app.py

# import streamlit as st
# from langchain.callbacks import StreamlitCallbackHandler
# import streamlit as st
# from langchain.llms import OpenAI
# from langchain.agents import AgentType, initialize_agent, load_tools
# from langchain.callbacks import StreamlitCallbackHandler
# import streamlit as st

# from scraping import prompty

# import os


# st.set_page_config(page_title="NutriMentor", page_icon=":robot:")
# st.header("NutriMentor")

# from langchain.chat_models import ChatOpenAI
# from langchain.chains.question_answering import load_qa_chain


    

# from langchain.chains import RetrievalQA
# from langchain.llms import OpenAI
# from langchain.document_loaders import TextLoader
# from langchain.document_loaders import PyPDFLoader
# from langchain.indexes import VectorstoreIndexCreator
# from langchain.text_splitter import CharacterTextSplitter
# from langchain.embeddings import OpenAIEmbeddings
# from langchain.vectorstores import Chroma
# from langchain.chains.question_answering import load_qa_chain

# if "generated" not in st.session_state:
#   st.session_state["generated"] = []

# if "past" not in st.session_state:
#   st.session_state["past"] = []

# if "messages" not in st.session_state:
#   st.session_state["messages"] = []

# init_alr = False
# def init_model():
#     os.environ["OPENAI_API_KEY"] = "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"
#     llm = ChatOpenAI(
#       openai_api_key=os.environ.get("OPENAI_API_KEY"),
#       model='gpt-3.5-turbo-16k',
#       temperature=0,
#       streaming=True
#     )
#     # load document
#     loader = PyPDFLoader("./Dietary_Guidelines_for_Americans_2020-2025.pdf")
#     documents = loader.load()
#     # split the documents into chunks
#     text_splitter = CharacterTextSplitter(chunk_size=10000, chunk_overlap=0)
#     texts = text_splitter.split_documents(documents)
#     # select which embeddings we want to use
#     embeddings = OpenAIEmbeddings()
#     # create the vectorestore to use as the index
#     db = Chroma.from_documents(texts, embeddings)
#     # expose this index in a retriever interface
#     retriever = db.as_retriever(search_type="similarity", search_kwargs={"k":2})
#     # create a chain to answer questions
#     qa = RetrievalQA.from_chain_type(
#         llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=False)
#     return qa





# st_callback = StreamlitCallbackHandler(st.container())



# # date_input = st.text_input(
# #         "Enter Date (ex. 10-11) 👇",
# #         label_visibility=st.session_state.visibility,
# #         disabled=st.session_state.disabled,
# #         placeholder=st.session_state.placeholder,
# #     )

# date_input = st.text_input(label = "Enter Date (ex. 10-11) 👇" )


# if prompt := st.chat_input():
#     st.chat_message("user").write(prompt)
#     with st.chat_message("assistant"):
#         #st_callback = StreamlitCallbackHandler(st.container())

#         if init_alr == False:
#             init_alr = True
            
#             qa = init_model()
#             #Call query generator with text_input
#             query = prompty(date_input, prompt)
#             result = qa({"query": query})
    
#             st.write(result['result'])
#             st.stop()