github with streamlit - need to integrate api key retrieval and dynamic repo specification

github_st.py

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+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")
+USER = "heaversm"
+REPO = "gdrive-docker"
+GITHUB_BASE_URL = "https://github.com/"
+FILE_TYPES_TO_LOAD = (".py", ".ts")
+
+@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")
+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(
+        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")
+
+
+