(subSet[:])
-                return
-            for i in range(index, len(nums)):
-                backtrack(k, i + 1, subSet + [nums[i]])
-        for j in range(len(nums) + 1):
-            backtrack(j, 0, [])
-        return outputs
-""", height = 330
-)
-button = st.button("Find Similar Questions")
-if button:
-  query = text_input
-  answer = get_similar_links(query, db, embedding_vector)
-  for link in set(answer):
-      st.write(link)
-
-else:
-  st.info("Please Input Valid Text")
-
-# get_db()

unpickle.py

@@ -1,19 +0,0 @@
-import pickle
-
-# Define the path to the pickle file
-pickle_file_path = 'codesearchdb.pickle'
-
-# Load the pickle file
-with open(pickle_file_path, 'rb') as file:
-    data = pickle.load(file)
-
-
-
-# Save the contents to a new file (for example, a JSON file)
-import json
-
-json_file_path = 'codesearchdb.json'
-with open(json_file_path, 'w') as json_file:
-    json.dump(data, json_file, indent=4)
-
-print(f"Contents have been saved to {json_file_path}")