import gradio as gr from bs4 import BeautifulSoup import requests from acogsphere import acf from bcogsphere import bcf import math 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 = "./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() #TOKEN2 = HF_TOKEN #login(token=TOKEN2) # Set db to latest #shutil.copyfile("./data/reviews01.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, rate INTEGER, celsci TEXT) ''') db.commit() db.close() 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", "rate", "celsci"]) return reviews, total_reviews def ccogsphere(name: str, rate: int, celsci: str): db = sqlite3.connect(DB_FILE) cursor = db.cursor() cursor.execute("INSERT INTO reviews(name, rate, celsci) VALUES(?,?,?)", [name, rate, celsci]) 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": [name,celsci]}) #demo.load() 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 load_data(): db = sqlite3.connect(DB_FILE) reviews, total_reviews = get_latest_reviews(db) db.close() return reviews, total_reviews 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, "./reviews1.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 scheduler2 = BackgroundScheduler() scheduler2.add_job(func=run_actr, trigger="interval", seconds=36) scheduler2.start() scheduler2 = BackgroundScheduler() scheduler2.add_job(func=backup_db, trigger="interval", seconds=3633000) scheduler2.start() scheduler3 = BackgroundScheduler() scheduler3.add_job(func=backup_db_csv, trigger="interval", seconds=3666000) scheduler3.start() demo.launch()