feat: add main menu options and scripts

resources/examples.csv

@@ -0,0 +1,12 @@
+context,question
+"CREATE TABLE head (name VARCHAR, born_state VARCHAR, age VARCHAR)","List the name, born state and age of the heads of departments ordered by age."
+"CREATE TABLE customer (id number, name text, gender text, age number, district_id number;
+CREATE TABLE registration (customer_id number, product_id number); 
+CREATE TABLE district (id number, name text, prefix text, province_id number); 
+CREATE TABLE province (id number, name text, code text)
+CREATE TABLE product (id number, category text, name text, description text, price number, duration number, data_amount number, voice_amount number, sms_amount number);",Có bao nhiêu khách hàng sử dụng gói cước có tên ST70?
+"CREATE TABLE customer (id number, name text, gender text, age number, district_id number;
+CREATE TABLE registration (customer_id number, product_id number); 
+CREATE TABLE district (id number, name text, prefix text, province_id number); 
+CREATE TABLE province (id number, name text, code text)
+CREATE TABLE product (id number, category text, name text, description text, price number, duration number, data_amount number, voice_amount number, sms_amount number);",Số lượng khách hàng có độ tuổi từ 15 đến 20 tuổi

st_app.py

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+import os
+import sys
+
+# insert current directory to sys.path
+sys.path.insert(0, os.path.abspath(os.path.dirname(__file__)))
+
+import re
+import numpy as np
+import pandas as pd
+import streamlit as st
+import requests
+
+from googletrans import Translator
+from langdetect import detect
+
+translator = Translator()
+
+st.set_page_config(
+    layout="wide",
+    page_title="Text To SQL",
+    page_icon="📊",
+)
+
+# TEXT_2_SQL_API = "http://83.219.197.235:40172/api/text2sql/ask"
+TEXT_2_SQL_API = os.environ.get(
+    "TEXT_2_SQL_API", "http://localhost:8501/api/text2sql/ask"
+)
+
+
+@st.cache_data
+def ask_text2sql(question, context):
+    if detect(question) != "en":
+        question = translate_question(question)
+        st.write("The question is translated to Vietnamese:")
+        st.code(question, language="en")
+
+    r = requests.post(
+        TEXT_2_SQL_API,
+        json={
+            "context": context,
+            "question": question,
+        },
+    )
+    return r.json()["answers"][0]
+
+
+@st.cache_data
+def translate_question(question):
+    return translator.translate(question, dest="en").text
+
+
+@st.cache_data
+def load_example_df():
+    example_df = pd.read_csv("resources/examples.csv")
+    return example_df
+
+
+def introduction():
+    st.title("📊 Introduction")
+    st.write("👋 Welcome to the Text to SQL app!")
+    st.write(
+        "🔍 This app allows you to explore the ability of Text to SQL model. The model is CodeLlama-13b finetuned using QLoRA on NSText2SQL dataset."
+    )
+    st.write(
+        "📈 The NSText2SQL dataset contains more than 290.000 training samples. Then, the model is evaluated on Spider and VMLP datasets."
+    )
+    st.write("📑 The other pages in this app include:")
+    st.write(
+        "  - 📊 EDA Page: This page includes several visualizations to help you understand the two dataset: Spider and VMLP."
+    )
+    st.write(
+        "  - 💰 Text2SQL Page: This page allows you to generate SQL query from a given question and context."
+    )
+    st.write(
+        "  - 🧑‍💻 About Page: This page provides information about the app and its creators."
+    )
+    st.write(
+        "  - 📚 Reference Page: This page lists the references used in building this app."
+    )
+
+
+# Define a function for the EDA page
+def eda():
+    st.title("📊 Dataset Exploration")
+
+    # st.subheader("Candlestick Chart")
+    # fig = go.Figure(
+    #     data=[
+    #         go.Candlestick(
+    #             x=df["date"],
+    #             open=df["open"],
+    #             high=df["high"],
+    #             low=df["low"],
+    #             close=df["close"],
+    #             increasing_line_color="green",
+    #             decreasing_line_color="red",
+    #         )
+    #     ],
+    #     layout=go.Layout(
+    #         title="Tesla Stock Price",
+    #         xaxis_title="Date",
+    #         yaxis_title="Price (USD)",
+    #         xaxis_rangeslider_visible=True,
+    #     ),
+    # )
+    # st.plotly_chart(fig)
+
+    # st.subheader("Line Chart")
+    # # Plot the closing price over time
+    # plot_column = st.selectbox(
+    #     "Select a column", ["open", "close", "low", "high"], index=0
+    # )
+    # fig = px.line(
+    #     df, x="date", y=plot_column, title=f"Tesla {plot_column} Price Over Time"
+    # )
+    # st.plotly_chart(fig)
+
+    # st.subheader("Distribution of Closing Price")
+    # # Plot the distribution of the closing price
+    # closing_price_hist = px.histogram(
+    #     df, x="close", nbins=30, title="Distribution of Tesla Closing Price"
+    # )
+    # st.plotly_chart(closing_price_hist)
+
+    # st.subheader("Raw Data")
+    # st.write("You can see the raw data below.")
+    # # Display the dataset
+    # st.dataframe(df)
+
+
+def preprocess_context(context):
+    context = context.replace("\n", " ").replace("\t", " ").replace("\r", " ")
+
+    # Remove multiple spaces
