217 lines
8.3 KiB
Python
217 lines
8.3 KiB
Python
import time
|
||
import os
|
||
import joblib
|
||
import streamlit as st
|
||
import google.generativeai as genai
|
||
from dotenv import load_dotenv
|
||
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext, Document
|
||
from llama_index.llms.openai import OpenAI
|
||
import openai
|
||
from pathlib import Path
|
||
|
||
# Load environment variables
|
||
load_dotenv()
|
||
GOOGLE_API_KEY = os.environ.get('GOOGLE_API_KEY')
|
||
genai.configure(api_key=os.environ.get('GEMINI_API_KEY'))
|
||
load_dotenv(Path("../../.env"))
|
||
openai.api_key = os.getenv("OPENAI_API_KEY")
|
||
|
||
# Constants
|
||
MODEL_ROLE = 'ai'
|
||
AI_AVATAR_ICON = '👄'
|
||
DATA_DIR = 'data/'
|
||
|
||
|
||
def initialize_session_state():
|
||
"""Initialize the chat message history in session state."""
|
||
if "messages" not in st.session_state:
|
||
st.session_state.messages = [
|
||
{"role": "assistant", "content": "Ask me a question about documents from your local files or from the Web."}
|
||
]
|
||
|
||
|
||
@st.cache_resource(show_spinner=False)
|
||
def load_data(input_dir):
|
||
"""Load and index documents from the specified directory."""
|
||
with st.spinner("Loading and indexing your docs – hang tight! This should take 1-2 minutes."):
|
||
reader = SimpleDirectoryReader(input_dir=input_dir, recursive=True)
|
||
docs = reader.load_data()
|
||
service_context = ServiceContext.from_defaults(
|
||
llm=OpenAI(
|
||
model="gpt-3.5-turbo",
|
||
temperature=0.5,
|
||
system_prompt=(
|
||
"You are an expert on content & digital marketing and your job is to answer technical questions."
|
||
"Assume that all questions are related to provided documents, as context."
|
||
"Keep your answers technical and based on facts – do not hallucinate features."
|
||
)
|
||
)
|
||
)
|
||
index = VectorStoreIndex.from_documents(docs, service_context=service_context)
|
||
return index
|
||
|
||
|
||
def display_chat_history():
|
||
"""Display the chat message history."""
|
||
for message in st.session_state.messages:
|
||
with st.chat_message(message["role"]):
|
||
st.write(message["content"])
|
||
|
||
|
||
def generate_response(prompt, chat_engine):
|
||
"""Generate a response from the chat engine and update the chat history."""
|
||
if prompt:
|
||
st.session_state.messages.append({"role": "user", "content": prompt})
|
||
|
||
with st.chat_message("assistant"):
|
||
with st.spinner("Thinking..."):
|
||
response = chat_engine.chat(prompt)
|
||
st.write(response.response)
|
||
st.session_state.messages.append({"role": "assistant", "content": response.response})
|
||
|
||
|
||
def history_chatbot():
|
||
"""Main function to run the Streamlit app with history chat functionality."""
|
||
# Ensure the data/ directory exists
|
||
os.makedirs(DATA_DIR, exist_ok=True)
|
||
|
||
# Generate a new chat ID
|
||
new_chat_id = f'{time.time()}'
|
||
|
||
# Load past chats if available
|
||
try:
|
||
past_chats = joblib.load(os.path.join(DATA_DIR, 'past_chats_list'))
|
||
except FileNotFoundError:
|
||
past_chats = {}
|
||
|
||
# Sidebar for past chats
|
||
with st.sidebar:
|
||
st.write('# Past Chats')
|
||
if 'chat_id' not in st.session_state:
|
||
st.session_state.chat_id = st.selectbox(
|
||
label='Pick a past chat',
|
||
options=[new_chat_id] + list(past_chats.keys()),
|
||
format_func=lambda x: past_chats.get(x, 'New Chat'),
|
||
placeholder='_'
|
||
)
|
||
else:
|
||
st.session_state.chat_id = st.selectbox(
|
||
label='Pick a past chat',
|
||
options=[new_chat_id, st.session_state.chat_id] + list(past_chats.keys()),
|
||
index=1,
|
||
format_func=lambda x: past_chats.get(x, 'New Chat' if x != st.session_state.chat_id else st.session_state.chat_title),
|
||
placeholder='_'
|
||
)
|
||
st.session_state.chat_title = f'ChatSession-{st.session_state.chat_id}'
|
||
|
||
# Load chat history if available
|
||
try:
|
||
st.session_state.messages = joblib.load(os.path.join(DATA_DIR, f'{st.session_state.chat_id}-st_messages'))
|
||
st.session_state.gemini_history = joblib.load(os.path.join(DATA_DIR, f'{st.session_state.chat_id}-gemini_messages'))
