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| import time | |
| import os | |
| from functools import lru_cache | |
| import pandas as pd | |
| import streamlit as st | |
| import streamlit_ace as stace | |
| import duckdb | |
| import numpy as np # for user session | |
| import scipy # for user session | |
| import plotly_express | |
| import plotly.express as px # for user session | |
| import plotly.figure_factory as ff # for user session | |
| import matplotlib.pyplot as plt # for user session | |
| import sklearn | |
| from ydata_profiling import ProfileReport | |
| from streamlit_pandas_profiling import st_profile_report | |
| st.set_page_config(page_title="PySQLify", page_icon="🔎", layout="wide") | |
| st.title("Auto-PySQLify") | |
| header = """ | |
| > _Automated Data Analysis_ Tool for analzying datasets with auto generated Python / SQL code using natural language. | |
| > `GPT-powered` and `Jupyter notebook-inspired` | |
| """ | |
| st.markdown(header, unsafe_allow_html=True) | |
| st.markdown( | |
| "> <sub>[NYC AI Hackathon](https://tech.cornell.edu/events/nyc-gpt-llm-hackathon/) April, 23 2023</sub>", | |
| unsafe_allow_html=True, | |
| ) | |
| if "ANTHROPIC_API_KEY" not in os.environ: | |
| os.environ["ANTHROPIC_API_KEY"] = st.text_input( | |
| "Anthropic API Key", type="password" | |
| ) | |
| if "OPENAI_API_KEY" not in os.environ: | |
| os.environ["OPENAI_API_KEY"] = st.text_input("OpenAI API Key", type="password") | |
| p = st.write | |
| print = st.write | |
| display = st.write | |
| def _read_csv(f, **kwargs): | |
| df = pd.read_csv(f, on_bad_lines="skip", **kwargs) | |
| # clean | |
| df.columns = [c.strip() for c in df.columns] | |
| return df | |
| def timer(func): | |
| def wrapper_function(*args, **kwargs): | |
| start_time = time.time() | |
| func(*args, **kwargs) | |
| st.write(f"`{(time.time() - start_time):.2f}s.`") | |
| return wrapper_function | |
| SAMPLE_DATA = { | |
| "Churn dataset": "https://raw.githubusercontent.com/AtashfarazNavid/MachineLearing-ChurnModeling/main/Streamlit-WebApp-1/Churn.csv", | |
| "Periodic Table": "https://gist.githubusercontent.com/GoodmanSciences/c2dd862cd38f21b0ad36b8f96b4bf1ee/raw/1d92663004489a5b6926e944c1b3d9ec5c40900e/Periodic%2520Table%2520of%2520Elements.csv", | |
| "Movies": "https://raw.githubusercontent.com/reisanar/datasets/master/HollywoodMovies.csv", | |
| "Iris Flower": "https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv", | |
| "World Population": "https://gist.githubusercontent.com/curran/13d30e855d48cdd6f22acdf0afe27286/raw/0635f14817ec634833bb904a47594cc2f5f9dbf8/worldcities_clean.csv", | |
| "Country Table": "https://raw.githubusercontent.com/datasciencedojo/datasets/master/WorldDBTables/CountryTable.csv", | |
| "World Cities": "https://raw.githubusercontent.com/dr5hn/countries-states-cities-database/master/csv/cities.csv", | |
| "World States": "https://raw.githubusercontent.com/dr5hn/countries-states-cities-database/master/csv/states.csv", | |
| "World Countries": "https://raw.githubusercontent.com/dr5hn/countries-states-cities-database/master/csv/countries.csv", | |
| } | |
| def read_data(): | |
| txt = "Upload a data file (supported files: .csv)" | |
| placeholder = st.empty() | |
| with placeholder: | |
| col1, col2, col3 = st.columns([3, 2, 1]) | |
| with col1: | |
| file_ = st.file_uploader(txt, help="TODO: .tsv, .xls, .xlsx") | |
| with col2: | |
| url = st.text_input( | |
| "Read from a URL", | |
| placeholder="Enter URL (supported types: .csv and .tsv)", | |
| ) | |
| if url: | |
