Update app.py

app.py

@@ -7,102 +7,12 @@
 import streamlit as st
 from dotenv import load_dotenv
 
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-
-from InstructorEmbedding import INSTRUCTOR
-from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chat_models import ChatOpenAI
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from htmlTemplates import css, bot_template, user_template
-from langchain.llms import HuggingFaceHub
-
-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
-
-#  Chunk size and overlap must not exceed the models capacity!
-#
-def get_text_chunks(text):
-    text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size=800,    #  1000
-        chunk_overlap=200,
-        length_function=len
-    )
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-
-def get_vectorstore(text_chunks):
-    # embeddings = OpenAIEmbeddings()
-
-    #  pip install InstructorEmbedding
-    #  pip install sentence-transformers==2.2.2
-    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
-
-    #  from InstructorEmbedding import INSTRUCTOR
-    # model = INSTRUCTOR('hkunlp/instructor-xl')
-    # sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
-    # instruction = "Represent the Science title:"
-    # embeddings = model.encode([[instruction, sentence]])
-
-    # embeddings = model.encode(text_chunks)
-    print('have Embeddings:   ')
-
-    # text_chunks="this is a test"
-    #   FAISS,  Chroma and other vector databases
-    #
-    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-    print('FAISS succeeds:   ')
-
-    return vectorstore
-
-def get_conversation_chain(vectorstore):
-    # llm = ChatOpenAI()
-    #  llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
-    #  google/bigbird-roberta-base     facebook/bart-large
-    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.5, "max_length": 512})
-
-    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})
-    # response = st.session_state.conversation({'summarization': user_question})
-    st.session_state.chat_history = response['chat_history']
-
-
-    # st.empty()
-
-    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()
+    # load_dotenv()
     st.set_page_config(page_title="MLP Chat with multiple PDFs",
                        page_icon=":books:")
 
@@ -116,8 +26,8 @@ def main():
     st.header("Mike's PDF Chat :books:")
 
     user_question = st.text_input("Ask a question about your documents:")
-    if user_question:
-        handle_userinput(user_question)
+    # if user_question:
+    #     handle_userinput(user_question)
 
     # st.write( user_template, unsafe_allow_html=True)
     # st.write(user_template.replace( "{{MSG}}", "Hello robot!"), unsafe_allow_html=True)