|
||
print('Loaded existing chat history')
|
||
except FileNotFoundError:
|
||
st.session_state.messages = []
|
||
st.session_state.gemini_history = []
|
||
print('Initialized new chat history')
|
||
|
||
# Configure the AI model
|
||
st.session_state.model = genai.GenerativeModel('gemini-pro')
|
||
st.session_state.chat = st.session_state.model.start_chat(history=st.session_state.gemini_history)
|
||
|
||
# Display past messages
|
||
for message in st.session_state.messages:
|
||
with st.chat_message(name=message['role'], avatar=message.get('avatar')):
|
||
st.markdown(message['content'])
|
||
|
||
# Handle user input
|
||
if prompt := st.chat_input('Ask Alwrity...'):
|
||
if st.session_state.chat_id not in past_chats:
|
||
past_chats[st.session_state.chat_id] = st.session_state.chat_title
|
||
joblib.dump(past_chats, os.path.join(DATA_DIR, 'past_chats_list'))
|
||
|
||
# Display and save user message
|
||
with st.chat_message('user'):
|
||
st.markdown(prompt)
|
||
st.session_state.messages.append({'role': 'user', 'content': prompt})
|
||
|
||
# Send message to AI and stream the response
|
||
response = st.session_state.chat.send_message(prompt, stream=True)
|
||
full_response = ''
|
||
with st.chat_message(name=MODEL_ROLE, avatar=AI_AVATAR_ICON):
|
||
message_placeholder = st.empty()
|
||
for chunk in response:
|
||
for ch in chunk.text.split(' '):
|
||
full_response += ch + ' '
|
||
time.sleep(0.05)
|
||
message_placeholder.write(full_response + '▌')
|
||
message_placeholder.write(full_response)
|
||
|
||
# Save the AI response
|
||
st.session_state.messages.append({
|
||
'role': MODEL_ROLE,
|
||
'content': full_response,
|
||
'avatar': AI_AVATAR_ICON
|
||
})
|
||
st.session_state.gemini_history = st.session_state.chat.history
|
||
|
||
# Persist chat history to disk
|
||
joblib.dump(st.session_state.messages, os.path.join(DATA_DIR, f'{st.session_state.chat_id}-st_messages'))
|
||
joblib.dump(st.session_state.gemini_history, os.path.join(DATA_DIR, f'{st.session_state.chat_id}-gemini_messages'))
|
||
|
||
|
||
def alwrity_chat_docqa():
|
||
"""Main function to run the Streamlit app with document question answering functionality."""
|
||
st.header("Ask Alwrity 💬 📚")
|
||
initialize_session_state()
|
||
option = st.radio(
|
||
"Choose Data Source To Ask From:",
|
||
("Ask Your Local Docs", "Ask Your PDFs", "Ask Your Videos", "Ask Your Audio Files")
|
||
)
|
||
|
||
if option == "Ask Your Local Docs":
|
||
input_dir = st.text_input("Enter the path to the folder:")
|
||
if input_dir:
|
||
st.session_state.input_dir = input_dir
|
||
|
||
elif option == "Ask Your PDFs":
|
||
pdf_file = st.file_uploader("Upload a PDF file or enter a URL:", type=["pdf"])
|
||
if pdf_file:
|
||
st.session_state.input_file = pdf_file
|
||
|
||
elif option == "Ask Your Videos":
|
||
video_dir = st.text_input("Enter the path to the video folder:")
|
||
if video_dir:
|
||
st.session_state.input_dir = video_dir
|
||
|
||
elif option == "Ask Your Audio Files":
|
||
audio_dir = st.text_input("Enter the path to the audio folder:")
|
||
if audio_dir:
|
||
st.session_state.input_dir = audio_dir
|
||
|
||
if 'input_dir' in st.session_state:
|
||
index = load_data(st.session_state.input_dir)
|
||
chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)
|
||
display_chat_history()
|
||
prompt = st.chat_input("Your question")
|
||
if st.session_state.messages[-1]["role"] != "assistant":
|
||
generate_response(prompt, chat_engine)
|
||
|
||
elif 'input_file' in st.session_state:
|
||
# Handle PDF file or URL input here
|
||
st.write("Handling PDF file or URL input is not implemented yet.")
|
||
|
||
|
||
def alwrity_rag_chatbot():
|
||
"""Main function to run the combined Streamlit app."""
|
||
st.sidebar.title("Alwrity RAG Chatbot")
|
||
app_mode = st.sidebar.selectbox("Choose mode", ["History Chatbot", "Document QA Chatbot"])
|
||
|
||
if app_mode == "History Chatbot":
|
||
history_chatbot()
|
||
elif app_mode == "Document QA Chatbot":
|
||
alwrity_chat_docqa()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
alwrity_rag_chatbot()
|