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import time
import os
import joblib
import streamlit as st
from utils.questions_dataset import system_instruction, get_model_tools
from google.genai import types
from google import genai
# GOOGLE_API_KEY=os.environ.get('GOOGLE_API_KEY')
GEMINI_API_KEY = "AIzaSyAjpHA08BUwLhK-tIlORxcB18RAp3541-M"
client = genai.Client(api_key=GEMINI_API_KEY)
new_chat_id = f'{time.time()}'
MODEL_ROLE = 'ai'
AI_AVATAR_ICON = '✨'
# Create a data/ folder if it doesn't already exist
try:
os.mkdir('data/')
except:
# data/ folder already exists
pass
# Load past chats (if available)
try:
past_chats: dict = joblib.load('data/past_chats_list')
except:
past_chats = {}
# Sidebar allows a list of past chats
with st.sidebar:
st.write('# Past Chats')
if st.session_state.get('chat_id') is None:
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:
# This will happen the first time AI response comes in
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='_',
)
# Save new chats after a message has been sent to AI
st.session_state.chat_title = f'ChatSession-{st.session_state.chat_id}'
st.write('# Chat with LSAT Tutor')
# Chat history (allows to ask multiple questions)
try:
st.session_state.messages = joblib.load(
f'data/{st.session_state.chat_id}-st_messages'
)
st.session_state.gemini_history = joblib.load(
f'data/{st.session_state.chat_id}-gemini_messages'
)
except:
st.session_state.messages = []
st.session_state.gemini_history = []
print('new_cache made')
st.session_state.chat = client.chats.create(model='gemini-2.0-flash',
config=types.GenerateContentConfig(
tools=[get_model_tools()],
system_instruction=system_instruction),
history=st.session_state.gemini_history
)
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(
name=message['role'],
avatar=message.get('avatar'),
):
st.markdown(message['content'])
# React to user input
if prompt := st.chat_input('Your message here...'):
# Save this as a chat for later
if st.session_state.chat_id not in past_chats.keys():
past_chats[st.session_state.chat_id] = st.session_state.chat_title
joblib.dump(past_chats, 'data/past_chats_list')
# Display user message in chat message container
with st.chat_message('user'):
st.markdown(prompt)
# Add user message to chat history
st.session_state.messages.append(
dict(
role='user',
content=prompt,
)
)
## Send message to AI
response = st.session_state.chat.send_message_stream(
prompt,
)
# Display assistant response in chat message container
with st.chat_message(
name=MODEL_ROLE,
avatar=AI_AVATAR_ICON,
):
message_placeholder = st.empty()
full_response = ''
assistant_response = response
# Streams in a chunk at a time
for chunk in response:
# Simulate stream of chunk
if chunk.text == None:
full_response = "No response!! Report to admin!"
for ch in chunk.text.split(' '):
full_response += ch + ' '
time.sleep(0.05)
# Rewrites with a cursor at end
message_placeholder.write(full_response + '▌')
# Write full message with placeholder
message_placeholder.write(full_response)
# Add assistant response to chat history
st.session_state.messages.append(
dict(
role=MODEL_ROLE,
content=full_response,
avatar=AI_AVATAR_ICON,
)
)
st.session_state.gemini_history = st.session_state.chat.get_history()
# Save to file
joblib.dump(
st.session_state.messages,
f'data/{st.session_state.chat_id}-st_messages',
)
joblib.dump(
st.session_state.gemini_history,
f'data/{st.session_state.chat_id}-gemini_messages',
)
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