import os
import json
import gradio as gr
import zipfile
import tempfile
import requests
import urllib.parse
import io
from huggingface_hub import HfApi, login
from PyPDF2 import PdfReader
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_groq import ChatGroq
from dotenv import load_dotenv
from langchain.docstore.document import Document
# 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)
PERSIST_DIRECTORY = config["persist_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"]
REPOSITORY_DIRECTORY = config["repository_directory"]
GROQ_API_KEY = os.environ["GROQ_API_KEY"]
HF_TOKEN = os.environ["HF_Token"]
login(HF_TOKEN)
api = HfApi()
def load_project_id(json_file):
with open(json_file, 'r') as f:
data = json.load(f)
return data['project_id']
def download_gitlab_repo():
print("Start the upload_gitRepository function")
project_id = load_project_id('repository_ids.json')
encoded_project_id = urllib.parse.quote_plus(project_id)
# Define the URL to download the repository archive
archive_url = f"{GITLAB_API_URL}/projects/{encoded_project_id}/repository/archive.zip"
# Download the repository archive
response = requests.get(archive_url)
archive_bytes = io.BytesIO(response.content)
# Retrieve the original file name from the response headers
content_disposition = response.headers.get('content-disposition')
if content_disposition:
filename = content_disposition.split('filename=')[-1].strip('\"')
else:
filename = 'archive.zip' # Fallback to a default name if not found
# Check if the file already exists in the repository
existing_files = api.list_repo_files(repo_id=HF_SPACE_NAME, repo_type='space')
target_path = f"{REPOSITORY_DIRECTORY}/{filename}"
print(f"Target Path: '{target_path}'")
print(f"Existing Files: {[repr(file) for file in existing_files]}")
if target_path in existing_files:
print(f"File '{target_path}' already exists in the repository. Skipping upload...")
else:
# Upload the ZIP file to the new folder in the Hugging Face space repository
print("Uploading File to directory:")
print(f"Archive Bytes: {repr(archive_bytes.getvalue())[:100]}") # Show a preview of bytes
print(f"Target Path in Repo: '{target_path}'")
api.upload_file(
path_or_fileobj=archive_bytes,
path_in_repo=target_path,
repo_id=HF_SPACE_NAME,
repo_type='space'
)
print("Upload complete")
def get_all_files_in_folder(temp_dir, partial_path):
all_files = []
print("inner method of get all files in folder")
target_dir = os.path.join(temp_dir, partial_path)
print(target_dir)
for root, dirs, files in os.walk(target_dir):
print(f"Files in current directory ({root}): {files}")
for file in files:
print(f"Processing file: {file}")
all_files.append(os.path.join(root, file))
return all_files
def get_file(temp_dir, file_path):
full_path = os.path.join(temp_dir, file_path)
return full_path
def process_directory(directory, partial_paths=None, file_paths=None):
all_texts = []
file_references = []
zip_files = [file for file in os.listdir(directory) if file.endswith('.zip')]
if not zip_files:
print("No zip file found in the directory.")
return all_texts, file_references
if len(zip_files) > 1:
print("More than one zip file found.")
