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import os
import json
import bcrypt
import pandas as pd
import numpy as np
from typing import List
from pathlib import Path
from langchain_huggingface import HuggingFaceEndpoint
from langchain.schema import StrOutputParser
from langchain.agents import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_csv_agent
import chainlit as cl
from chainlit.input_widget import TextInput, Select, Switch, Slider
from deep_translator import GoogleTranslator
@cl.step(type="tool")
async def LLMistral():
os.environ['HUGGINGFACEHUB_API_TOKEN'] = os.environ['HUGGINGFACEHUB_API_TOKEN']
repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
llm = HuggingFaceEndpoint(
repo_id=repo_id, max_new_tokens=5300, temperature=0.5, task="text2text-generation", streaming=True
)
return llm
@cl.set_chat_profiles
async def chat_profile():
return [
cl.ChatProfile(name="Traitement des données d'enquête : «Expé CFA : questionnaire auprès des professionnels de la branche de l'agencement»",markdown_description="Vidéo exploratoire autour de l'événement",icon="/public/logo-ofipe.png",),
]
@cl.set_starters
async def set_starters():
return [
cl.Starter(
label="Répartition du nombre de CAA dans les entreprises",
message="Quel est le nombre de chargé.e d'affaires en agencement dans les entreprises?",
icon="/public/request-theme.svg",
)
]
@cl.on_message
async def on_message(message: cl.Message):
await cl.Message(f"> SURVEYIA").send()
model = await LLMistral()
agent = create_csv_agent(
llm,
"./public/ExpeCFA_LP_CAA.csv",
verbose=False,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION
)
msg = cl.Message(content="")
class PostMessageHandler(BaseCallbackHandler):
"""
Callback handler for handling the retriever and LLM processes.
Used to post the sources of the retrieved documents as a Chainlit element.
"""
def __init__(self, msg: cl.Message):
BaseCallbackHandler.__init__(self)
self.msg = msg
self.sources = set() # To store unique pairs
def on_retriever_end(self, documents, *, run_id, parent_run_id, **kwargs):
for d in documents:
source_page_pair = (d.metadata['source'], d.metadata['page'])
self.sources.add(source_page_pair) # Add unique pairs to the set
def on_llm_end(self, response, *, run_id, parent_run_id, **kwargs):
sources_text = "\n".join([f"{source}#page={page}" for source, page in self.sources])
self.msg.elements.append(
cl.Text(name="Sources", content=sources_text, display="inline")
)
cb = cl.AsyncLangchainCallbackHandler()
res = await agent.acall("Réponds en langue française à la question suivante :\n" + message.content + "\nDétaille la réponse en faisant une analyse complète en 2000 mots minimum.", callbacks=[cb])
answer = res['output']
await cl.Message(content=GoogleTranslator(source='auto', target='fr').translate(answer)).send() |