| file_ = url | |
| with col3: | |
| selected = st.selectbox( | |
| "Select a sample dataset", options=[""] + list(SAMPLE_DATA) | |
| ) | |
| if selected: | |
| file_ = SAMPLE_DATA[selected] | |
| if not file_: | |
| st.stop() | |
| placeholder.empty() | |
| # kwargs = {"skiprows": st.number_input("skip header", value=0, max_value=10)} | |
| kwargs = {"skiprows": 0} | |
| try: | |
| return _read_csv(file_, **kwargs) | |
| except Exception as e: | |
| st.warning("Unsupported file type!") | |
| st.stop() | |
| def display(df): | |
| view_info = st.sidebar.checkbox("view data types") | |
| st.dataframe(df, use_container_width=True) | |
| # info | |
| st.markdown(f"> <sup>shape `{df.shape}`</sup>", unsafe_allow_html=True) | |
| if view_info: | |
| types_ = df.dtypes.to_dict() | |
| types_ = [{"Column": c, "Type": t} for c, t in types_.items()] | |
| df_ = pd.DataFrame(types_) | |
| st.sidebar.subheader("TABLE DETAILS") | |
| st.sidebar.write(df_) | |
| def code_editor(language, hint, show_panel, key=None, content=None): | |
| # Spawn a new Ace editor | |
| placeholder = st.empty() | |
| default_theme = "solarized_dark" if language == "sql" else "chrome" | |
| with placeholder.expander("CELL CONFIG"): | |
| # configs | |
| _THEMES = stace.THEMES | |
| _KEYBINDINGS = stace.KEYBINDINGS | |
| col21, col22 = st.columns(2) | |
| with col21: | |
| theme = st.selectbox( | |
| "Theme", options=[default_theme] + _THEMES, key=f"{language}1{key}" | |
| ) | |
| tab_size = st.slider( | |
| "Tab size", min_value=1, max_value=8, value=4, key=f"{language}2{key}" | |
| ) | |
| with col22: | |
| keybinding = st.selectbox( | |
| "Keybinding", | |
| options=[_KEYBINDINGS[-2]] + _KEYBINDINGS, | |
| key=f"{language}3{key}", | |
| ) | |
| font_size = st.slider( | |
| "Font size", | |
| min_value=5, | |
| max_value=24, | |
| value=14, | |
| key=f"{language}4{key}", | |
| ) | |
| height = st.slider( | |
| "Editor height", value=130, max_value=777, key=f"{language}5{key}" | |
| ) | |
| # kwargs = {theme: theme, keybinding: keybinding} # TODO: DRY | |
| if not show_panel: | |
| placeholder.empty() | |
| content = stace.st_ace( | |
| value=content if content else "", | |
| language=language, | |
| height=height, | |
| show_gutter=False, | |
| # annotations="", | |
| placeholder=hint, | |
| keybinding=keybinding, | |
| theme=theme, | |
| font_size=font_size, | |
| tab_size=tab_size, | |
| key=key, | |
| ) | |
| # Display editor's content as you type | |
| # content | |
| return content | |
| def query_data(sql, df): | |
| try: | |
| return duckdb.query(sql).df() | |
| except Exception as e: | |
| st.warning("Invalid Query!") | |
| # st.stop() | |
| def download(df, key, save_as="results.csv"): | |
| # -- to download | |
| # @st.cache_data | |
| def convert_df(_df): | |
| return _df.to_csv().encode("utf-8") | |
| csv = convert_df(df) | |
| st.download_button("Download", csv, save_as, "text/csv", key=key) | |
| def display_results(query: str, result: pd.DataFrame, key: str): | |
| st.dataframe(result, use_container_width=True) | |
| st.markdown(f"> `{result.shape}`") | |
| download(result, key=key) | |
| def run_python_script(user_script, key): | |
| if user_script.startswith("st.") or ";" in user_script: | |
| py = user_script | |
| elif user_script.endswith("?"): # -- same as ? in Jupyter Notebook | |
| in_ = user_script.replace("?", "") | |
| py = f"st.help({in_})" | |
| else: | |
| py = f"st.write({user_script})" | |
| try: | |
| cmds = py.split(";") | |
| for cmd in cmds: | |
| exec(cmd) | |
| except Exception as e: | |
