Create app.py

app.py

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+import streamlit as st
+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)
+
+
+@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()
+  return db
+
+
+def get_similar_links(query, db, embeddings):
+  docs_and_scores = db.similarity_search_by_vector(embedding_vector)
+  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)
+  return hrefs
+
+
+embedding_vector = get_hugging_face_model()
+db = get_db()
+st.title("📒 PDSASearch Engine 🤖 ")
+text_input = st.text_input("Enter some text")
+button = st.button("Find Similar Questions on Leetcode")
+if text_input:
+  query = text_input
+  answer = get_similar_links(query, db, embedding_vector)
+  st.write("".join(answer))
+
+else:
+  st.info("Please Input Valid Text")
+
+with st.sidebar:
+  st.markdown("""
+        ### Created by Ashwin Rachha.
+        Source Data : https://github.com/AshwinRachha/LeetCode-Solutions
+        Medium Blog : 
+    """)