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app.py

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+import streamlit as st
+from langchain.llms import HuggingFaceHub
+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")
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-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')
+        llm = HuggingFaceHub(repo_id="lmsys/vicuna-7b-v1.1", model_kwargs={"temperature":0.5, "max_length":512})
+        chain = load_qa_chain(llm, chain_type="stuff")
+        respuesta = chain.run(input_documents=docs, question=user_question)
+
+        st.write(respuesta)    

requeriments.txt

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+openai
+langchain
+faiss-cpu
+streamlit
+PyPDF2
+sentence-transformers