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import openai
import os
import gradio as gr
from langchain.document_loaders import DirectoryLoader, TextLoader, UnstructuredFileLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
os.environ["OPENAI_API_KEY"] = "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"
def question_document(Document, Question):
# Load documents with DirectoryLoader
if not Document.name.endswith('.txt'):
return ("Le document doit être un fichier texte (.txt)")
loader = TextLoader(Document.name, encoding = "ISO-8859-1")
#loader = DirectoryLoader("", glob="*.txt", loader_kwargs = {"encoding" : "ISO-8859-1"})
txt_docs = loader.load_and_split()
# Create embeddings
embeddings = OpenAIEmbeddings()
# Write in DB
txt_docsearch = Chroma.from_documents(txt_docs, embeddings)
# Define LLM
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.3)
# Create Retriever
qa_txt = RetrievalQA.from_chain_type(llm=llm,
chain_type="map_reduce",
retriever=txt_docsearch.as_retriever()
)
answer = qa_txt.run(Question) #+ "If you don't find the answer in the document, don't answer, say you don't know, in the language of the question." )
return answer
#Définition de l'interface
iface = gr.Interface(
fn = question_document,
inputs= ["file","text"],
outputs = gr.outputs.Textbox(label="Réponse"),
title="Long Text Questioner",
description="par Nicolas \nPermet d'interroger un document texte",
allow_flagging = "never")
iface.launch() |