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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Toastmasters-CC-Manual.pdf filter=lfs diff=lfs merge=lfs -text

Toastmasters-CC-Manual.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ec9fdec57c22ead344bac5319e0097990f20df8660285b75256c81427817ace8
+size 5989158

app.py

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+import streamlit as st
+import openai
+import os
+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
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+from htmlTemplates import css, bot_template, user_template
+from PIL import Image
+
+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
+
+# documentation for CharacterTextSplitter:
+# https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html
+def get_text_chunk(text):
+    text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size = 1000,
+        chunk_overlap = 200,
+        length_function = len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+#embedding using openAI embedding. Warn: This will cost you money
+def get_vectorstore_openAI(text_chunks):
+    embeddings = OpenAIEmbeddings()
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+#embedding using instructor-xl with your local machine for free
+#you can find more details at: https://huggingface.co/hkunlp/instructor-xl
+def get_vectorstore(text_chunks):
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+def get_conversation_chain(vectorstore):
+    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):
+    response = st.session_state.conversation({'question': user_question})
+    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 openai api_key from .evn
+    load_dotenv()
+    #openai.api_key = os.getenv("OPENAI_API_KEY")
+    
+    ##############################################################################
+    #set up basic page
+    st.set_page_config(page_title="Chat With multiple PDFs", page_icon=":books:")
+    st.write(css, unsafe_allow_html=True)
+
+    #initial session_state in order to avoid refresh
+    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 based on PDF you provided :books:")
+    user_question = st.text_input("Ask a question about your documents:")
+
+    if user_question:
+        handle_userinput(user_question)
+
+# Define the templates
+
+    with st.sidebar:
+        st.subheader("Your PDF documents")
+        pdf_docs = st.file_uploader("Upload your pdfs here and click on 'Proces'", accept_multiple_files= True)
+        #if the button is pressed
+        if st.button("Process"):
+            with st.spinner("Processing"):
+                #get pdf text
+                raw_text = get_pdf_text(pdf_docs)
+                print('raw_text is created')
+
+                #get the text chunks
+                text_chunks = get_text_chunk(raw_text)
+                print('text_chunks are generated')
+
+                #create vector store
+                vectorstore = get_vectorstore_openAI(text_chunks)
+                print('vectorstore is created')
+
+                #create converstion chain
+                st.session_state.conversation = get_conversation_chain(vectorstore)
+                print('conversation chain created')
+    
+
+
+
+# to run this application, you need to run "streamlit run app.py"
+if __name__ == '__main__':
+    main()

requirements.txt

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+streamlit==1.23.1
+PyPDF2==3.0.1
+langchain==0.0.197
+openai==0.27.8
+python-dotenv==1.0.0
+faiss-cpu==1.7.4
+huggingface-hub==0.15.1
+InstructorEmbedding==1.0.1
+sentence-transformers==2.2.2
+tiktoken==0.4.0