Upload 3 files

app.py

@@ -0,0 +1,68 @@
+from PyPDF2 import PdfReader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS
+import streamlit as st
+from dotenv import load_dotenv,find_dotenv
+from streamlit_extras.add_vertical_space import add_vertical_space
+import pickle 
+import os
+
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+
+## Slide-bar
+with st.sidebar:
+    st.title('PDF Q&A')
+    st.markdown('''
+    ## About
+    This app is an LLM-powered chatbot built using:
+    - [Streamlit](https://streamlit.io/)
+    - [LangChain](https://python.langchain.com/)
+    - [OpenAI](https://platform.openai.com/docs/models) LLM model
+ 
+    ''')
+    add_vertical_space(5)
+    st.write('Made by Harshit')
+    
+def main():
+    st.header("Q&A from Pdfs: ")
+    
+    
+    load_dotenv(find_dotenv())
+    
+    pdf_reader = PdfReader('48lawsofpower.pdf')
+    # st.write(pdf_reader)
+    
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+        
+    text_splitter = CharacterTextSplitter(
+    separator = "\n",
+    chunk_size = 1000,
+    chunk_overlap  = 200,
+    length_function = len,
+    )
+    ## Chunk Formation
+    chunks = text_splitter.split_text(text= text)
+    
+    ## Embedding    
+    embeddings = OpenAIEmbeddings()
+    document_search = FAISS.from_texts(chunks, embeddings)
+    
+    
+    query = st.text_input("Ask your questions: ")
+     
+    docs = document_search.similarity_search(query=query)
+ 
+    llm = OpenAI()
+    chain = load_qa_chain(llm=llm, chain_type="stuff")
+    response = chain.run(input_documents=docs, question=query)
+    st.write(response)
+    
+        
+if __name__ == '__main__':
+    main()
+    
+    

requirements.txt

@@ -0,0 +1,9 @@
+langchain
+openai
+PyPDF2
+faiss-cpu
+tiktoken
+streamlit
+python-dotenv
+pickle
+os