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import sqlite3 |
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import huggingface_hub |
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import gradio as gr |
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import pandas as pd |
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import shutil |
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import os |
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import datetime |
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from apscheduler.schedulers.background import BackgroundScheduler |
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DB_FILE = "./reviews.db" |
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TOKEN = os.environ.get('HUB_TOKEN') |
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repo = huggingface_hub.Repository( |
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local_dir="data", |
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repo_type="dataset", |
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clone_from="freddyaboulton/gradio-reviews", |
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use_auth_token=TOKEN |
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) |
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repo.git_pull() |
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shutil.copyfile("./data/reviews.db", DB_FILE) |
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db = sqlite3.connect(DB_FILE) |
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try: |
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db.execute("SELECT * FROM reviews").fetchall() |
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db.close() |
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except sqlite3.OperationalError: |
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db.execute( |
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''' |
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CREATE TABLE reviews (id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, |
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP NOT NULL, |
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name TEXT, review INTEGER, comments TEXT) |
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''') |
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db.commit() |
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db.close() |
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def get_latest_reviews(db: sqlite3.Connection): |
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reviews = db.execute("SELECT * FROM reviews ORDER BY id DESC limit 10").fetchall() |
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total_reviews = db.execute("Select COUNT(id) from reviews").fetchone()[0] |
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reviews = pd.DataFrame(reviews, columns=["id", "date_created", "name", "review", "comments"]) |
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return reviews, total_reviews |
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def add_review(name: str, review: int, comments: str): |
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db = sqlite3.connect(DB_FILE) |
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cursor = db.cursor() |
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cursor.execute("INSERT INTO reviews(name, review, comments) VALUES(?,?,?)", [name, review, comments]) |
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db.commit() |
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reviews, total_reviews = get_latest_reviews(db) |
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db.close() |
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return reviews, total_reviews |
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def load_data(): |
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db = sqlite3.connect(DB_FILE) |
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reviews, total_reviews = get_latest_reviews(db) |
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db.close() |
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return reviews, total_reviews |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(): |
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name = gr.Textbox(label="Name", placeholder="What is your name?") |
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review = gr.Radio(label="How satisfied are you with using gradio?", choices=[1, 2, 3, 4, 5]) |
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comments = gr.Textbox(label="Comments", lines=10, placeholder="Do you have any feedback on gradio?") |
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submit = gr.Button(value="Submit Feedback") |
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with gr.Column(): |
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with gr.Box(): |
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gr.Markdown("Most recently created 10 rows: See full dataset [here](https://huggingface.co/datasets/freddyaboulton/gradio-reviews)") |
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data = gr.Dataframe() |
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count = gr.Number(label="Total number of reviews") |
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submit.click(add_review, [name, review, comments], [data, count]) |
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demo.load(load_data, None, [data, count]) |
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def backup_db(): |
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shutil.copyfile(DB_FILE, "./data/reviews.db") |
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db = sqlite3.connect(DB_FILE) |
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reviews = db.execute("SELECT * FROM reviews").fetchall() |
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pd.DataFrame(reviews).to_csv("./data/reviews.csv", index=False) |
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print("updating db") |
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repo.push_to_hub(blocking=False, commit_message=f"Updating data at {datetime.datetime.now()}") |
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scheduler = BackgroundScheduler() |
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scheduler.add_job(func=backup_db, trigger="interval", seconds=60) |
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scheduler.start() |
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demo.launch() |