| c1, c2 = st.columns(2) | |
| c1.warning("Wrong Python command.") | |
| if c2.button("Show error", key=key): | |
| st.exception(e) | |
| def data_profiler(df): | |
| return ProfileReport(df, title="Profiling Report") | |
| def docs(): | |
| content = """ | |
| # What | |
| Upload a dataset to process (manipulate/analyze) it using SQL and Python, similar to running Jupyter Notebooks. | |
| To get started, drag and drop the dataset file, read from a URL, or select a sample dataset. To load a new dataset, refresh the webpage. | |
| > <sub>[_src code_ here](https://github.com/iamaziz/sqlify)</sub> | |
| More public datasets available [here](https://github.com/fivethirtyeight/data). | |
| # Usage | |
| Example usage | |
| > After loading the sample Iris dataset from sklearn (or select it from the dropdown list), the lines below can be executed inside a Python cell: | |
| ```python | |
| from sklearn.datasets import load_iris; | |
| from sklearn import tree; | |
| iris = load_iris(); | |
| X, y = iris.data, iris.target; | |
| clf = tree.DecisionTreeClassifier(max_depth=4); | |
| clf = clf.fit(X, y); | |
| plt.figure(figsize=(7,3)); | |
| fig, ax = plt.subplots() | |
| tree.plot_tree(clf, filled=True, fontsize=4); | |
| st.pyplot(fig) | |
| ``` | |
| Which outputs the tree below: | |
| > <img width="1000" alt="image" src="https://user-images.githubusercontent.com/3298308/222992623-1dba9bad-4858-43b6-84bf-9d7cf78d61f7.png"> | |
| # SCREENSHOTS | |
| ## _EXAMPLE 1_ | |
|  | |
| ## _EXAMPLE 2_ | |
|  | |
|  | |
| ## _EXAMPLE 3_ | |
|  | |
| ## _EXAMPLE 4_ | |
|  | |
| """ | |
| with st.expander("READE"): | |
| st.markdown(content, unsafe_allow_html=True) | |
| return st.checkbox("Show more code examples") | |
| def display_example_snippets(): | |
| from glob import glob | |
| examples = glob("./examples/*") | |
| with st.expander("EXAMPLES"): | |
| example = st.selectbox("", options=[""] + examples) | |
| if example: | |
| with open(example, "r") as f: | |
| content = f.read() | |
| st.code(content) | |
| class GPTWrapper: | |
| def __init__(self): # , df_info): | |
| from gpt import AnthropicSerivce, OpenAIService | |
| self.anthropic_model = AnthropicSerivce() | |
| self.df_info = df_info | |
| def ask_sql(df_info, question): | |
| from gpt import OpenAIService | |
| openai_model = OpenAIService() | |
| prompt = GPTWrapper().build_sql_prompt(df_info, question) | |
| res = openai_model.prompt(prompt) | |
| return res, prompt | |
| def ask_python(df_info, question): | |
| from gpt import OpenAIService | |
| openai_model = OpenAIService() | |
| prompt = GPTWrapper().build_python_prompt(df_info, question) | |
| res = openai_model.prompt(prompt) | |
| return res, prompt | |
| def build_sql_prompt(df_info, question): | |
| prompt = f"""I have data in a pandas dataframe, here is the data schema: {df_info} | |
| Next, I will ask you a question. Assume the table name is `df`. | |
| And you will answer in writing a SQL query only by using the table `df` and shema above. | |
| Here is the question: {question}. | |
| """ | |
| return prompt | |
| def build_python_prompt(df_info, question): | |
| prompt = f"""I have data in a pandas dataframe, here is the dataframe schema: {df_info} | |
| Next, I will ask you a question. Assume the data is stored in a variable named `df`. | |
| And you will answer in writing a Python code only by using the variable `df` and shema above. | |
| Here are some instructions you must follow when writing the code: | |
| - The answer must be Python code only. | |
