Delete app (1).py

app (1).py

@@ -1,46 +0,0 @@
-import streamlit as st
-import os
-
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings 
-from langchain.vectorstores import FAISS
-from langchain.chat_models import ChatOpenAI
-from langchain.chains.question_answering import load_qa_chain
-
-st.set_page_config('preguntaDOC')
-st.header("Pregunta a tu PDF")
-OPENAI_API_KEY = st.text_input('OpenAI API Key', type='password')
-pdf_obj = st.file_uploader("Carga tu documento", type="pdf", on_change=st.cache_resource.clear)
-
-@st.cache_resource 
-def create_embeddings(pdf):
-    pdf_reader = PdfReader(pdf)
-    text = ""
-    for page in pdf_reader.pages:
-        text += page.extract_text()
-
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=800,
-        chunk_overlap=100,
-        length_function=len
-        )        
-    chunks = text_splitter.split_text(text)
-
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
-    knowledge_base = FAISS.from_texts(chunks, embeddings)
-
-    return knowledge_base
-
-if pdf_obj:
-    knowledge_base = create_embeddings(pdf_obj)
-    user_question = st.text_input("Haz una pregunta sobre tu PDF:")
-
-    if user_question:
-        os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
-        docs = knowledge_base.similarity_search(user_question, 3)
-        llm = ChatOpenAI(model_name='gpt-3.5-turbo')
-        chain = load_qa_chain(llm, chain_type="stuff")
-        respuesta = chain.run(input_documents=docs, question=user_question)
-
-        st.write(respuesta)