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import gradio as gr |
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from dataclasses import dataclass |
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import sqlite3 |
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import random |
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from utils.git_utils.tsv_io import TSVFile |
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import json |
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from PIL import Image |
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import io |
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import base64 |
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import pandas as pd |
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import datetime |
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import os |
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db_path = "tmp/annotations.db" |
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columns = [ |
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"caption", |
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"conf", |
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"area", |
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"image_area", |
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"clip_score", |
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"image_id", |
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"region_cnt", |
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"region_id", |
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"is_acceptable", |
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"created_at", |
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] |
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conn = sqlite3.connect(db_path) |
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reviews = conn.execute("SELECT * FROM annotations").fetchall() |
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reviews = pd.DataFrame(reviews, columns=columns) |
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reviews.to_csv("tmp/annotations.csv", index=False) |
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""" |
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# How to get the table names? |
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import sqlite3 |
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conn = sqlite3.connect('your_database_name.db') |
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cursor = conn.cursor() |
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cursor.execute("SELECT name FROM sqlite_master WHERE type='table';") |
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# Fetch all rows from the result set and extract the table names |
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tables_info = cursor.fetchall() |
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table_names = [info[0] for info in tables_info] |
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print(table_names) |
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conn.close() |
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# How to get the column names? |
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import sqlite3 |
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conn = sqlite3.connect('your_database_name.db') |
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cursor = conn.cursor() |
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table_name = 'your_table_name' |
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cursor.execute(f'PRAGMA table_info({table_name});') |
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# Fetch all rows from the result set and extract the column names |
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columns_info = cursor.fetchall() |
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column_names = [info[1] for info in columns_info] |
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print(column_names) |
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conn.close() |
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""" |
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