app.py

import json
import os
import uuid
import pandas as pd
import streamlit as st
import argparse
import traceback
from typing import Dict
import requests
from utils.utils import load_data_split
from nsql.database import NeuralDB
from nsql.nsql_exec import NSQLExecutor
from nsql.nsql_exec_python import NPythonExecutor
from generation.generator import Generator
import time

ROOT_DIR = os.path.join(os.path.dirname(__file__), "./")
EXAMPLE_TABLES = {
    "Estonia men's national volleyball team": (558, "what are the total number of players from france?"),
    "Highest mountain peaks of California": (5, "which is the lowest mountain?"),
    "2010–11 UAB Blazers men's basketball team": (1, "how many players come from alabama?"),
    "1999 European Tour": (209, "how many consecutive times was south africa the host country?"),
    "Nissan SR20DET": (438, "which car is the only one with more than 230 hp?"),
}


@st.cache
def load_data():
    return load_data_split("missing_squall", "validation")


@st.cache
def get_key():
    # print the public IP of the demo machine
    ip = requests.get('https://checkip.amazonaws.com').text.strip()
    print(ip)

    URL = "http://54.242.37.195:20217/api/predict"
    # The springboard machine we built to protect the key, 20217 is the birthday of Tianbao's girlfriend
    # we will only let the demo machine have the access to the keys

    one_key = requests.post(url=URL, json={"data": "Hi, binder server. Give me a key!"}).json()['data'][0]
    return one_key


def read_markdown(path):
    with open(path, "r") as f:
        output = f.read()
    st.markdown(output, unsafe_allow_html=True)


def generate_binder_program(_args, _generator, _data_item):
    n_shots = _args.n_shots
    few_shot_prompt = _generator.build_few_shot_prompt_from_file(
        file_path=_args.prompt_file,
        n_shots=n_shots
    )
    generate_prompt = _generator.build_generate_prompt(
        data_item=_data_item,
        generate_type=(_args.generate_type,)
    )
    prompt = few_shot_prompt + "\n\n" + generate_prompt

    # Ensure the input length fit Codex max input tokens by shrinking the n_shots
    max_prompt_tokens = _args.max_api_total_tokens - _args.max_generation_tokens
    from transformers import AutoTokenizer
    tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=os.path.join(ROOT_DIR, "utils", "gpt2"))
    while len(tokenizer.tokenize(prompt)) >= max_prompt_tokens:  # TODO: Add shrink rows
        n_shots -= 1
        assert n_shots >= 0
        few_shot_prompt = _generator.build_few_shot_prompt_from_file(
            file_path=_args.prompt_file,
            n_shots=n_shots
        )
        prompt = few_shot_prompt + "\n\n" + generate_prompt

    response_dict = _generator.generate_one_pass(
        prompts=[("0", prompt)],  # the "0" is the place taker, take effect only when there are multi threads
        verbose=_args.verbose
    )
    print(response_dict)
    return response_dict["0"][0][0]


# Set up
parser = argparse.ArgumentParser()

parser.add_argument('--prompt_file', type=str, default='templates/prompts/prompt_wikitq_v3.txt')
# Binder program generation options
parser.add_argument('--prompt_style', type=str, default='create_table_select_3_full_table',
                    choices=['create_table_select_3_full_table',
                             'create_table_select_full_table',
                             'create_table_select_3',
                             'create_table',
                             'create_table_select_3_full_table_w_all_passage_image',
                             'create_table_select_3_full_table_w_gold_passage_image',
                             'no_table'])
parser.add_argument('--generate_type', type=str, default='nsql',
                    choices=['nsql', 'sql', 'answer', 'npython', 'python'])
parser.add_argument('--n_shots', type=int, default=14)
parser.add_argument('--seed', type=int, default=42)

# Codex options
# todo: Allow adjusting Codex parameters
parser.add_argument('--engine', type=str, default="code-davinci-002")
parser.add_argument('--max_generation_tokens', type=int, default=512)
parser.add_argument('--max_api_total_tokens', type=int, default=8001)
parser.add_argument('--temperature', type=float, default=0.)
parser.add_argument('--sampling_n', type=int, default=1)
parser.add_argument('--top_p', type=float, default=1.0)
parser.add_argument('--stop_tokens', type=str, default='\n\n',
                    help='Split stop tokens by ||')
parser.add_argument('--qa_retrieve_pool_file', type=str, default='templates/qa_retrieve_pool.json')