| - The code must include column names from the dataframe schema above only. | |
| - Import any required libraries in the first line of the generated code. | |
| - Use `df` as the variable name for the dataframe. | |
| - Don't include any comments in the code. | |
| - Every line of code must end with `;`. | |
| - For non-plotting answers, you must use `print()` to print the answer. | |
| - For plotting answers, one of the folowing options must be used: | |
| - `st.pyplot(fig)` to display the plot in the Streamlit app. | |
| - plotly_express to generate a plot and `st.plotly_chart()` to show it. | |
| Here is the question: {question} | |
| """ | |
| return prompt | |
| def suggest_questions(df_info, language): | |
| prompt = f""" | |
| {df_info} | |
| What questions (exploratory or explanatory) can be asked about this dataset to analyze the data as a whole using {language}? Be as specific as possible based on the data schema above. | |
| """ | |
| from gpt import OpenAIService | |
| openai_model = OpenAIService() | |
| res = openai_model.prompt(prompt) | |
| return res, prompt | |
| def ask_gpt_sql(df_info, key): | |
| # -- GPT AI | |
| # agi = GPTWrapper(df_info=df_info) | |
| question = st.text_input( | |
| "Ask a question about the dataset to get a SQL query that answers the question", | |
| placeholder="How many rows are there in the dataset?", | |
| key=key, | |
| ) | |
| if question: | |
| # res, prompt = agi.ask_sql(df_info, question) | |
| res, prompt = GPTWrapper().ask_sql(df_info, question) | |
| # st.markdown(f"```{prompt}```") | |
| sql_query = res.choices[0].message.content | |
| st.code(sql_query, language="sql") | |
| return sql_query | |
| with st.expander("Example questions"): | |
| res, prompt = GPTWrapper().suggest_questions(df_info, "SQL") | |
| suggestions = res.choices[0].message.content | |
| st.markdown("Here are some example questions:") | |
| st.markdown(f"```{suggestions}```", unsafe_allow_html=True) | |
| def ask_gpt_python(df_info, key): | |
| # -- GPT AI | |
| question = st.text_input( | |
| "Ask a question about the dataset to get a Python code that answers the question", | |
| placeholder="How many rows and columns are there in the dataset?", | |
| key=key, | |
| ) | |
| if question: | |
| res, prompt = GPTWrapper().ask_python(df_info, question) | |
| python_code = res.choices[0].message.content | |
| st.code(python_code, language="python") | |
| return python_code | |
| with st.expander("Example questions"): | |
| res, prompt = GPTWrapper().suggest_questions(df_info, "Python") | |
| suggestions = res.choices[0].message.content | |
| st.markdown("Here are some example questions:") | |
| st.markdown(suggestions, unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| show_examples = docs() | |
| if show_examples: | |
| display_example_snippets() | |
| df = read_data() | |
| display(df) | |
| # -- data schema | |
| import io | |
| sio = io.StringIO() | |
| df.info(buf=sio) | |
| df_info = sio.getvalue() | |
| # st.markdown(f"```{df_info}```", unsafe_allow_html=True) | |
| # run and execute SQL script | |
| def sql_cells(df): | |
| st.write("---") | |
| st.header("SQL") | |
| hint = """Type SQL to query the loaded dataset, data is stored in a table named 'df'. | |
| For example, to select 10 rows: | |
| SELECT * FROM df LIMIT 10 | |
| Describe the table: | |
| DESCRIBE TABLE df | |
| """ | |
| number_cells = st.sidebar.number_input( | |
| "Number of SQL cells to use", value=1, max_value=40 | |
| ) | |
| for i in range(number_cells): | |
| key = f"sql{i}" | |
