app.py

import streamlit as st
import langchain
from langchain.document_loaders import OnlinePDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
import pinecone

st.sidebar.markdown(" # Welcome to Ztudy ")

# ------------------------ PDF ------------------------
# Hard-coded PDFs (TODO: make this dynamic from Google Drive)
pdf_dict = {}
pdf_dict["Field Guide to Data Science"] = "https://wolfpaulus.com/wp-content/uploads/2017/05/field-guide-to-data-science.pdf"
pdf_dict["2023 GPT-4 Technical Report"] = "https://cdn.openai.com/papers/gpt-4.pdf"
pdf_dict["Administering Data Centers"] = "https://drive.google.com/file/d/1r3bqHq-ZszXnX6UJLOaeoEEa1plUYXZu"
pdf_dict["First Aid Reference Guide (Google)"] = "https://drive.google.com/file/d/1fzN2wa_uJ8INUYim88eCymSvJdyDT2fz/"
pdf_dict["First Aid Reference Guide (Public)"] = "https://www.sja.ca/sites/default/files/2021-05/First%20aid%20reference%20guide_V4.1_Public.pdf"
pdf_dict["Astronomy 2106"] = "https://drive.google.com/file/d/1XXmjMLENP90-eXEqOaTxQ8O56ZwExsVT"
pdf_dict["Astronomy 2106 (New)"] = "https://drive.google.com/file/d/1w1S-TY2PzeJ9mjPVb1yLwcYh5EI44oP7"
pdf_dict["Learning Deep Learning: Chapter 1"] = "https://drive.google.com/file/d/1o7feaKFzXd5-95GffZyynAwY_fzGafhr/view?usp=sharing"

# -------------------- Globals ------------------------
texts = None
pinecone_index = "group-1"

if 'exchanges' not in st.session_state:
    st.session_state.exchanges = []
if 'temperature' not in st.session_state:
    st.session_state.temperature = 0.5

# -------------------- Functions -----------------------
def console_log(msg):
    st.sidebar.write(msg)

def init_pinecone():
    pinecone.init(
        api_key=st.secrets["PINECONE_API_KEY"],  # find at app.pinecone.io
        environment=st.secrets["PINECONE_API_ENV"]  # next to api key in console
    ) 
    return
 
def load_vector_database():
    embeddings = OpenAIEmbeddings(openai_api_key=st.secrets["OPENAI_API_KEY"])
    init_pinecone()
    print(f"Number of vectors: {len(texts)} to be upserted to Index: {pinecone_index}")
    Pinecone.from_texts([t.page_content for t in texts], embeddings, index_name=pinecone_index)

def load_pdf(url):
    console_log(f"Loading {url}")
    loader = OnlinePDFLoader(url)
    data = loader.load()
    console_log(f'You have {len(data)} document(s) in your data')
    console_log(f'There are {len(data[0].page_content)} characters in your document')
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    global texts 
    texts = text_splitter.split_documents(data)
    console_log(f'After splitting, you have {len(texts)} documents')
    load_vector_database()

def chat(query, temperature):

    from langchain.llms import OpenAI
    from langchain.chains.question_answering import load_qa_chain

    llm = OpenAI(temperature=temperature, openai_api_key=st.secrets["OPENAI_API_KEY"])
    chain = load_qa_chain(llm, chain_type="stuff")
   
    embeddings = OpenAIEmbeddings(openai_api_key=st.secrets["OPENAI_API_KEY"])
    init_pinecone()
    vector_store = Pinecone.from_existing_index(pinecone_index, embeddings)
    docs = vector_store.similarity_search(query, include_metadata=True)

    # Comment/Uncomment to hide/show trace of documents
    with st.expander("See documents for embedding"):
        for i in range(len(docs)):
            st.write(docs[i])
    
    return chain.run(input_documents=docs, question=query)

def format_exchanges(exchanges):
    for i in range(len(exchanges)):
        if exchanges[i]["role"] == "user":
            icon, text, blank = st.columns([1,8,1])
        elif exchanges[i]["role"] == "assistant":
            blank, text, icon = st.columns([1,8,1])
        else:
            st.markdown("*" + exchanges[i]["role"] + ":* " + exchanges[i]["content"]) 
            continue
        
        with icon:
            st.image("icon_" + exchanges[i]["role"] + ".png", width=50)
        with text:
            st.markdown(exchanges[i]["content"])
        st.markdown("""---""")

def format_prompt(exchanges):
    # Include the last 6 exchanges
    prompt = ""
    for i in range( max(len(exchanges)-7,0), len(exchanges)):
        prompt += "[Q]" if (exchanges[i]["role"] == "user") else "[A]"
        prompt += ": " + exchanges[i]["content"] + "\n"
    with st.expander("See prompt sent to LLM"):
        st.write(prompt)
    return prompt

# ------------------------ Load PDF ------------------------
with st.sidebar:
    option = st.selectbox("Select a PDF", list(pdf_dict.keys()), key="pdf", on_change=None)
    st.markdown(f"*Selected*: {option}") 
    st.button('Click to start loading PDF', key="load_pdf", on_click=load_pdf, args=[pdf_dict[option]])

# ------------------------ Chatbot ------------------------
st.slider("Temperature (0 = Most Deterministic)", min_value=0.0, max_value=1.0, step=0.1, key="temperature")
st.text_input("Prompt", placeholder="Ask me anything", key="prompt")

if st.session_state.prompt:
    st.session_state.exchanges.append({"role": "user", "content": st.session_state.prompt})
    try:
        response = chat(format_prompt(st.session_state.exchanges), st.session_state.temperature)
    except Exception as e:
        st.error(e) 
        st.stop()
    st.session_state.exchanges.append({"role": "assistant", "content": response})
    format_exchanges(st.session_state.exchanges)