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()