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import os
import gradio as gr
from gradio import ChatMessage
import base64
from llama_index.core import StorageContext, load_index_from_storage
from dotenv import load_dotenv
from retrieve import get_latest_dir, get_latest_html_file
from graph_handler import query_graph_qa, plot_subgraph
from embed_handler import query_rag_qa
from evaluate import evaluate_llm, reasoning_graph, get_coupon
import base64
import time
load_dotenv()
KG_INDEX_PATH = get_latest_dir(os.getenv("GRAPH_DIR"))
KG_PLOT_PATH = get_latest_html_file(os.getenv("GRAPH_VIS"))
RAG_INDEX_PATH = get_latest_dir(os.getenv("EMBEDDING_DIR"))
# Load Graph-RAG index
graph_rag_index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=KG_INDEX_PATH)
)
# Load RAG index
rag_index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=RAG_INDEX_PATH)
)
def query_tqa(query, search_level):
"""
Query the Graph-RAG and RAG models for a given query.
Args:
query (str): The query to ask the RAGs.
search_level (int): The max search level to use for the Graph RAG.
Returns:
tuple: The response, reference, and reference text for the Graph-RAG and RAG models.
"""
if not query.strip():
raise gr.Error("Please enter a query before asking.")
grag_response, grag_reference, grag_reference_text = query_graph_qa(
graph_rag_index, query, search_level
)
# rag_response, rag_reference, rag_reference_text = query_rag_qa(
# rag_index, query, search_level
# )
print(str(grag_response.response))
return (
str(grag_response.response)
)
# with gr.Blocks() as demo:
# gr.Markdown("# Comfy Virtual Assistant")
# chatbot = gr.Chatbot(
# label="Comfy Virtual Assistant",
# type="messages",
# scale=1,
# # suggestions = [
# # {"text": "How much iphone cost?"},
# # {"text": "What phone options do i have ?"}
# # ],
# )
# msg = gr.Textbox(label="Input Your Query")
# clear = gr.ClearButton([msg, chatbot])
# def respond(message, chat_history):
# bot_message = query_tqa(message, 2)
# # chat_history.append((message, bot_message))
# chat_history.append(ChatMessage(role="user", content=message))
# chat_history.append(ChatMessage(role="assistant", content=bot_message))
# time.sleep(1)
# return "", chat_history
# msg.submit(respond, [msg, chatbot], [msg, chatbot])
def chatbot_response(message: str, history: List[Tuple[str, str]]) -> str:
# Use the query_tqa function to get the response
search_level = 2 # You can adjust this or make it configurable
response = query_tqa(message, search_level)
return response
# Create the Gradio interface
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.Button("Clear")
def user(user_message, history):
return "", history + [[user_message, None]]
def bot(history):
bot_message = chatbot_response(history[-1][0], history)
history[-1][1] = bot_message
return history
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, chatbot, chatbot
)
clear.click(lambda: None, None, chatbot, queue=False)
demo.launch(auth=(os.getenv("ID"), os.getenv("PASS")), share=False)
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