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import streamlit as st
from google import genai
from google.genai import types
from openai import OpenAI
# Show title and description.
st.title("💬 LSAT Tutor")
st.write(
"Hey there! I'm your tutor for today. We'll revise the LSAT Logical Reasoning Section."
)
# Ask user for their OpenAI API key via `st.text_input`.
# Alternatively, you can store the API key in `./.streamlit/secrets.toml` and access it
# via `st.secrets`, see https://docs.streamlit.io/develop/concepts/connections/secrets-management
# openai_api_key = st.text_input("OpenAI API Key", type="password")
GEMINI_API_KEY = "AIzaSyAjpHA08BUwLhK-tIlORxcB18RAp3541-M"
# Create a client.
client = genai.Client(api_key=GEMINI_API_KEY)
# Create a session state variable to store the chat messages. This ensures that the
# messages persist across reruns.
if "messages" not in st.session_state:
st.session_state.messages = []
# Display the existing chat messages via `st.chat_message`.
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Create a chat input field to allow the user to enter a message. This will display
# automatically at the bottom of the page.
if prompt := st.chat_input("Ready to begin?"):
# Store and display the current prompt.
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
# Generate a response using the OpenAI API.
# stream = client.chat.completions.create(
# model="gemini-2.0-flash",
# # config=types.GenerateContentConfig(
# # system_instruction=system_instruction,
# # tools=[tools]),
# messages=[
# {"role": m["role"], "content": m["content"]}
# for m in st.session_state.messages
# ],
# stream=True,
# )
stream = client.chats.create(model="gemini-2.0-flash",
# messages = [
# {"role": m["role"], "content": m["content"]}
# for m in st.session_state.messages
# ]
# config=types.GenerateContentConfig(
# system_instruction=system_instruction,
# tools=[tools]
# )
)
# Stream the response to the chat using `st.write_stream`, then store it in
# session state.
with st.chat_message("assistant"):
response = st.write_stream(stream.send_message(prompt))
st.session_state.messages.append({"role": "assistant", "content": response})
# stream = client.chat.completions.create(
# model="gpt-3.5-turbo",
# messages=[
# {"role": m["role"], "content": m["content"]}
# for m in st.session_state.messages
# ],
# stream=True,
# )
# import streamlit as st
# import random
# import time
# # Streamed response emulator
# def response_generator():
# response = random.choice(
# [
# "Hello there! How can I assist you today?",
# "Hi, human! Is there anything I can help you with?",
# "Hi there. Do you need help?",
# ]
# )
# for word in response.split():
# yield word + " "
# time.sleep(0.05)
# st.title("Simple chat")
# # Initialize chat history
# if "messages" not in st.session_state:
# st.session_state.messages = []
# # Display chat messages from history on app rerun
# for message in st.session_state.messages:
# with st.chat_message(message["role"]):
# st.markdown(message["content"])
# # Accept user input
# if prompt := st.chat_input("What is up?"):
# # Add user message to chat history
# st.session_state.messages.append({"role": "user", "content": prompt})
# # Display user message in chat message container
# with st.chat_message("user"):
# st.markdown(prompt)
# # Display assistant response in chat message container
# with st.chat_message("assistant"):
# response = st.write_stream(response_generator())
# # Add assistant response to chat history
# st.session_state.messages.append({"role": "assistant", "content": response})
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