| import streamlit as st
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| from utils.indexing import *
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| from utils.retrieval import *
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| from utils.generation import *
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| from io import BytesIO
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| st.title("Chat with your PDF docs using Resoning and Action Prompt")
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| uploaded_file = st.file_uploader("Choose a PDF files", type="pdf", accept_multiple_files=True)
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| if uploaded_file is not None:
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| all_split_docs = []
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| for file in uploaded_file:
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| pdf_bytes = BytesIO(file.read())
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| document = load_document(pdf_bytes)
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| doc_split = splitter(document)
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| all_split_docs.extend(doc_split)
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|
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| if all_split_docs:
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| create_vectorstore(all_split_docs)
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| query = st.text_input("Ask query to document.")
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| if query:
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| embedding = GPT4AllEmbeddings()
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| db = FAISS.load_local('D:/my_projects/ScholarQuery/assignment/faiss_index', embedding, allow_dangerous_deserialization=True)
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| retrieval = db.as_retriever()
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| llm = model()
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| response = Naive_retriever(llm, retrieval, query)
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| st.write(response)
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