app.py

import os
import json
import gradio as gr
import streamlit as st
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 kadi_apy_bot import KadiAPYBot

# 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)
hf_api = HfApi()

# Access the values
LLM_MODEL_NAME = config["llm_model_name"]
LLM_MODEL_TEMPERATURE = float(config["llm_model_temperature"])

def initialize():
    global kadiAPY_bot

    vectorstore = get_chroma_vectorstore(get_SFR_Code_embedding_model(), vectorstore_path)
    llm = get_groq_llm(LLM_MODEL_NAME, LLM_MODEL_TEMPERATURE, GROQ_API_KEY)

    kadiAPY_bot = KadiAPYBot(llm, vectorstore)

initialize()

def bot_kadi(history, state):
    """
    Handle user input and generate bot response using gr.State().
    """
    user_query = history[-1][0]
    
    # Add user query to the bot's state for session-specific history
    state["history"].append({"query": user_query, "response": None})
    
    # Process the query with the bot and generate a response
    response = kadiAPY_bot.process_query(user_query)
    
    # Save the response back to session state
    state["history"][-1]["response"] = response
    
    history[-1] = (user_query, response)
    yield history

# Gradio UI
def add_text(history, text, state):
    """
    Add user text to history and initialize state if needed.
    """
    if "history" not in state:
        state["history"] = []  # Initialize session-specific state
    
    history = history + [(text, None)]
    yield history, ""

def check_input_text(text):
    if not text:
        gr.Warning("Please input a question.")
        raise TypeError
    return True
    
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")
                    
                    # Create session-specific state with gr.State
                    session_state = gr.State()  

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


    demo.launch()

if __name__ == "__main__":
    main()