app.py

import os
import json
import gradio as gr

from huggingface_hub import HfApi, login
from PyPDF2 import PdfReader
from langchain_huggingface import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_groq import ChatGroq
from dotenv import load_dotenv
from langchain.docstore.document import Document
from langchain.schema import Document

from chunk_python_code import chunk_python_code_with_metadata
from vectorstore import get_chroma_vectorstore
from download_repo_to_huggingface import download_and_upload_kadiAPY_repo_to_huggingfacespace
from process_repo_zipfile import extract_files_and_filepath_from_dir
from ragchain import RAGChain

# Load environment variables from .env file
load_dotenv()

# Load configuration from JSON file
with open('config.json') as config_file:
    config = json.load(config_file)

with open("config2.json", "r") as file:
    config2 = json.load(file)

PERSIST_DOC_DIRECTORY = config["persist_doc_directory"]
PERSIST_CODE_DIRECTORY =config["persist_code_directory"]
CHUNK_SIZE = config["chunk_size"]
CHUNK_OVERLAP = config["chunk_overlap"]
EMBEDDING_MODEL_NAME = config["embedding_model"]
LLM_MODEL_NAME = config["llm_model"]
LLM_TEMPERATURE = config["llm_temperature"]
GITLAB_API_URL = config["gitlab_api_url"]
HF_SPACE_NAME = config["hf_space_name"]
DATA_DIR = config["data_dir"]

GROQ_API_KEY = os.environ["GROQ_API_KEY"]
HF_TOKEN = os.environ["HF_Token"]



login(HF_TOKEN)
api = HfApi()



def split_python_code_into_chunks(texts, file_paths):
    chunks = [] 
    for text, file_path in zip(texts, file_paths):
        document_chunks = chunk_python_code_with_metadata(text, file_path)
        chunks.extend(document_chunks)   
    return chunks


# Split text into chunks
def split_into_chunks(texts, references, chunk_size, chunk_overlap):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
    chunks = []

    for text, reference in zip(texts, references):
        chunks.extend([
            Document(
                page_content=chunk,
                metadata={
                    "source": reference,
                    "usage": "doc"
                }
            ) 
            for chunk in text_splitter.split_text(text)
        ])
    return chunks


# Setup Vectorstore
def embed_documents_into_vectorstore(chunks, model_name, persist_directory):
    print("Start setup_vectorstore_function")
    embedding_model = HuggingFaceEmbeddings(model_name=model_name)   
    vectorstore = get_chroma_vectorstore(embedding_model, persist_directory)
    vectorstore.add_documents(chunks)
    return vectorstore

# Setup LLM
def setup_llm(model_name, temperature, api_key):
    llm = ChatGroq(model=model_name, temperature=temperature, api_key=api_key)
    return llm


def format_kadi_apy_library_context(docs):
    doc_context = []
    
    for doc in docs:
        # Extract metadata information
        class_info = doc.metadata.get("class", "Unknown Class")
        type_info = doc.metadata.get("type", "Unknown Type")
        source_info = doc.metadata.get("source", "Unknown Type")

        print(":}\n\n", doc.page_content)
        formatted_doc = f"# source: {source_info}\n# class: {class_info}\n# type: {type_info}\n{doc.page_content}\n\n\n"
        doc_context.append(formatted_doc)
    
    return doc_context


def format_kadi_api_doc_context(docs):
    doc_context = []

    for doc in docs:
        source_info = doc.metadata.get("source", "Unknown Type")
        print(":}\n\n", doc.page_content)
        formatted_doc = f"# source: {source_info}\n{doc.page_content}\n\n\n"
        doc_context.append(formatted_doc)
    
    return doc_context


                   
def rag_workflow(query):
    """
    RAGChain class to perform the complete RAG workflow.
    """
    # Assume 'llm' and 'vector_store' are already initialized instances
    rag_chain = RAGChain(llm, vector_store)

    # Step 1: Predict which library usage is relevant
    # predict which python library to search in: (standard) kadiAPY-library or kadiAPY-cli-library 
    code_library_usage_prediction = rag_chain.predict_library_usage(query)
    print(f"Predicted library usage: {code_library_usage_prediction}")