+    context = re.sub(" +", " ", context)
+
+    return context
+
+
+def examples():
+    st.title("Examples")
+    st.write(
+        "This page uses CodeLlama-13b finetuned using QLoRA on NSText2SQL dataset to generate SQL query from a given question and context.\nThe examples are listed below"
+    )
+
+    example_df = load_example_df()
+    example_tabs = st.tabs([f"Example {i+1}" for i in range(len(example_df))])
+    example_btns = []
+
+    for idx, row in example_df.iterrows():
+        with example_tabs[idx]:
+            st.markdown("##### Context:")
+            st.code(row["context"], language="sql")
+            st.markdown("##### Question:")
+            st.text(row["question"])
+
+            example_btns.append(st.button("Generate SQL query", key=f"exp-btn-{idx}"))
+
+            if example_btns[idx]:
+                st.markdown("##### SQL query:")
+                query = ask_text2sql(row["question"], row["context"])
+                st.code(query, language="sql")
+
+
+# Define a function for the Stock Prediction page
+def interactive_demo():
+    st.title("Text to SQL using CodeLlama-13b")
+    st.write(
+        "This page uses CodeLlama-13b finetuned using QLoRA on NSText2SQL dataset to generate SQL query from a given question and context."
+    )
+
+    st.subheader("Input")
+    context_placeholder = st.empty()
+    question_placeholder = st.empty()
+    context = context_placeholder.text_area(
+        "##### Context",
+        "CREATE TABLE head (name VARCHAR, born_state VARCHAR, age VARCHAR)",
+        key="context",
+    )
+    question = question_placeholder.text_input(
+        "##### Question",
+        "List the name, born state and age of the heads of departments ordered by age.",
+        key="question",
+    )
+    get_sql_button = st.button("Generate SQL query")
+
+    if get_sql_button:
+        st.markdown("##### Output")
+        query = ask_text2sql(question, context)
+        st.write("The SQL query generated by the model is:")
+        # Display the SQL query in a code block
+        st.code(query, language="sql")
+
+
+# Define a function for the About page
+def about():
+    st.title("🧑‍💻 About")
+    st.write(
+        "This Streamlit app allows you to explore stock prices and make predictions using an LSTM model."
+    )
+
+    st.header("Author")
+    st.write(
+        "This app was developed by Minh Nam. You can contact the author at trminhnam20082002@gmail.com."
+    )
+
+    st.header("Data Sources")
+    st.markdown(
+        "The Spider dataset was sourced from [Spider](https://yale-lily.github.io/spider)."
+    )
+    st.markdown("The vMLP dataset is a private dataset from Viettel.")
+
+    st.header("Acknowledgments")
+    st.write(
+        "The author would like to thank Dr. Nguyen Van Nam for his proper guidance, Mr. Nguyen Chi Dong for his support."
+    )
+
+    st.header("License")
+    st.write(
+        # "This app is licensed under the MIT License. See LICENSE.txt for more information."
+        "N/A"
+    )
+
+
+def references():
+    st.title("📚 References")
+    st.header(
+        "References for Text to SQL project using foundation model - CodeLlama-13b"
+    )
+
+    st.subheader("1. 'Project for time-series data' by AI VIET NAM, et al.")
+    st.write(
+        "This organization provides a tutorial on how to build a stock price prediction model using LSTM in the AIO2022 course."
+    )
+    st.write("Link: https://www.facebook.com/aivietnam.edu.vn")
+
+    st.subheader(
+        "2. 'PyTorch LSTMs for time series forecasting of Indian Stocks' by Vinayak Nayak"
+    )
+    st.write(
+        "This blog post describes how to build a stock price prediction model using LSTM, RNN and CNN-sliding window model."
+    )
+    st.write(
+        "Link: https://medium.com/analytics-vidhya/pytorch-lstms-for-time-series-forecasting-of-indian-stocks-8a49157da8b9#b052"
+    )
+
+    st.header("References for Streamlit")
+
+    st.subheader("1. Streamlit Documentation")
+    st.write(
+        "The official documentation for Streamlit provides detailed information about how to use the library and build Streamlit apps."
+    )
+    st.write("Link: https://docs.streamlit.io/")
+
+    st.subheader("2. Streamlit Community")
+    st.write(
+        "The Streamlit community includes a forum and a GitHub repository with examples and resources for building Streamlit apps."
+    )
+    st.write(
+        "Link: https://discuss.streamlit.io/ and https://github.com/streamlit/streamlit/"
+    )
+
+
+# Create the sidebar
+st.sidebar.title("Menu")
+pages = [
+    "Introduction",
+    "Datasets",
+    "Examples",
+    "Interactive Demo",
+    "About",
+    "References",
+]
+selected_page = st.sidebar.radio("Go to", pages)
+
+# Show the appropriate page based on the selection
+if selected_page == "Introduction":
+    introduction()
+elif selected_page == "EDA":
+    eda()
+elif selected_page == "Examples":
+    examples()
+elif selected_page == "Interactive Demo":
+    interactive_demo()
+elif selected_page == "About":
+    about()
+elif selected_page == "References":
+    references()

st_requirements.txt

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+googletrans
+streamlit
+numpy
+pandas
+googletrans==3.1.0a0
+langdetect
+requests