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# -*- coding: utf-8 -*-
"""finito.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1rNnk3xajWzVdyH2eXJ58ghLpk-zejGw-
"""
!pip install -r requirements.txt
!pip install gradio
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
def faq(query: str) -> str:
reponse = conversation_chain({"question": query, "chat_history": []})
return reponse['answer']
qa_faq = StructuredTool.from_function(
func = faq ,
description="""
Repondre à des questions general .
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.run(query)
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="""
Rechercher des données.
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.
""",
)
tools_add = [
qa_faq,
fetch_data,
]
agent = initialize_agent(
tools = tools_add,
llm = llm,
agent = "zero-shot-react-description",
verbose = True
)
agent.invoke("bonjour je veux l'addresse de contact. Et donner moi les donnée de la finance")
gr.ChatInterface(agent.invoke).launch() |