    
    # Step 2: Retrieve contexts (documents and code snippets)
    kadiAPY_doc_documents, kadiAPY_code_documents = rag_chain.retrieve_contexts(query, code_library_usage_prediction)
    print("Retrieved Document Contexts:", kadiAPY_doc_documents)
    print("Retrieved Code Contexts:", kadiAPY_code_documents)

    
    formatted_doc_snippets = rag_chain.format_documents(kadiAPY_doc_documents)
    formatted_code_snippets = rag_chain.format_documents(kadiAPY_code_documents)
    print("FORMATTED Retrieved Document Contexts:", formatted_doc_snippets)
    print("FORMATTED Retrieved Code Contexts:" , formatted_doc_snippets)
    print(formatted_code_snippets)
    # Step 4: Generate the final response
    response = rag_chain.generate_response(query, formatted_doc_snippets, formatted_code_snippets)
    print("Generated Response:", response)

    return response


def initialize():
    global vector_store, chunks, llm

    
    download_and_upload_kadiAPY_repo_to_huggingfacespace(
        api_url=config2["gitlab"]["api_url"],
        project_id=config2["gitlab"]["project"]["id"],
        version=config2["gitlab"]["project"]["version"]
    )
    
    code_texts, code_references = extract_files_and_filepath_from_dir(DATA_DIR, ['kadi_apy'], [])
    doc_texts, kadiAPY_doc_references = extract_files_and_filepath_from_dir(DATA_DIR, ['docs/source/'], [])
    
    print("LEEEEEEEEEEEENGTH of code_texts: ", len(code_texts))
    print("LEEEEEEEEEEEENGTH of doc_files: ", len(doc_texts))
    
    code_chunks = split_python_code_into_chunks(code_texts, code_references)
    doc_chunks = split_into_chunks(doc_texts, kadiAPY_doc_references, CHUNK_SIZE, CHUNK_OVERLAP)

    print(f"Total number of code_chunks: {len(code_chunks)}")
    print(f"Total number of doc_chunks: {len(doc_chunks)}")

    filename = "test"
    vector_store = embed_documents_into_vectorstore(doc_chunks + code_chunks, EMBEDDING_MODEL_NAME, f"{DATA_DIR}/{filename}")
    llm = setup_llm(LLM_MODEL_NAME, LLM_TEMPERATURE, GROQ_API_KEY)

    from langchain_community.document_loaders import TextLoader
          
initialize()


# Gradio utils
def check_input_text(text):
    if not text:
        gr.Warning("Please input a question.")
        raise TypeError
    return True

def add_text(history, text):
    history = history + [(text, None)]
    yield history, ""


import gradio as gr


def bot_kadi(history):
    user_query = history[-1][0]
    response = rag_workflow(user_query)
    history[-1] = (user_query, response)

    yield history  

def main():
    with gr.Blocks() as demo:
        gr.Markdown("## KadiAPY - AI Coding-Assistant")
        gr.Markdown("AI assistant for KadiAPY based on RAG architecture powered by LLM")

        with gr.Tab("KadiAPY - AI Assistant"):
            with gr.Row():
                with gr.Column(scale=10):
                    chatbot = gr.Chatbot([], elem_id="chatbot", label="Kadi Bot", bubble_full_width=False, show_copy_button=True, height=600)
                    user_txt = gr.Textbox(label="Question", placeholder="Type in your question and press Enter or click Submit")

                    with gr.Row():
                        with gr.Column(scale=1):
                            submit_btn = gr.Button("Submit", variant="primary")
                        with gr.Column(scale=1):
                            clear_btn = gr.Button("Clear", variant="stop")

                    gr.Examples(
                        examples=[
                            "Who is working on Kadi4Mat?",
                            "How do i install the Kadi-Apy library?",
                            "How do i install the Kadi-Apy library for development?",
                            "I need a method to upload a file to a record",
                        ],
                        inputs=user_txt,
                        outputs=chatbot,
                        fn=add_text,
                        label="Try asking...",
                        cache_examples=False,
                        examples_per_page=3,
                    )

            user_txt.submit(check_input_text, user_txt, None).success(add_text, [chatbot, user_txt], [chatbot, user_txt]).then(bot_kadi, [chatbot], [chatbot])
            submit_btn.click(check_input_text, user_txt, None).success(add_text, [chatbot, user_txt], [chatbot, user_txt]).then(bot_kadi, [chatbot], [chatbot])
            clear_btn.click(lambda: None, None, chatbot, queue=False)

    demo.launch() 

    
if __name__ == "__main__":
    main()