Create app.py

app.py

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+# https://www.youtube.com/watch?v=dXxQ0LR-3Hg
+
+# Create Conda virtual environment
+# conda create --name gpt_chatbot  python=3.9.4
+# conda activate gpt_chatbot
+
+# Installation
+# pip install streamlit pypdf2 langchain python-dotenv faiss-cpu openai huggingface_hub
+# pip install tiktoken 
+
+# pip install InstructorEmbedding sentence_transformers
+
+# Could not import tiktoken python package. This is needed in order to for OpenAIEmbeddings. Please install it with `pip install tiktoken`.
+# run the app using the following command in anaconda VS Code terminal
+# streamlit run app.py
+
+
+
+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.vectorstores import FAISS # FAISS instead of PineCone
+# from langchain.llms import OpenAI
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from htmlTemplates import css, bot_template, user_template 
+
+def get_pdf_text(pdf_docs):
+    text =""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+def get_text_chunks(text):
+    text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return  chunks
+
+def get_vectorstore(text_chunks):
+    # embeddings = OpenAIEmbeddings()
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+def get_conversation_chain(vectorstore):
+    #llm = OpenAI()
+    llm = ChatOpenAI()
+    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=vectorstore.as_retriever(),
+        memory=memory
+    )
+    return conversation_chain
+
+
+
+def handle_userinput(user_question):
+    # st.session_state.conversation contains all the configuration from our vectorstore and memory.
+    response = st.session_state.conversation({'question': user_question})
+    # st.write(response)
+    st.session_state.chat_history = response['chat_history']
+
+    for i, message in enumerate(st.session_state.chat_history):
+        if i % 2 == 0:
+            st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+        else:
+            st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+
+
+
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Chat with multiple law journal PDFs",
+                        page_icon=":books:")
+    
+    st.write(css, unsafe_allow_html=True)
+    
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = None
+    
+    st.header("Chat with multiple PDFs :books:")
+
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(user_question)
+
+    #st.write(user_template.replace("{{MSG}}", "hello robot"), unsafe_allow_html=True)
+    #st.write(bot_template.replace("{{MSG}}", "hello human"), unsafe_allow_html=True)
+
+    # "https://i.ibb.co/rdZC7LZ/Photo-logo-1.png"
+    # "https://huggingface.co/spaces/gli-mrunal/GPT_instruct_chatbot/blob/main/images/bot.jpg" 
+    # "https://huggingface.co/spaces/gli-mrunal/GPT_instruct_chatbot/blob/main/images/CSUN_Matadors_logo.svg.png"
+
+    with st.sidebar:
+        st.subheader("Your documents")
+
+        pdf_docs = st.file_uploader(
+            "Upload your PDfs here and click on 'Process'", accept_multiple_files=True)
+        if st.button("Process"):
+            with st.spinner("Processing"):
+                # ---------------   get pdf text  -------------------
+
+                raw_text = get_pdf_text(pdf_docs)
+                #st.write(raw_text)
+
+                # ----------   get the text chunks  -------------------------
+
+                text_chunks = get_text_chunks(raw_text)
+                #st.write(text_chunks)
+
+
+                # --------------   create vector store------------------------
+                # https://openai.com/pricing  --> Embedding Models
+                # Chose to use the best embedding model - intructor_xl ranked higher than OpenAi's embeddings from huggingface leaderboard
+                # https://huggingface.co/spaces/mteb/leaderboard
+
+                vectorstore = get_vectorstore(text_chunks)
+
+                # create conversation chain
+                st.session_state.conversation = get_conversation_chain(vectorstore)
+                #conversation = get_conversation_chain(vectorstore)
+
+    #st.session_state.conversation
+
+
+
+            
+
+if __name__ == '__main__':
+    main()