app.py

import gradio as gr
from bs4 import BeautifulSoup
import requests
from youtube1 import getviews
from textsentiment import getresult
import math
#from python_actr import *
#from cogscidighum import *


#class myCelSci(Model):
#    pass
    
#def main(link):
#    response=getviews(link)+getresult("hello world")[0]["label"] + str(math.trunc(getresult("hello world")[0]["score"])*100/100)
#    return  response #result #soup.prettify()



import sqlite3
import huggingface_hub
import pandas as pd
import shutil
import os
import datetime
from apscheduler.schedulers.background import BackgroundScheduler

import random
import time

DB_FILE = "./reviews.db"

TOKEN = os.environ.get('HF_KEY')

repo = huggingface_hub.Repository(
    local_dir="data",
    repo_type="dataset",
    clone_from="CognitiveScience/csdhdata",
    use_auth_token=TOKEN
)
repo.git_pull()

# Set db to latest
shutil.copyfile("./data/reviews.db", DB_FILE)

# Create table if it doesn't already exist

db = sqlite3.connect(DB_FILE)
try:
    db.execute("SELECT * FROM reviews").fetchall()
    db.close()
except sqlite3.OperationalError:
    db.execute(
        '''
        CREATE TABLE reviews (id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
                              created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP NOT NULL,
                              name TEXT, review INTEGER, comments TEXT)
        ''')
    db.commit()
    db.close()


def get_latest_reviews(db: sqlite3.Connection):
    reviews = db.execute("SELECT * FROM reviews ORDER BY id DESC limit 10").fetchall()
    total_reviews = db.execute("Select COUNT(id) from reviews").fetchone()[0]
    reviews = pd.DataFrame(reviews, columns=["id", "date_created", "name", "review", "comments"])
    return reviews, total_reviews


def add_review(name: str, review: int, comments: str):
    db = sqlite3.connect(DB_FILE)
    cursor = db.cursor()
    cursor.execute("INSERT INTO reviews(name, review, comments) VALUES(?,?,?)", [name, review, comments])
    db.commit()
    reviews, total_reviews = get_latest_reviews(db)
    db.close()
    return reviews, total_reviews

def load_data():
    db = sqlite3.connect(DB_FILE)
    reviews, total_reviews = get_latest_reviews(db)
    db.close()
    return reviews, total_reviews
    
def delete_review(id: int):
    db = sqlite3.connect(DB_FILE)
    cursor = db.cursor()
    cursor.execute("DELETE FROM reviews WHERE id = ?", [id])
    db.commit()
    reviews, total_reviews = get_latest_reviews(db)
    db.close()
    return reviews, total_reviews
    
def delete_all_reviews():
    db = sqlite3.connect(DB_FILE)
    cursor = db.cursor()
    cursor.execute("DELETE FROM reviews")
    db.commit()
    reviews, total_reviews = get_latest_reviews(db)
    db.close()
    return reviews, total_reviews
def celsci(link):
    response=getviews(link)+getresult("hello world")[0]["label"] + str(math.trunc(getresult("hello world")[0]["score"])*100/100)
    return  response #result #soup.prettify()

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():   
                chatbot3 = gr.Chatbot()
                msg3 = gr.Textbox()
                clear3 = gr.ClearButton([msg2, chatbot2])
            
                def respond3(message, chat_history):
                    bot_message = random.choice(["How are you3?", "I love you3", "I'm very hungry3"])
                    chat_history.append((message, bot_message))
                    time.sleep(2)
                    
                    return "", chat_history
            
                msg2.submit(respond3, [msg3, chatbot3], [msg3, chatbot3])
            
    with gr.Row():
        with gr.Column():            
                name = gr.Textbox(label="Name", placeholder="What is your name?")
                review = gr.Radio(label="How satisfied are you with using gradio?", choices=[1, 2, 3, 4, 5])
                comments = gr.Textbox(label="Comments", lines=10, placeholder="Do you have any feedback on gradio?")
                submit = gr.Button(value="Submit Feedback")
        with gr.Column():
                chatbot = gr.Chatbot()
                msg = gr.Textbox()
                clear = gr.ClearButton([msg, chatbot])
            
                def respond(message, chat_history):
                    bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
                    chat_history.append((message, bot_message))
                    time.sleep(2)
                    
                    return "", chat_history
            
                msg.submit(respond, [msg, chatbot], [msg, chatbot])
        
        with gr.Column():
                chatbot2 = gr.Chatbot()
                msg2 = gr.Textbox()
                clear2 = gr.ClearButton([msg2, chatbot2])
            
                def respond2(message, chat_history):
                    bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
                    chat_history.append((message, bot_message))
                    time.sleep(2)
                    
                    return "", chat_history
            
                msg2.submit(respond2, [msg2, chatbot2], [msg2, chatbot2])

        with gr.Column():
                submitsave = gr.Button(value="Save")
            
                def backup_db2():
                    shutil.copyfile(DB_FILE, "./data/reviews.db")
                    db = sqlite3.connect(DB_FILE)
                    reviews = db.execute("SELECT * FROM reviews").fetchall()
                    pd.DataFrame(reviews).to_csv("./data/reviews.csv", index=False)
                    print("updating db")
                    repo.push_to_hub(blocking=False, commit_message=f"Updating data at {datetime.datetime.now()}")
                submit.click(backup_db2)
        with gr.Column():
            with gr.Box():
                gr.Code(
                value="""def hello_world():
            return "Hello, world!"
            
        print(hello_world())""",
                language="python",
                interactive=True,
                show_label=False,
            )
                gr.Markdown("Based on dataset [here](https://huggingface.co/datasets/freddyaboulton/gradio-reviews)")
                data = gr.Dataframe()
            count = gr.Number(label="Total number of reviews")
            
    submit.click(add_review, [name, review, comments], [data, count])

    record2del = gr.Textbox(label="Id: ", lines=1, placeholder="to delete?")

    submit2 = gr.Button(value="Delete Review")
    id_input = gr.Number(value=202, visible=False)
    submit2.click(delete_review, id_input)



    submit3 = gr.Button(value="Delete All Reviews")
    submit3.click(delete_all_reviews)


    demo.load(load_data, None, [data, count])

def backup_db():
    shutil.copyfile(DB_FILE, "./data/reviews.db")
    db = sqlite3.connect(DB_FILE)
    reviews = db.execute("SELECT * FROM reviews").fetchall()
    pd.DataFrame(reviews).to_csv("./data/reviews.csv", index=False)
    print("updating db")
    repo.push_to_hub(blocking=False, commit_message=f"Updating data at {datetime.datetime.now()}")


scheduler = BackgroundScheduler()
scheduler.add_job(func=backup_db, trigger="interval", seconds=60)
scheduler.start()


demo.launch()