Create app.py

app.py

@@ -0,0 +1,116 @@
+import streamlit as st
+import os
+from github import Github
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.llms import OpenAI
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+openai_api_key = os.getenv("OPENAI_API_KEY")
+
+# Function to fetch repository data from GitHub
+def fetch_github_repo_data(git_repo, github_token):
+    try:
+        g = Github(github_token)
+        repo = g.get_repo(git_repo)
+        contents = repo.get_contents("")
+        repo_data = ""
+        while contents:
+            file_content = contents.pop(0)
+            if file_content.type == "dir":
+                contents.extend(repo.get_contents(file_content.path))
+            else:
+                file_data = repo.get_contents(file_content.path).decoded_content
+                try:
+                    text = file_data.decode("utf-8")
+                    repo_data += f"\n\nFile: {file_content.path}\n{text}"
+                except UnicodeDecodeError:
+                    # Skip non-text files
+                    continue
+        return repo_data
+    except Exception as e:
+        st.error(f"Error fetching GitHub repository data: {e}")
+        return None
+
+# Function to perform RAG using OpenAI and Chroma
+def perform_rag(repo_data, prompt):
+    try:
+        if repo_data:
+            # Create embeddings
+            embeddings = HuggingFaceEmbeddings()
+
+            # Split text into chunks
+            text_splitter = RecursiveCharacterTextSplitter(
+                chunk_size=1000,
+                chunk_overlap=20,
+                length_function=len,
+                is_separator_regex=False,
+            )
+            chunks = text_splitter.create_documents([repo_data])
+
+            # Store chunks in ChromaDB
+            persist_directory = 'github_repo_embeddings'
+            vectordb = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=persist_directory)
+            vectordb.persist()  # Persist ChromaDB
+
+            # Load persisted Chroma database
+            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+
+            # Perform retrieval using Chroma
+            docs = vectordb.similarity_search(prompt)
+            if docs:
+                text = docs[0].page_content
+            else:
+                st.warning("No relevant documents found.")
+                return None
+
+            # Perform generation using OpenAI
+            llm = OpenAI(api_key=openai_api_key, model="gpt-4", temperature=0.7, max_tokens=500)
+            question_with_context = f"Context: {text}\n\nQuestion: {prompt}\n\nAnswer:"
+            response = llm.generate(question_with_context)
+
+            return response
+        else:
+            st.warning("No repository data found or error occurred.")
+            return None
+
+    except Exception as e:
+        st.error(f"Error performing RAG: {e}")
+        return None
+
+# Streamlit application
+def main():
+    st.title("Chat with GitHub Repository \ud83d\udcac")
+    st.caption("This app allows you to chat with a GitHub Repo using OpenAI and ChromaDB")
+
+    # Get the GitHub token from the user
+    github_token = st.text_input("Enter your GitHub Token", type="password")
+
+    # Get the GitHub repository from the user
+    git_repo = st.text_input("Enter the GitHub Repo (owner/repo)", type="default")
+
+    # Add the GitHub data to the knowledge base if the GitHub token is provided
+    if github_token and git_repo:
+        # Fetch GitHub repository data
+        repo_data = fetch_github_repo_data(git_repo, github_token)
+
+        if repo_data:
+            st.success(f"Added {git_repo} to knowledge base!")
+
+            # Ask a question about the repository
+            prompt = st.text_input("Ask any question about the GitHub Repo")
+
+            # Chat with the repository
+            if prompt:
+                answer = perform_rag(repo_data, prompt)
+                if answer:
+                    st.subheader("Generated Answer:")
+                    st.write(answer)
+        else:
+            st.error(f"Failed to fetch data for {git_repo}. Please check the repository name and your token's permissions.")
+
+if __name__ == "__main__":
+    main()