def get_predictions():
videos = []
novos_videos_json = "novos_videos.json"
if not os.path.exists(novos_videos_json):
run_backend.update_db()
last_update = os.path.getmtime(novos_videos_json) * 1e9
# if time.time_ns() - last_update > (720*3600*1e9): # aprox. 1 mes
# run_backend.update_db()
with open("novos_videos.json", 'r') as data_file:
for line in data_file:
line_json = json.loads(line)
videos.append(line_json)
predictions = []
for video in videos:
# print(video)
# print(video['video_id'])
predictions.append((video['video_id'], video['title'], float(video['score'])))
predictions = sorted(predictions, key=lambda x: x[2], reverse=True)[:30]
predictions_formatted = []
for e in predictions:
# print(e)
predictions_formatted.append(
"<tr><th><a href=\"{link}\">{title}</a></th><th>{score}</th></tr>".format(title=e[1], link=e[0],
score=e[2]))
return '\n'.join(predictions_formatted), last_update
def get_predictions():
videos = []
novos_videos_json = "data/novos_videos.json"
if not os.path.exists(novos_videos_json):
run_backend.update_db()
last_update = os.path.getmtime(novos_videos_json) * 1e9
#if time.time_ns() - last_update > (720*3600*1e9): #when the range is > than aprox. 1 month
# run_backend.update_db()
with open(novos_videos_json, 'r') as data_files:
for line in data_files:
line_json = json.loads(line)
videos.append(line_json)
predictions = []
for video in videos:
predictions.append(
(video['video_id'], video['title'], float(video['score'])))
#Getting only the 30 recomendations with highest score
predictions = sorted(predictions, key=lambda x: x[2], reverse=True)[:30]
#Creating html code for each video recommended
predictions_formatted = []
for e in predictions:
predictions_formatted.append(
"<tr><th><a href=\"{link}\">{title}<a/></th><th>{score}<\th><\tr>".
format(title=e[1], link=e[0], score=e[2]))
return '\n'.join(predictions_formatted), last_update
def get_predictions():
videos = []
new_videos_json = 'new_videos.json'
if not os.path.exists(new_videos_json):
run_backend.update_db()
with open(new_videos_json, 'r') as data_file:
for line in data_file:
line_json = json.loads(line)
videos.append(line_json)
predictions = []
for video in videos:
print(video)
predictions.append(
(video['video_id'], video['title'], float(video['score'])))
predictions = sorted(predictions, key=lambda x: x[2], reverse=True)[:30]
predictions_formatted = []
for prediction in predictions:
print(prediction)
predictions_formatted.append(
'<tr><th><a href=\'{link}\'>{title}</a></th><th>{score}</th></tr>'.
format(link=prediction[0],
title=prediction[1],
score=prediction[2]))
return '\n'.join(predictions_formatted)
def get_predictions():
new_videos_csv = "new_videos.csv"
if not os.path.exists(new_videos_csv):
print("inicio update db")
run_backend.update_db(queries, limit_page)
last_update = os.path.getmtime(new_videos_csv) * 1e9
predictions = []
new_videos = pd.read_csv("new_videos.csv", sep=";")
new_videos = new_videos.drop_duplicates()
for row in new_videos.iterrows():
predictions.append((row[1]["video_id"], row[1]["title"], float(row[1]["score"]), row[1]["thumb"]))
predictions = sorted(predictions, key=lambda x: x[2], reverse=True)[:30]
predictions_formatted = []
carrousel_formatted = []
for n, e in enumerate(predictions):
if n == 0:
carrousel_formatted.append("""<div class="carousel-item active">
<iframe width="600" height="337" class="iframe-video" src=\"{embed}\" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<div>
<p>Score: <b>{score}</b></p>
</div>
</div>""".format(embed=e[0], score=e[2]))
elif n !=0 and n <= 4:
carrousel_formatted.append("""<div class="carousel-item">
<iframe width="600" height="337" class="iframe-video" src=\"{embed}\" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<div>
<p>Score: <b>{score}</b></p>
</div>
</div>""".format(embed=e[0], score=e[2]))
predictions_formatted.append("""<tr>
<th><a href=\"{link}\">{title}</a></th>
<th>{score}</th>
</tr>""".format(title=e[1], link=e[0], score=e[2]))
