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import gradio as gr |
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''' |
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https://huggingface.co/spaces/kevinhug/clientX |
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https://hits.seeyoufarm.com/ |
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''' |
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''' |
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TIME SERIES ANALYTICS |
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''' |
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import yfinance as yf |
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import seaborn as sns |
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from sklearn.preprocessing import StandardScaler |
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def trend(t): |
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data = yf.download(t, period="3mo") |
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for c in t.split(' '): |
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q=data.loc[:,('Close',c)] |
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data.loc[:,('Close_MA',c)]=q.rolling(9).mean() -q.rolling(42).mean() |
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q=data.loc[:,('Volume',c)] |
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data.loc[:,('Volume_MA',c)]=q.rolling(9).mean() -q.rolling(42).mean() |
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ma=data.loc[:,["Volume_MA","Close_MA"]].tail(15) |
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std=StandardScaler() |
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result=std.fit_transform(ma) |
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df=pd.DataFrame(result,columns=ma.columns) |
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d=df.tail(1).stack(level=-1).droplevel(0, axis=0) |
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return gr.ScatterPlot(d, x="Close_MA", y="Volume_MA",color=d.index.values) |
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''' |
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SIMILAR VECTOR DB SEARCH |
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''' |
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import chromadb |
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client = chromadb.PersistentClient(path="chroma.db") |
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db = client.get_collection(name="banks") |
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def similar(issue): |
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global db |
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docs = db.query(query_texts=issue, n_results=5) |
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return docs |
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''' |
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FINE TUNE LLM LIKE SCORE |
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''' |
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from fastai.text.all import * |
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import pathlib |
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p=pathlib.Path('./banks_txt_like.pkl').resolve() |
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''' |
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NotImplementedError: cannot instantiate ‘WindowsPath’ on your system |
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''' |
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import platform |
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plt = platform.system() |
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if plt == 'Windows': |
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pathlib.PosixPath = pathlib.WindowsPath |
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else: |
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pathlib.WindowsPath = pathlib.PosixPath |
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learn = load_learner(p) |
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def like(issue): |
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pred,idx,probs = learn.predict(issue) |
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return pred |
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''' |
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https://www.gradio.app/docs/interface |
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''' |
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with gr.Blocks() as demo: |
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''' |
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https://hits.seeyoufarm.com/ |
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https://dash.elfsight.com |
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''' |
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counter=""" |
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<script src="https://static.elfsight.com/platform/platform.js" data-use-service-core defer></script> |
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<div class="elfsight-app-5f3e8eb9-9103-490e-9999-e20aa4157dc7" data-elfsight-app-lazy></div> |
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""" |
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gr.Markdown("""Enhancing Customer Engagement and Operational Efficiency with NLP |
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========= |
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1) Semantic Similarity Document Search (SSDS) |
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2) Fine Tune LLM |
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#### Data Scientist: Kevin Wong, [email protected], 416-903-7937 |
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##### Open source ml bank dataset, |
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__I'm just using a small sample of this data set for demo__ |
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https://www.kaggle.com/datasets/trainingdatapro/20000-customers-reviews-on-banks/?select=Banks.csv |
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[](https://hits.seeyoufarm.com) |
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""") |
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with gr.Tab("Semantic Similarity Document Search (SSDS)"): |
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in_similar = gr.Textbox(placeholder="having credit card problem", |
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label="Issue", |
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info="issue you want to explore about" |
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) |
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out_similar = gr.JSON(label="Similar Verbatim") |
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btn_similar = gr.Button("Find Similar Verbatim") |
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btn_similar.click(fn=similar, inputs=in_similar, outputs=out_similar) |
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gr.Examples( |
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[ |
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["having credit card problem"], |
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["low interest credit card"], |
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["loan"], |
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["upset customer"], |
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["what is the password"], |
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], |
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[in_similar] |
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) |
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gr.Markdown(""" |
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Description: |
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======= |
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In today's dynamic financial landscape, the Semantic Similarity Document Search (SSDS) capability is a practical innovation to improve client experience, marketing leads, and sentiment analysis. As a Data Scientist with a decades in the financial industry, I see the value of SSDS in action. |
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Investment Portfolio Construction/Marketing Leads: |
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------ |
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To enhance marketing strategies, SSDS identifies market trends and consumer preferences, such as the demand for low-interest credit cards, and GIC. It's a treasure trove for refining our product offerings to the targeted customer according to their credit score, risk appetite, demographic, collateral, capital, and economic conditions, enhancing the lift and efficiency of the recommendation process. |
