Upload 3 files

utils.py

@@ -1,49 +0,0 @@
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain import FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.llms import openai
-from langchain.chat_models import ChatOpenAI
-from langchain.callbacks import get_openai_callback
-from PyPDF2 import PdfReader
-
-def process_text(text):
-    text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size=1000,
-        chunk_overlap=200,
-        length_function=len
-    )
-
-    chunks = text_splitter.split_text(text)
-
-    embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
-
-    knowledgeBase=FAISS.from_texts(chunks,embeddings)
-
-    return knowledgeBase
-
-def summarizer(pdf):
-    if pdf is not None:
-        pdf_reader=PdfReader(pdf)
-        text = ""
-
-        for page in pdf_reader.pages:
-            text += page.extract_text() or ""
-
-        knowledgeBase = process_text(text)
-
-        query="Summarize the content of the uploaded PDF file in 10-15 sentences."
-
-        if query:
-            docs=knowledgeBase.similarity_search(query)
-
-            OpenAIModel = "gpt-3.5-turbo-16k"
-            llm = ChatOpenAI(model=OpenAIModel, temperature=0.7)
-
-            chain=load_qa_chain(llm, chain_type='stuff')
-
-            with get_openai_callback() as cost:
-                response=chain.run(input_documents=docs, question=query)
-                print(cost)
-                return response