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from omegaconf import OmegaConf
import streamlit as st
import os
from PIL import Image
import re
from translate import Translator
from llama_index.indices.managed.vectara import VectaraIndex
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.utils import print_text
learning_styles = ['traditional', 'inquiry based']
languages = {'English': 'en', 'Spanish': 'es', 'French': 'fr', 'German': 'de', 'Arabic': 'ar', 'Chinese': 'zh-cn',
'Hebrew': 'he', 'Hindi': 'hi', 'Italian': 'it', 'Japanese': 'ja', 'Korean': 'ko', 'Portuguese': 'pt'}
initial_prompt = "How can I help you today?"
def launch_bot():
def reset():
cfg = st.session_state.cfg
llm = OpenAI(model="gpt-4o", temperature=0)
tr_prompt = Translator(to_lang=languages[cfg.language]).translate(initial_prompt)
print(tr_prompt)
st.session_state.messages = [{"role": "assistant", "content": tr_prompt, "avatar": "🦖"}]
vectara = VectaraIndex(vectara_api_key=cfg.api_key,
vectara_customer_id=cfg.customer_id,
vectara_corpus_id=cfg.corpus_id)
vectara_tool = QueryEngineTool(
query_engine = vectara.as_query_engine(summary_enabled=True, summary_num_results=5, summary_response_lang = languages[cfg.language],
summary_prompt_name="vectara-summary-ext-24-05-large"),
metadata = ToolMetadata(name="Vectara",
description="Vectara Query Engine that is able to answer any questions about the Justice Harvard class."),
)
llm = OpenAI(model="gpt-4o", temperature=0)
st.session_state.agent = ReActAgent.from_tools(
tools=[vectara_tool], llm=llm,
context = f'''
You are a teacher assistant at Justice Harvard course. You are helping a student with his questions.
The student is student who is {cfg.student_age} years old, you personalize your assistance to the student's age,
and rephrase your answer if needed to fit the {cfg.style} learning style.
''',
verbose=True
)
if 'cfg' not in st.session_state:
cfg = OmegaConf.create({
'customer_id': str(os.environ['VECTARA_CUSTOMER_ID']),
'corpus_id': str(os.environ['VECTARA_CORPUS_ID']),
'api_key': str(os.environ['VECTARA_API_KEY']),
'style': learning_styles[0],
'language': 'English',
'student_age': 21
})
st.session_state.cfg = cfg
st.session_state.style = learning_styles[0]
st.session_state.language = 'English'
st.session_state.student_age = 21
reset()
cfg = st.session_state.cfg
st.set_page_config(page_title="Teaching Assistant", layout="wide")
# left side content
with st.sidebar:
image = Image.open('Vectara-logo.png')
st.image(image, width=250)
st.markdown(f"## Welcome to Justice Harvard.\n\n\n")
st.markdown("\n")
cfg.style = st.selectbox('Learning Style:', learning_styles)
if st.session_state.style != cfg.style:
st.session_state.style = cfg.style
reset()
st.markdown("\n")
cfg.language = st.selectbox('Language:', languages.keys())
if st.session_state.language != cfg.language:
st.session_state.langage = cfg.language
reset()
st.markdown("\n")
cfg.student_age = st.number_input(
'Student age:', min_value=13, value=cfg.student_age,
step=1, format='%i'
)
if st.session_state.student_age != cfg.student_age:
st.session_state.student_age = cfg.student_age
reset()
st.markdown("\n\n")
if st.button('Start Over'):
reset()
st.markdown("---")
st.markdown(
"## How this works?\n"
"This app was built with [Vectara](https://vectara.com).\n\n"
"It demonstrates the use of Agentic Chat functionality with Vectara"
)
st.markdown("---")
if "messages" not in st.session_state.keys():
reset()
# Display chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"], avatar=message["avatar"]):
st.write(message["content"])
# User-provided prompt
if prompt := st.chat_input():
st.session_state.messages.append({"role": "user", "content": prompt, "avatar": '🧑💻'})
with st.chat_message("user", avatar='🧑💻'):
print_text(f"Starting new question: {prompt}\n", color='green')
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", avatar='🤖'):
with st.spinner('Thinking...'):
res = st.session_state.agent.chat(prompt)
cleaned = re.sub(r'\[\d+\]', '', res.response)
st.write(cleaned)
message = {"role": "assistant", "content": cleaned, "avatar": '🤖'}
st.session_state.messages.append(message)
if __name__ == "__main__":
launch_bot()
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