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from langchain.prompts import StringPromptTemplate
import re
import langchain
from qa_txt import conversation_chain
from key_extract import chain
from bs4 import BeautifulSoup
import requests
from data_process import *
from langchain.tools.base import StructuredTool
from langchain.agents import initialize_agent
from qa_txt import llm
import gradio as gr
from langchain.agents import (
create_react_agent,
AgentExecutor,
tool,
)
from langchain import hub
prompt = hub.pull("hwchase17/react")
def faq(query: str) -> str:
reponse = conversation_chain.invoke({"question": query, "chat_history": []})
return reponse['answer']
qa_faq = StructuredTool.from_function(
func = faq ,
description="""
Respond to general and FAQ questions about the website .
Parameters :
- query (string) : the same input as the user input no more no less and dont translate it even if it is in another language.
Returns :
- string : the output as returned from the function in french.
"""
)
def request_data(query: str) -> str:
request = chain.invoke({"input": query})['text']
mot_cle = nettoyer_string(request)
mots = mot_cle.split()
ui = mots[0]
rg = chercher_data(ui)
if len(rg[0]):
reponse_final = format_reponse(rg)
return reponse_final
else:
return "Désolé, il semble que nous n'ayons pas de données correspondant à votre demande pour le moment. Avez-vous une autre question ou avez-vous besoin d'aide sur quelque chose d'autre?"
fetch_data = StructuredTool.from_function(
func=request_data,
description="""
Request and fetch data using a search keyword.
Parameters :
- query (string) : the same input as the user input no more no less and always it must be in french if it isn't already. For example : "give me data about health" the input is health in french which is santé, same for other languages.
Returns :
- string : the output as returned from the function in french , includes the link to all the data about the keyword along with an example.
""",
)
tools_add = [
qa_faq,
fetch_data,
]
agent = create_react_agent(llm=llm, tools=tools_add, prompt=prompt)
agent_executor = AgentExecutor(
agent=agent,
tools=tools_add,
verbose=True,
)
def data_gov_ma(message, history):
try:
response = agent_executor.invoke({"input": message})
return response['output']
except ValueError as e:
return "Je suis désolé, je n'ai pas compris votre question.Pourriez-vous la reformuler s'il vous plaît ?"
gr.ChatInterface(data_gov_ma).launch() |