| col1, col2 = st.columns([2, 1]) | |
| st.markdown("<br>", unsafe_allow_html=True) | |
| show_panel = ( | |
| False # col2.checkbox("Show cell config panel", key=f"{i}-sql") | |
| ) | |
| col1.write(f"> `IN[{i+1}]`") | |
| # -- GPT AI | |
| query = ask_gpt_sql(df_info, key=f"{key}-gpt") | |
| content = None | |
| if query and st.button("Run the generated code", key=f"{key}-use-sql"): | |
| content = query | |
| sql = code_editor( | |
| "sql", | |
| hint, | |
| show_panel=show_panel, | |
| key=key, | |
| content=content if content else None, | |
| ) | |
| if sql: | |
| st.code(sql, language="sql") | |
| st.write(f"`OUT[{i+1}]`") | |
| res = query_data(sql, df) | |
| display_results(sql, res, f"{key}{sql}") | |
| # run and dexectue python script | |
| def python_cells(): | |
| st.write("---") | |
| st.markdown("### Python") | |
| hint = """Type Python command (one-liner) to execute or manipulate the dataframe e.g. `df.sample(7)`. By default, results are rendered using `st.write()`. | |
| 📊 Visulaization example: from "movies" dataset, plot average rating by genre: | |
| st.line_chart(df.groupby("Genre")[["RottenTomatoes", "AudienceScore"]].mean()) | |
| 🗺 Maps example: show the top 10 populated cities in the world on map (from "Cities Population" dataset) | |
| st.map(df.sort_values(by='population', ascending=False)[:10]) | |
| NOTE: for multi-lines, a semi-colon can be used to end each line e.g. | |
| print("first line"); | |
| print("second line); | |
| """ | |
| hint = """Type Python code here (use semicolons to end each line)""" | |
| help = """ | |
| For multiple lines, use semicolons e.g. | |
| ```python | |
| fig, ax = plt.subplots(); | |
| ax.hist(df[[col1, col2]]); | |
| st.pyplot(fig); | |
| ``` | |
| or | |
| ```python | |
| groups = [group for _, group in df.groupby('class')]; | |
| for i in range(3): | |
| st.write(groups[i]['name'].iloc[0]) | |
| st.bar_chart(groups[i].mean()) | |
| ``` | |
| """ | |
| number_cells = st.sidebar.number_input( | |
| "Number of Python cells to use", | |
| value=1, | |
| max_value=40, | |
| min_value=1, | |
| help=help, | |
| ) | |
| for i in range(number_cells): | |
| # st.markdown("<br><br><br>", unsafe_allow_html=True) | |
| col1, col2 = st.columns([2, 1]) | |
| # col1.write(f"> `IN[{i+1}]`") | |
| show_panel = ( | |
| False # col2.checkbox("Show cell config panel", key=f"panel{i}") | |
| ) | |
| # -- GPT AI | |
| query = ask_gpt_python(df_info, key=f"{i}-gpt") | |
| content = None | |
| if query and st.checkbox("Run the generated code", key=f"{i}-use-python"): | |
| content = query | |
| user_script = code_editor( | |
| "python", | |
| hint, | |
| show_panel=show_panel, | |
| key=i, | |
| content=content if content else None, | |
| ) | |
| if user_script: | |
| df.rename( | |
| columns={"lng": "lon"}, inplace=True | |
| ) # hot-fix for "World Population" dataset | |
| st.write(f"> `IN[{i+1}]`") | |
| st.code(user_script, language="python") | |
| st.write(f"> `OUT[{i+1}]`") | |
| run_python_script(user_script, key=f"{user_script}{i}") | |
| if st.sidebar.checkbox("Show SQL cells", value=True): | |
| sql_cells(df) | |
| if st.sidebar.checkbox("Show Python cells", value=True): | |
| python_cells() | |
| st.sidebar.write("---") | |
| if st.sidebar.checkbox( | |
| "Generate Data Profile Report", | |
| help="pandas profiling, generated by [ydata-profiling](https://github.com/ydataai/ydata-profiling)", | |
| ): | |
| st.write("---") | |
| st.header("Data Profiling") | |
| profile = data_profiler(df) | |
| st_profile_report(profile) | |
| st.write("---") |