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import streamlit as st | |
import os | |
import pandas as pd | |
import openai | |
# from openai import OpenAI | |
from llama_index.llms import OpenAI | |
from llama_index import SimpleDirectoryReader | |
from llama_index import Document | |
from llama_index import VectorStoreIndex | |
from llama_index import ServiceContext | |
from llama_index.embeddings import HuggingFaceEmbedding | |
from llama_index.memory import ChatMemoryBuffer | |
import pkg_resources | |
import shutil | |
import main | |
### To trigger trulens evaluation | |
main.main() | |
### Finally, start streamlit app | |
leaderboard_path = pkg_resources.resource_filename( | |
"trulens_eval", "Leaderboard.py" | |
) | |
evaluation_path = pkg_resources.resource_filename( | |
"trulens_eval", "pages/Evaluations.py" | |
) | |
ux_path = pkg_resources.resource_filename( | |
"trulens_eval", "ux" | |
) | |
os.makedirs("./pages", exist_ok=True) | |
shutil.copyfile(leaderboard_path, os.path.join("./pages", "1_Leaderboard.py")) | |
shutil.copyfile(evaluation_path, os.path.join("./pages", "2_Evaluations.py")) | |
if os.path.exists("./ux"): | |
shutil.rmtree("./ux") | |
shutil.copytree(ux_path, "./ux") | |
# App title | |
st.set_page_config(page_title="π¬ Open AI Chatbot") | |
openai_api = os.getenv("OPENAI_API_KEY") | |
# "./raw_documents/HI_Knowledge_Base.pdf" | |
input_files = ["./raw_documents/HI Chapter Summary Version 1.3.pdf"] | |
embedding_model = "BAAI/bge-small-en-v1.5" | |
system_content = ("You are a helpful study assistant. " | |
"You do not respond as 'User' or pretend to be 'User'. " | |
"You only respond once as 'Assistant'." | |
) | |
data_df = pd.DataFrame( | |
{ | |
"Completion": [30, 40, 100, 10], | |
} | |
) | |
data_df.index = ["Chapter 1", "Chapter 2", "Chapter 3", "Chapter 4"] | |
# Replicate Credentials | |
with st.sidebar: | |
st.title("π¬ Open AI Chatbot") | |
st.write("This chatbot is created using the GPT model from Open AI.") | |
if openai_api: | |
pass | |
elif "OPENAI_API_KEY" in st.secrets: | |
st.success("API key already provided!", icon="β ") | |
openai_api = st.secrets["OPENAI_API_KEY"] | |
else: | |
openai_api = st.text_input("Enter OpenAI API token:", type="password") | |
if not (openai_api.startswith("sk-") and len(openai_api)==51): | |
st.warning("Please enter your credentials!", icon="β οΈ") | |
else: | |
st.success("Proceed to entering your prompt message!", icon="π") | |
### for streamlit purpose | |
os.environ["OPENAI_API_KEY"] = openai_api | |
st.subheader("Models and parameters") | |
selected_model = st.sidebar.selectbox("Choose an OpenAI model", | |
["gpt-3.5-turbo-1106", "gpt-4-1106-preview"], | |
key="selected_model") | |
temperature = st.sidebar.slider("temperature", min_value=0.01, max_value=2.0, | |
value=0.1, step=0.01) | |
st.data_editor( | |
data_df, | |
column_config={ | |
"Completion": st.column_config.ProgressColumn( | |
"Completion %", | |
help="Percentage of content covered", | |
format="%.1f%%", | |
min_value=0, | |
max_value=100, | |
), | |
}, | |
hide_index=False, | |
) | |
st.markdown("π Reach out to SakiMilo to learn how to create this app!") | |
if "init" not in st.session_state.keys(): | |
st.session_state.init = {"warm_start": "No"} | |
# Store LLM generated responses | |
if "messages" not in st.session_state.keys(): | |
st.session_state.messages = [{"role": "assistant", | |
"content": "How may I assist you today?"}] | |
# Display or clear chat messages | |
for message in st.session_state.messages: | |
with st.chat_message(message["role"]): | |
st.write(message["content"]) | |
def clear_chat_history(): | |
st.session_state.messages = [{"role": "assistant", | |
"content": "How may I assist you today?"}] | |
chat_engine = get_query_engine(input_files=input_files, | |
llm_model=selected_model, | |
temperature=temperature, | |
embedding_model=embedding_model, | |
system_content=system_content) | |
chat_engine.reset() | |
st.sidebar.button("Clear Chat History", on_click=clear_chat_history) | |
def get_document_object(input_files): | |
documents = SimpleDirectoryReader(input_files=input_files).load_data() | |
document = Document(text="\n\n".join([doc.text for doc in documents])) | |
return document | |
def get_llm_object(selected_model, temperature): | |
llm = OpenAI(model=selected_model, temperature=temperature) | |
return llm | |
def get_embedding_model(model_name): | |
embed_model = HuggingFaceEmbedding(model_name=model_name) | |
return embed_model | |
def get_query_engine(input_files, llm_model, temperature, | |
embedding_model, system_content): | |
document = get_document_object(input_files) | |
llm = get_llm_object(llm_model, temperature) | |
embedded_model = get_embedding_model(embedding_model) | |
service_context = ServiceContext.from_defaults(llm=llm, embed_model=embedded_model) | |
index = VectorStoreIndex.from_documents([document], service_context=service_context) | |
memory = ChatMemoryBuffer.from_defaults(token_limit=15000) | |
# chat_engine = index.as_query_engine(streaming=True) | |
chat_engine = index.as_chat_engine( | |
chat_mode="context", | |
memory=memory, | |
system_prompt=system_content | |
) | |
return chat_engine | |
def generate_llm_response(prompt_input): | |
chat_engine = get_query_engine(input_files=input_files, | |
llm_model=selected_model, | |
temperature=temperature, | |
embedding_model=embedding_model, | |
system_content=system_content) | |
# st.session_state.messages | |
response = chat_engine.stream_chat(prompt_input) | |
return response | |
# Warm start | |
if st.session_state.init["warm_start"] == "No": | |
clear_chat_history() | |
st.session_state.init["warm_start"] = "Yes" | |
# User-provided prompt | |
if prompt := st.chat_input(disabled=not openai_api): | |
client = OpenAI() | |
st.session_state.messages.append({"role": "user", "content": prompt}) | |
with st.chat_message("user"): | |
st.write(prompt) | |
# Generate a new response if last message is not from assistant | |
if st.session_state.messages[-1]["role"] != "assistant": | |
with st.chat_message("assistant"): | |
with st.spinner("Thinking..."): | |
# response = generate_llm_response(client, prompt) | |
response = generate_llm_response(prompt) | |
placeholder = st.empty() | |
full_response = "" | |
for token in response.response_gen: | |
full_response += token | |
placeholder.markdown(full_response) | |
placeholder.markdown(full_response) | |
message = {"role": "assistant", "content": full_response} | |
st.session_state.messages.append(message) |