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Update app.py
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app.py
CHANGED
@@ -50,25 +50,6 @@ for message in st.session_state.messages:
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st.markdown(message["content"])
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background_info = f"""
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My name is Yiqiao and I am the developer of this chatbot. Here's my profile:
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I have been in the AI/ML space since 2015, leading all forms of AI-backed solutions including but not limited to Computer Vision, Natural Language Models (NLP), and most recently Large Language Models (LLMs) and Generative AI. I am currently a Tech Lead at [Vertex Inc](https://www.vertexinc.com/), a global leading provider of tax technologies ππ».
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Previously, I was a Senior ML Engineer at an [S&P 500](https://en.wikipedia.org/wiki/S%26P_500) company, [LabCorp](https://www.labcorp.com/), developing AI-driven solutions π§ π» in drug diagnostics, drug development, operations management, and financial decisions for our global leaders in life sciences ππ¬ (see Labcorp SEC filings [here](https://www.sec.gov/edgar/browse/?CIK=920148&owner=exclude)).
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I have also held positions such as enterprise-level Data Scientist at [Bayer](https://www.bayer.com/en/agriculture) (a [EURO STOXX 50](https://en.wikipedia.org/wiki/EURO_STOXX_50) company), Quantitative Researcher (apprenticeship) at [AQR](https://www.aqr.com/) (a global hedge fund pioneering in alternative quantitative strategies to portfolio management and [factor-based trading](https://www.blackrock.com/us/individual/investment-ideas/what-is-factor-investing)), and Equity Trader at [T3 Trading](https://t3trading.com/) on Wall Street (where I was briefly licensed [Series 56](https://brokercheck.finra.org/individual/summary/6338834) by [FINRA](https://www.finra.org/)).
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I supervise a small fund specializing in algorithmic trading (since 2011, performance is [here](https://yinscapital.com/letters/)) in equity market, cryptocurrencies, and real estate investment. I also run my own monetized [YouTube Channel](https://youtube.com/YiqiaoYin/).
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Feel free to add me on [LinkedIn](https://www.linkedin.com/in/yiqiaoyin/). ππ
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Though I started in Finance, my AI career started from academic environment. I was a PhD student in [Statistics](http://stat.columbia.edu/) at [Columbia University](https://www.columbia.edu/) from September of 2020 to December of 2021 ππ. I earned a B.A. in [Mathematics](https://www.sas.rochester.edu/mth/), and an M.S. in [Finance](https://simon.rochester.edu/programs/full-time-ms-in-finance) from [University of Rochester](https://www.rochester.edu/) πΌπ. My research interests are wide-ranging in representation learning, including [Feature Learning](https://en.wikipedia.org/wiki/Feature_learning), [Deep Learning](https://en.wikipedia.org/wiki/Deep_learning), [Computer Vision (CV)](https://en.wikipedia.org/wiki/Computer_vision), and [Natural Language Processing (NLP)](https://en.wikipedia.org/wiki/Natural_language_processing) π€π. Additionally, I have some prior research experience in [Financial Economics](https://en.wikipedia.org/wiki/Financial_economics) and [Asset Pricing](https://en.wikipedia.org/wiki/Asset_pricing) πΉπ.
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I teach students at my leisure time and it's one of my greatest passion. Here's about the program:
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- Typically I teach one-to-one sessions for students from age 15 to adults.
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- We conduct quantitative and AI-driven research on a topic of student's choice.
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- Depending on the needs, the research could lead to presentation/competition/publication/conference.
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- For sample work, go to [this site](https://www.y-yin.io/) and click on "Research".
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"""
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# React to user input
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if prompt := st.chat_input("What is up?"):
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@@ -76,10 +57,9 @@ if prompt := st.chat_input("What is up?"):
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st.chat_message("user").markdown(prompt)
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content":
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Remember the {background_info}
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and answer the question {prompt}"""})
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response = call_llama(prompt)
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# Display assistant response in chat message container
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st.markdown(message["content"])
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# React to user input
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if prompt := st.chat_input("What is up?"):
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st.chat_message("user").markdown(prompt)
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content": prompt})
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# API Call
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response = call_llama(prompt)
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# Display assistant response in chat message container
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