app.py

import gradio as gr
from bs4 import BeautifulSoup
import requests
from acogsphere import acf
from bcogsphere import bcf
from ecogsphere import ecf

import pandas as pd 
import math
import json

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
#import requests

from huggingface_hub import hf_hub_download

#hf_hub_download(repo_id="CogSphere/aCogSphere", filename="./reviews.csv")

from huggingface_hub import login
from datasets import load_dataset

#dataset = load_dataset("csv", data_files="./data.csv")


DB_FILE = "./reviewsitr.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()

#TOKEN2 = HF_TOKEN


#login(token=TOKEN2)

# Set db to latest
#shutil.copyfile("./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.execute("SELECT * FROM reviews2").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, view TEXT, duration TEXT)
        ''')
    db.commit()
    db.close()

    db = sqlite3.connect(DB_FILE)

def get_latest_reviews(db: sqlite3.Connection):
    reviews = db.execute("SELECT * FROM reviews ORDER BY id DESC limit 100").fetchall()
    total_reviews = db.execute("Select COUNT(id) from reviews").fetchone()[0]
    reviews = pd.DataFrame(reviews, columns=["id", "date_created", "name", "view", "duration"])
    return reviews, total_reviews

def get_latest_reviews2(db: sqlite3.Connection):
    reviews2 = db.execute("SELECT * FROM reviews2 ORDER BY id DESC limit 100").fetchall()
    total_reviews2 = db.execute("Select COUNT(id) from reviews2").fetchone()[0]
    reviews2 = pd.DataFrame(reviews2, columns=["id","title", "link","channel", "description", "views", "uploaded", "duration", "durationString"])
    return reviews2, total_reviews2
    
def ccogsphere(name: str, rate: int, celsci: str):
    db = sqlite3.connect(DB_FILE)
    cursor = db.cursor()
      
    #try:
    celsci2=celsci.split()
    print("split",celsci2,celsci)
    celsci2=celsci2[0] + "+" + celsci2[1]
    celsci2=ecf(celsci2)
    df=pd.DataFrame.from_dict(celsci2["videos"])
    celsci2=json.dumps(celsci2["videos"])
    for index, row in df.iterrows():
        view = str(row["views"])
        duration = str(row["duration"])
        print(view, duration)
        #celsci=celsci+celsci2
        cursor.execute("INSERT INTO reviews(name, view, duration) VALUES(?,?,?)", [celsci+str(index+1), view, duration])
        db.commit()
    
    reviews, total_reviews = get_latest_reviews(db)
    db.close()
    r = requests.post(url='https://ccml-persistent-data2.hf.space/api/predict/', json={"data": [celsci + " ", celsci2]}) 

    return reviews, total_reviews

def run_actr():
    from python_actr import log_everything

    #code1="tim = MyAgent()"
    #code2="subway=MyEnv()"
    #code3="subway.agent=tim"
    #code4="log_everything(subway)"]
    from dcogsphere import RockPaperScissors
    from dcogsphere import ProceduralPlayer
    #from dcogsphere import logy

    env=RockPaperScissors()
    env.model1=ProceduralPlayer()
    env.model1.choice=env.choice1
    env.model2=ProceduralPlayer()
    env.model2.choice=env.choice2
    env.run()

def run_ecs(inp):
    try:
        result=ecf(inp)
        df=pd.DataFrame.from_dict(result["videos"])
    except sqlite3.OperationalError:
        print ("db error")
    
    df=df.drop(df.columns[4], axis=1)

    db = sqlite3.connect(DB_FILE)
    #cursor = db.cursor()
    #cursor.execute("INSERT INTO reviews2(title, link, thumbnail,channel, description, views, uploaded, duration, durationString) VALUES(?,?,?,?,?,?,?,?,?)", [title, link, thumbnail,channel, description, views, uploaded, duration, durationString])
    df.to_sql('reviews2', db, if_exists='replace', index=False)