# debug options
parser.add_argument('-v', '--verbose', action='store_false')
args = parser.parse_args()
keys = [get_key()]

# The title
st.markdown("# Binder Playground")

# Summary about Binder
read_markdown('resources/summary.md')

# Introduction of Binder
# todo: Write Binder introduction here
# read_markdown('resources/introduction.md')
st.image('resources/intro.png')

# Upload tables/Switch tables

st.markdown('### Try Binder!')
col1, _ = st.columns(2)
with col1:
    selected_table_title = st.selectbox(
        "Select an example table",
        (
            "Estonia men's national volleyball team",
            "Highest mountain peaks of California",
            "2010–11 UAB Blazers men's basketball team",
            "1999 European Tour",
            "Nissan SR20DET",
        )
    )

# Here we just use ourselves'
data_items = load_data()
data_item = data_items[EXAMPLE_TABLES[selected_table_title][0]]
table = data_item['table']
header, rows, title = table['header'], table['rows'], table['page_title']
db = NeuralDB(
    [{"title": title, "table": table}])  # todo: try to cache this db instead of re-creating it again and again.
df = db.get_table_df()
st.markdown("Title: {}".format(title))
st.dataframe(df)

# Let user input the question
question = st.text_input(
    "Ask a question about the table:",
    value=EXAMPLE_TABLES[selected_table_title][1]
)
with col1:
    # todo: Why selecting language will flush the page?
    selected_language = st.selectbox(
        "Select a programming language",
        ("SQL", "Python"),
    )
if selected_language == 'SQL':
    args.prompt_file = 'templates/prompts/prompt_wikitq_v3.txt'
    args.generate_type = 'nsql'
elif selected_language == 'Python':
    args.prompt_file = 'templates/prompts/prompt_wikitq_python_simplified_v4.txt'
    args.generate_type = 'npython'
else:
    raise ValueError(f'{selected_language} language is not supported.')
button = st.button("Generate program")
if not button:
    st.stop()

# Generate Binder Program
generator = Generator(args, keys=keys)
with st.spinner("Generating program ..."):
    binder_program = generate_binder_program(args, generator,
                                             {"question": question, "table": db.get_table_df(), "title": title})


# Do execution
st.markdown("#### Binder program")
if selected_language == 'SQL':
    with st.container():
        st.write(binder_program)
    executor = NSQLExecutor(args, keys=keys)
elif selected_language == 'Python':
    st.code(binder_program, language='python')
    executor = NPythonExecutor(args, keys=keys)
    db = db.get_table_df()
else:
    raise ValueError(f'{selected_language} language is not supported.')
try:
    stamp = '{}'.format(uuid.uuid4())
    os.makedirs('tmp_for_vis/', exist_ok=True)
    with st.spinner("Executing program ..."):
        exec_answer = executor.nsql_exec(stamp, binder_program, db)
    # todo: Make it more pretty!
    # todo: Do we need vis for Python?
    if selected_language == 'SQL':
        with open("tmp_for_vis/{}_tmp_for_vis_steps.txt".format(stamp), "r") as f:
            steps = json.load(f)
        st.markdown("#### Steps & Intermediate results")
        for i, step in enumerate(steps):
            st.markdown(step)
            st.text("↓")
            with st.spinner('...'):
                time.sleep(1)
            with open("tmp_for_vis/{}_result_step_{}.txt".format(stamp, i), "r") as f:
                result_in_this_step = json.load(f)
            if isinstance(result_in_this_step, Dict):
                st.dataframe(pd.DataFrame(pd.DataFrame(result_in_this_step["rows"], columns=result_in_this_step["header"])))
            else:
                st.markdown(result_in_this_step)
            st.text("↓")
    elif selected_language == 'Python':
        pass
    if isinstance(exec_answer, list) and len(exec_answer) == 1:
        exec_answer = exec_answer[0]
    st.markdown(f'Execution answer: {exec_answer}')
except Exception as e:
    traceback.print_exc()