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Combining **SingleStore MEMSQL/Kafka structured streaming** for a real-time recommendation for closing sales at the right time in the right channel. |
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Optimize your Asset Allocation with your objective function and cost function. |
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### issue: |
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- low interest credit card |
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- GIC |
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AML/KYC/Compliance/Audit/Cyber Security/Fraud Analytics/Observability: |
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------ |
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Proactive Detection: Identify potential fraud threats and vulnerabilities in real-time. |
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Customer-Centric Approach: Gain insights into customer concerns, allowing us to address them promptly. |
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### issue: |
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- what is the password |
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Client Experience: |
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------ |
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When a client faces a bad experience, SSDS helps us swiftly locate relevant documents to understand and address their concerns, be it credit card issues, late payment fees, or credit score drops. |
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### issue: |
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- having bad client experience |
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- having credit card problem |
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- late payment fee |
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- credit score dropping |
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Sentiments: |
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------ |
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SSDS tracks customer sentiment, empowering us to swiftly respond to upset customers. It ensures we address their issues promptly, enhancing trust and loyalty. |
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With no need for jargon, SSDS delivers tangible value to our fintech operations. It's about staying agile, informed, and customer-centric in a rapidly changing financial world. |
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### issue: |
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- upset customer |
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""") |
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with gr.Accordion("Future Improvement"): |
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gr.Markdown(""" |
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tuning the distance for use case |
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""") |
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with gr.Tab("Fine Tune LLM"): |
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in_like = gr.Textbox(placeholder="having credit card problem" , label="Issue", |
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info="issue you want to explore about") |
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out_like = gr.Textbox(placeholder="like score in range [2 to 248] from fine tuning data", |
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label="like score", |
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info="like score") |
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btn_like = gr.Button("Classify Like Score") |
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btn_like.click(fn=like, inputs=in_like, outputs=out_like) |
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gr.Examples( |
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[ |
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["having credit card problem"], |
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["low interest credit card"], |
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["loan"], |
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["upset customer"], |
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["what is the password"], |
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], |
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[in_like] |
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) |
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gr.Markdown(""" |
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Maximizing Trading Efficiency: Investment in Modernization and Growth Industries |
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========= |
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The industry life cycle is a useful tool for traders to identify growth and decline industries. It describes the evolution of an industry based on its stages of growth and decline 1. There are four phases of the industry life cycle: introduction, growth, maturity, and decline 2. |
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A growth industry is a sector of an economy that experiences a higher-than-average growth rate compared to other sectors. Growth industries are often new or pioneer industries that did not exist in the past. Their growth is a result of demand for new products or services offered by companies in the field 3. Identifying growth industries can help traders to speed up trading by investing in companies that are likely to experience rapid growth in the future. |
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On the other hand, a decline industry is a sector of an economy that is experiencing a lower-than-average growth rate compared to other sectors. Identifying decline industries can help traders to avoid investing in companies that are likely to experience a decline in the future 2. This can help traders to minimize losses and maximize profits. |
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By identifying growth and decline industries, traders can make informed investment decisions and speed up trading by investing in companies that are likely to experience growth in the future and avoiding companies that are likely to experience a decline in the future. |
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""") |
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with gr.Tab("Trading Analyics"): |
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in_ts = gr.Textbox(placeholder="QQQM CIF VEGI PJP", |
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label="Ticker", |
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info="Identify Industry Trend, (top right is grow trending)" |
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) |
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plot = gr.ScatterPlot() |
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btn_ts = gr.Button("Find Trending Industry") |
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btn_ts.click(fn=trend, inputs=in_ts, outputs=plot) |
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gr.Markdown(""" |
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Smart Insights: Elevating Customer Engagement Through Sentiment Analysis |
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========= |
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As a Data Scientist with a decades of financial industry experience, I recognize the paramount importance of staying closely tuned to our customer's needs and opinions. In this app, Fine Tune LLM, we have shown how fine-tuning a Language Model (LLM) on a custom dataset can provide valuable insights into customer sentiment across crucial areas such as service, sales, point of failure, product, and emerging trends. |
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Objective: |
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--------- |
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Our aim is to extract meaningful insights from customer interactions to improve our services, products, and overall customer experience. This analysis will help us understand what our customers are discussing and how they feel about different aspects of our business. |
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Use Case: |
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- intervene attrition through incentive |
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""") |
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demo.launch() |
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