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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
#repo = huggingface_hub.HfRepository(repo_id="lysandre/test-model", token=token)
# Clone the repository to a local directory
#repo.clone_from_hub()
#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 (datetime('now', 'localtime', '+3 hours')) NOT NULL,
# name TEXT, view TEXT, duration TEXT)
# ''')
# db.commit()
# db.close()
# db = sqlite3.connect(DB_FILE)
# #created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP NOT NULL,
def get_latest_reviews(celsci2): #db: sqlite3.Connection):
try:
celsci2=ecf(celsci2)
df=pd.DataFrame.from_dict(celsci2["videos"])
reviews = df.DataFrame(reviews, columns=["id", "date_created", "name", "view", "duration"])
except: # sqlite3.OperationalError:
df=pd.DataFrame()
print ("db ...")
#reviews = db.execute("SELECT * FROM reviews ORDER BY id DESC limit 100").fetchall()
#total_reviews = db.execute("Select COUNT(id) from reviews").fetchone()[0]
total_reviews=reviews.count()[0]
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+" Video #"+str(index+1), view, duration])
#db.commit()
reviews=df
total_reviews=reviews.count()[0]
#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:
df=pd.DataFrame()
print ("db ...")
#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)
reviews2=df
total_reviews2=reviews2.count()[0]
return reviews2, total_reviews2
#def load_data():
# #db = sqlite3.connect(DB_FILE)
# reviews, total_reviews = get_latest_reviews()
# #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(get_latest_reviews, celsci, [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(celsci):
#if name=="abc":
#run_code()
#demo.load(get_latest_reviews, celsci, [data, count])
#return "Hello " + name + "!"
demo.launch() |