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import os
import json
import gradio as gr
from huggingface_hub import HfApi, login
from dotenv import load_dotenv
from llm import get_groq_llm
from vectorstore import get_chroma_vectorstore
from embeddings import get_SFR_Code_embedding_model
from kadiApy_ragchain import KadiApyRagchain
# Load environment variables from .env file
load_dotenv()
vectorstore_path = "data/vectorstore"
GROQ_API_KEY = os.environ["GROQ_API_KEY"]
HF_TOKEN = os.environ["HF_Token"]
with open("config.json", "r") as file:
config = json.load(file)
login(HF_TOKEN)
# Access the values
LLM_MODEL_NAME = config["llm_model_name"]
LLM_MODEL_TEMPERATURE = float(config["llm_model_temperature"])
# A class to encapsulate the bot logic
class KadiBot:
def __init__(self, hf_token: str, groq_api_key: str, config: dict, vectorstore_path: str):
self.vectorstore = get_chroma_vectorstore(get_SFR_Code_embedding_model(), vectorstore_path)
self.llm = get_groq_llm(config["llm_model_name"], float(config["llm_model_temperature"]), groq_api_key)
self.kadiAPY_ragchain = KadiApyRagchain(self.llm, self.vectorstore)
def process_query(self, user_query, chat_history):
response = self.kadiAPY_ragchain.process_query(user_query, chat_history)
chat_history[-1] = (user_query, response)
return chat_history
def add_text_to_chat_history(chat_history, user_input):
chat_history = chat_history + [(user_input, None)]
return chat_history, ""
def show_history(chat_history):
return chat_history
def reset_all():
return [], "", ""
def main():
# Initialize the KadiBot
kadi_bot = KadiBot(HF_TOKEN, GROQ_API_KEY, config, vectorstore_path)
with gr.Blocks() as demo:
gr.Markdown("## KadiAPY - AI Coding-Assistant")
gr.Markdown("AI assistant for KadiAPY based on RAG architecture powered by LLM")
# Create a state for session management
chat_history = gr.State([])
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=[
"Write me a python script with which can convert plain JSON to a Kadi4Mat-compatible extra metadata structure",
"I need a method to upload a file to a record. The id of the record is 3",
],
inputs=user_txt,
outputs=chatbot,
fn=add_text_to_chat_history,
label="Try asking...",
cache_examples=False,
examples_per_page=3,
)
# Use the state to persist chat history between interactions
user_txt.submit(add_text_to_chat_history, [chat_history, user_txt], [chat_history, user_txt]).then(show_history, [chat_history], [chatbot])\
.then(kadi_bot.process_query, [chat_history], [chatbot])
submit_btn.click(add_text_to_chat_history, [chat_history, user_txt], [chat_history, user_txt]).then(show_history, [chat_history], [chatbot])\
.then(kadi_bot.process_query, [chat_history], [chatbot])
clear_btn.click(
reset_all,
None,
[chat_history, chatbot, user_txt],
queue=False
)
demo.launch()
if __name__ == "__main__":
main()
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