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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="π",
)
# 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("π Introduction")
st.write("π Welcome to the Text to SQL app!")
st.write(
"π 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(
"π The NSText2SQL dataset contains more than 290.000 training samples. Then, the model is evaluated on Spider and VMLP datasets."
)
st.write("π The other pages in this app include:")
st.write(
" - π EDA Page: This page includes several visualizations to help you understand the two dataset: Spider and VMLP."
)
st.write(
" - π° Text2SQL Page: This page allows you to generate SQL query from a given question and context."
)
st.write(
" - π§βπ» About Page: This page provides information about the app and its creators."
)
st.write(
" - π Reference Page: This page lists the references used in building this app."
)
# Define a function for the EDA page
def eda():
st.title("π 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("π§βπ» 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("π 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()
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