Spaces:
Running
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initial
Browse files- README.md +5 -4
- Vectara-logo.png +0 -0
- app.py +135 -0
- requirements.txt +6 -0
README.md
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---
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title: Justice Harvard
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sdk: streamlit
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sdk_version: 1.
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app_file: app.py
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pinned: false
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license: apache-2.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Justice Harvard
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emoji: π¨
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colorFrom: indigo
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colorTo: indigo
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sdk: streamlit
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sdk_version: 1.32.2
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app_file: app.py
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pinned: false
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license: apache-2.0
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short_description: Teacher Assistant - Justice Harvard
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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Vectara-logo.png
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app.py
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from omegaconf import OmegaConf
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from query import VectaraQuery
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import streamlit as st
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import os
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from PIL import Image
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import re
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from translate import Translator
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from llama_index.indices.managed.vectara import VectaraIndex
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from llama_index.core.agent import ReActAgent
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from llama_index.llms.openai import OpenAI
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from llama_index.core.tools import QueryEngineTool, ToolMetadata
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from llama_index.core.utils import print_text
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learning_styles = ['traditional', 'inquiry based']
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languages = {'English': 'en', 'Spanish': 'es', 'French': 'fr', 'German': 'de', 'Arabic': 'ar', 'Chinese': 'zh-cn',
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'Hebrew': 'he', 'Hindi': 'hi', 'Italian': 'it', 'Japanese': 'ja', 'Korean': 'ko', 'Portuguese': 'pt'}
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initial_prompt = "How can I help you today?"
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def launch_bot():
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def reset():
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cfg = st.session_state.cfg
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llm = OpenAI(model="gpt-4o", temperature=0)
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tr_prompt = Translator(to_lang=languages[cfg.language]).translate(initial_prompt)
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print(tr_prompt)
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st.session_state.messages = [{"role": "assistant", "content": tr_prompt, "avatar": "π¦"}]
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vectara = VectaraIndex(vectara_api_key=cfg.api_key,
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vectara_customer_id=cfg.customer_id,
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vectara_corpus_id=cfg.corpus_id)
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vectara_tool = QueryEngineTool(
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query_engine = vectara.as_query_engine(summary_enabled=True, summary_num_results=5, summary_response_lang = languages[cfg.language],
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summary_prompt_name="vectara-summary-ext-24-05-large"),
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metadata = ToolMetadata(name="Vectara",
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description="Vectara Query Engine that is able to answer any questions about the Justice Harvard class."),
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)
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llm = OpenAI(model="gpt-4o", temperature=0)
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st.session_state.agent = ReActAgent.from_tools(
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tools=[vectara_tool], llm=llm,
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context = f'''
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You are a teacher assistant at Justice Harvard course. You are helping a student with his questions.
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The student is student who is {cfg.student_age} years old, you personalize your assistance to the student's age,
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and rephrase your answer if needed to fit the {cfg.style} learning style.
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''',
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verbose=True
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)
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if 'cfg' not in st.session_state:
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cfg = OmegaConf.create({
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'customer_id': str(os.environ['VECTARA_CUSTOMER_ID']),
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'corpus_id': str(os.environ['VECTARA_CORPUS_ID']),
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'api_key': str(os.environ['VECTARA_API_KEY']),
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'style': learning_styles[0],
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'language': 'English',
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'student_age': 21
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})
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st.session_state.cfg = cfg
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st.session_state.style = learning_styles[0]
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st.session_state.language = 'English'
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st.session_state.student_age = 21
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reset()
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cfg = st.session_state.cfg
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st.set_page_config(page_title="Teaching Assistant", layout="wide")
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# left side content
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with st.sidebar:
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image = Image.open('Vectara-logo.png')
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st.image(image, width=250)
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st.markdown(f"## Welcome to Justice Harvard.\n\n\n")
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st.markdown("\n")
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cfg.style = st.selectbox('Learning Style:', learning_styles)
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if st.session_state.style != cfg.style:
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st.session_state.style = cfg.style
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reset()
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st.markdown("\n")
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cfg.language = st.selectbox('Language:', languages.keys())
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if st.session_state.language != cfg.language:
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st.session_state.langage = cfg.language
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reset()
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st.markdown("\n")
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cfg.student_age = st.number_input(
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'Student age:', min_value=13, value=cfg.student_age,
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step=1, format='%i'
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)
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if st.session_state.student_age != cfg.student_age:
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st.session_state.student_age = cfg.student_age
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reset()
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st.markdown("\n\n")
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if st.button('Start Over'):
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reset()
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st.markdown("---")
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st.markdown(
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"## How this works?\n"
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"This app was built with [Vectara](https://vectara.com).\n\n"
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"It demonstrates the use of Agentic Chat functionality with Vectara"
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)
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st.markdown("---")
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if "messages" not in st.session_state.keys():
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reset()
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# Display chat messages
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for message in st.session_state.messages:
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with st.chat_message(message["role"], avatar=message["avatar"]):
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st.write(message["content"])
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# User-provided prompt
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if prompt := st.chat_input():
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st.session_state.messages.append({"role": "user", "content": prompt, "avatar": 'π§βπ»'})
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with st.chat_message("user", avatar='π§βπ»'):
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print_text(f"Starting new question: {prompt}\n", color='green')
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st.write(prompt)
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# Generate a new response if last message is not from assistant
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if st.session_state.messages[-1]["role"] != "assistant":
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with st.chat_message("assistant", avatar='π€'):
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with st.spinner('Thinking...'):
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res = st.session_state.agent.chat(prompt)
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cleaned = re.sub(r'\[\d+\]', '', res.response)
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response = st.write(cleaned)
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message = {"role": "assistant", "content": cleaned, "avatar": 'π€'}
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st.session_state.messages.append(message)
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if __name__ == "__main__":
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launch_bot()
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requirements.txt
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requests_to_curl==1.1.0
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toml==0.10.2
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omegaconf==2.3.0
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syrupy==4.0.8
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streamlit==1.32.2
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translate==3.6.1
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