    #db.commit()
    reviews2, total_reviews2 = get_latest_reviews(db)
    db.close()
    #print ("print000", total_reviews2,reviews2)
    return reviews2, total_reviews2
    
    
def load_data():
    db = sqlite3.connect(DB_FILE)
    reviews, total_reviews = get_latest_reviews(db)
    db.close()
    return reviews, total_reviews
def load_data2():
    db = sqlite3.connect(DB_FILE)
    reviews2, total_reviews2 = get_latest_reviews2(db)
    db.close()
    return reviews2, total_reviews2
    
css="footer {visibility: hidden}"
# Applying style to highlight the maximum value in each row
#styler = df.style.highlight_max(color = 'lightgreen', axis = 0)
with gr.Blocks(css=css) as demo:
    with gr.Row():
        with gr.Column():
            data = gr.Dataframe() #styler)
            count = gr.Number(label="Rates!")
    with gr.Row():
        with gr.Column():
            name = gr.Textbox(label="a") #, placeholder="What is your name?")
            rate =  gr.Textbox(label="b") #, placeholder="What is your name?") #gr.Radio(label="How satisfied are you with using gradio?", choices=[1, 2, 3, 4, 5])
            celsci = gr.Textbox(label="c") #, lines=10, placeholder="Do you have any feedback on gradio?")
            #run_actr()
            submit = gr.Button(value=".")            
            submit.click(ccogsphere, [name, rate, celsci], [data, count])
            demo.load(load_data, None, [data, count])
            @name.change(inputs=name, outputs=celsci,_js="window.location.reload()")
            @rate.change(inputs=rate, outputs=name,_js="window.location.reload()")
            @celsci.change(inputs=celsci, outputs=rate,_js="window.location.reload()")  
            
            def secwork(name):
                #if name=="abc":
                #run_code()
                load_data()
                #return "Hello " + name + "!"
def backup_db():
    shutil.copyfile(DB_FILE, "./reviews.db")
    db = sqlite3.connect(DB_FILE)
    reviews = db.execute("SELECT * FROM reviews").fetchall()
    pd.DataFrame(reviews).to_csv("./reviews.csv", index=False)
    print("updating db")
    repo.push_to_hub(blocking=False, commit_message=f"Updating data at {datetime.datetime.now()}")
    
def backup_db_csv():
    shutil.copyfile(DB_FILE, "./reviews2.db")
    db = sqlite3.connect(DB_FILE)
    reviews = db.execute("SELECT * FROM reviews").fetchall()
    pd.DataFrame(reviews).to_csv("./reviews2.csv", index=False)
    print("updating db csv")
    dataset = load_dataset("csv", data_files="./reviews2.csv")
    repo.push_to_hub("CognitiveScience/csdhdata", blocking=False) #, commit_message=f"Updating data-csv at {datetime.datetime.now()}")
    #path1=hf_hub_url()
    #print (path1)
    #hf_hub_download(repo_id="CogSphere/aCogSphere", filename="./*.csv")
    #hf_hub_download(repo_id="CognitiveScience/csdhdata", filename="./*.db")
    #hf_hub_download(repo_id="CogSphere/aCogSphere", filename="./*.md")
    #hf_hub_download(repo_id="CognitiveScience/csdhdata", filename="./*.md")


#def load_data2():
#    db = sqlite3.connect(DB_FILE)
#    reviews, total_reviews = get_latest_reviews(db)
#    #db.close()
#    demo.load(load_data,None, [reviews, total_reviews])
#    #return reviews, total_reviews
    
#scheduler1 = BackgroundScheduler()
#scheduler1.add_job(func=run_actr, trigger="interval", seconds=10)
#scheduler1.start()

scheduler2 = BackgroundScheduler()
scheduler2.add_job(func=backup_db, trigger="interval", seconds=3633)
scheduler2.start()

scheduler3 = BackgroundScheduler()
scheduler3.add_job(func=backup_db_csv, trigger="interval", seconds=3666)
scheduler3.start()

demo.launch()