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import gradio as gr
from bs4 import BeautifulSoup
from langchain.embeddings import HuggingFaceEmbeddings
import pickle
import torch
import io
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)
def get_hugging_face_model():
model_name = "mchochlov/codebert-base-cd-ft"
hf = HuggingFaceEmbeddings(model_name=model_name)
return hf
def get_db():
with open("codesearchdb.pickle", "rb") as f:
db = CPU_Unpickler(f).load()
return db
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
def find_similar_questions(text_input):
embedding_vector = get_hugging_face_model()
db = get_db()
query = text_input
answer = get_similar_links(query, db, embedding_vector)
return "\n".join(set(answer))
s_example = """
class Solution(object):
def isValid(self, s):
stack = []
mapping = {")": "(", "}": "{", "]": "["}
for char in s:
if char in mapping:
top_element = stack.pop() if stack else '#'
if mapping[char] != top_element:
return False
else:
stack.append(char)
return not stack
"""
iface = gr.Interface(
fn=find_similar_questions,
inputs=gr.inputs.Textbox(lines=20, label="Enter a Code Example", value = s_example),
outputs=gr.outputs.Textbox(label="Similar Questions on Leetcode"),
title="📒 DSASearch Engine 🤖",
description="Find similar questions on Leetcode based on a code example.",
allow_flagging=False,
)
iface.launch()
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