return all_texts, file_references
else:
zip_file_path = os.path.join(directory, zip_files[0])
# Create a temporary directory for the zip file
with tempfile.TemporaryDirectory() as tmpdirname:
# Unzip the file into the temporary directory
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
zip_ref.extractall(tmpdirname)
print(f"Extracted {zip_file_path} to {tmpdirname}")
files = []
unzipped_root = os.listdir(tmpdirname)
if len(unzipped_root) == 1 and os.path.isdir(os.path.join(tmpdirname, unzipped_root[0])):
tmpsubdirpath= os.path.join(tmpdirname, unzipped_root[0])
else:
tmpsubdirpath = tmpdirname
if partial_paths:
print("Go in partial_paths")
for partial_path in partial_paths:
files += get_all_files_in_folder(tmpsubdirpath, partial_path)
else:
print("wtf")
for root, _, files_list in os.walk(tmpdirname):
for file in files_list:
files.append(os.path.join(root, file))
if file_paths:
print("Go in normal paths")
files += [get_file(tmpdirname, file_path) for file_path in file_paths]
print(f"Total number of files: {len(files)}")
for file_path in files:
print(f"Paths of files: {files}")
file_ext = os.path.splitext(file_path)[1]
if os.path.getsize(file_path) == 0:
print(f"Skipping an empty file: {file_path}")
continue
with open(file_path, 'rb') as f:
if file_ext in ['.rst', '.md', '.txt', '.html', '.json', '.yaml', '.py']:
text = f.read().decode('utf-8')
print(f"Extracted text from {file_path}:\n{text[:50]}...\n")
elif file_ext in ['.svg']:
text = f"SVG file content from {file_path}"
elif file_ext in ['.png', '.ico']:
text = f"Image metadata from {file_path}"
else:
continue
all_texts.append(text)
file_references.append(file_path)
print(f"Print the text for testing broooo {all_texts}")
return all_texts, file_references
# 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}) for chunk in text_splitter.split_text(text)])
print(f"Total number of chunks: {len(chunks)}")
return chunks
# Setup Chroma
def setup_chroma(chunks, model_name="sentence-transformers/all-mpnet-base-v2", persist_directory="chroma_data"):
embedding_model = HuggingFaceEmbeddings(model_name=model_name)
vectorstore = Chroma.from_documents(chunks, embedding=embedding_model, persist_directory=persist_directory)
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 query_chroma(vectorstore, query, k):
results = vectorstore.similarity_search(query, k=k)
chunks_with_references = [(result.page_content, result.metadata["source"]) for result in results]
# Print the chosen chunks and their sources to the console
print("\nChosen chunks and their sources for the query:")
for chunk, source in chunks_with_references:
print(f"Source: {source}\nChunk: {chunk}\n")
print("-" * 50)
return chunks_with_references
def rag_workflow(query):
docs = query_chroma(vectorstore, query, k=10)
context = "\n\n".join([doc for doc, _ in docs])
references = "\n".join([f"[{i+1}] {ref}" for i, (_, ref) in enumerate(docs)])
print(f"Context for the query:\n{context}\n")
print(f"References for the query:\n{references}\n")
prompt = f"""You are an expert python developer. Provide a clear and consice answer based only on the information in the retrieved context.
The retrieved context contains source code and documenation of an api library.
If no related Information is found from the context to answer the query, reply that you do not know.
Context:
{context}
Query:
{query}
"""
response = llm.invoke(prompt)
return response.content, references
def initialize():
global vectorstore, chunks, llm
partial_paths = ['kadi-apy-master/docs/source/setup/', 'kadi-apy-master/kadi_apy/lib/','kadi-apy-master/docs/source/usage/']
file_paths = []
all_texts, file_references = process_directory(REPOSITORY_DIRECTORY, partial_paths, file_paths)
chunks = split_into_chunks(all_texts, file_references, 512, 0)
print(f"Total number of chunks: {len(chunks)}")
# vectorstore = setup_chroma(chunks, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY)
# llm = setup_llm(LLM_MODEL_NAME, LLM_TEMPERATURE, GROQ_API_KEY)
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, references = rag_workflow(user_query)
history[-1] = (user_query, response)
# Format references for display with text passages
formatted_references = ""
docs = query_chroma(vectorstore, user_query, k=5)
for i, (doc, ref) in enumerate(docs):
formatted_references += f"""
Reference {i+1}
Source: {ref}
Text: {doc}
"""
yield history, formatted_references
def main():
with gr.Blocks() as demo:
gr.Markdown("## Kadi4Mat - AI Chat-Bot")
gr.Markdown("AI assistant for Kadi4Mat based on RAG architecture powered by LLM")
with gr.Tab("Kadi4Mat - 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)
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,
)
with gr.Column(scale=3):
with gr.Tab("References"):
doc_citation = gr.HTML("
References used in answering the question will be displayed below.