Create app.py

app.py

@@ -0,0 +1,60 @@
+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))
+
+
+iface = gr.Interface(
+    fn=find_similar_questions,
+    inputs=gr.inputs.Textbox(lines=20, label="Enter a Code 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()