return "\n".join(predictions_formatted), "\n".join(carrousel_formatted), last_update
def get_predictions():
cars = []
new_cars_json = "new_cars.json"
if not os.path.exists(new_cars_json):
run_backend.update_db()
last_update = os.path.getmtime(new_cars_json)*1e9
#if time.time_ns() - last_update > (60*60*24*1e9): #aprox 1 day
#run_backend.update_db()
#last_update = datetime.datetime.today().strftime('%Y-%m-%d')
with open("new_cars.json", "r") as data_file:
for line in data_file:
line_json = json.loads(line)
cars.append(line_json)
predictions = []
for car in cars:
predictions.append(
{"car_link":car['car_id'],
"car":car['car'],
"score": float(car['score'])
}
)
predictions = sorted(predictions, key = lambda x: x['score'], reverse = True)[:30]
return predictions, last_update
def get_predictions():
events = []
# Using a file as DB in this first version
run_backend.update_db()
with open("new_events.json", 'r') as data_file:
for line in data_file:
line_json = json.loads(line)
events.append(line_json)
predictions = []
for event in events:
predictions.append((event['link'], event['name'],
float(event['prediction']), event['platform']))
# reverse=True: descending order
predictions = sorted(predictions, key=lambda x: x[2], reverse=True)[:50]
predictions_formatted = []
for event in predictions:
predictions_formatted.append("<tr><th><a href=\"{link}\">{name}</a></th><th>{prediction}, {rounded}</th><th>{platform}</th></tr>".format(link=event[0], \
name=event[1], prediction=event[2], rounded=round(event[2]), platform=event[3]))
return '\n'.join(predictions_formatted)
def get_predictions():
videos = []
novos_videos_json = "novos_videos.json"
if not os.path.exists(novos_videos_json):
run_backend.update_db()
last_update = os.path.getmtime(novos_videos_json) * 1e9
#if time.time_ns() - last_update > (720*3600*1e9): # aprox. 1 mes
# run_backend.update_db()
with open("novos_videos.json", 'r') as data_file:
for line in data_file:
line_json = json.loads(line)
videos.append(line_json)
predictions = []
for video in videos:
# depois que cria a lista de tuples
predictions.append(
(video['video_id'], video['title'], float(video['score'])))
# aplico a função sorted, usar a função no key, vai pegar o ultimo elemento(score) e vai ordenar de trás pra frente, do maior pro menor essa lista, e vou pegar o 30 primeiros
predictions = sorted(predictions, key=lambda x: x[2], reverse=True)[:30]
# agora vou iterar, so que ppr essa lista. E nessa lista vou colcoar o código HTML pra cada um dos videos que estão nessa lista de predictions, temos um HTML muito simples que criar uma tag com hiperlink pra cada video, e coloca nessas th e tr que são as tags pra organizar a tabela, e coloca a score tbm. Isso é o codigo de cada um dos videos que temos na pagina do app.
predictions_formatted = []
for e in predictions:
#print(e)
predictions_formatted.append(
"<tr><th><a href=\"{link}\">{title}</a></th><th>{score}</th></tr>".
format(title=e[1], link=e[0], score=e[2]))
# no fim eu preciso dar um 'join' nessa lista e colocar uma linha, eu coloco cada item dessa lista numa nova linha pra ele formatar corretamente a tabela. É possivel ver isso mais claro no codigo fonte da pagina do app.
# Daqui o get_prediction vai pro main_page e o resto ta explicado la.
return '\n'.join(predictions_formatted), last_update
def get_predictions():
links = []
novos_links_json = "novos_links.json"
## Se o json não existir, buscamos novos links com a função update_db()
if not os.path.exists(novos_links_json):
run_backend.update_db()
last_update_epch = os.path.getmtime(novos_links_json)
last_update = time.ctime(last_update_epch)
with open("novos_links.json", 'r') as data_file:
for line in data_file:
line_json = json.loads(line)
links.append(line_json)
predictions = []
for link in links:
if link in predictions:
continue
else:
predictions.append(
(link['link_id'], link['titulo'], float(link['score'])))
#Ordena do mais interessante ao menos interessante, apresentando apenas 30
predictions = sorted(predictions, key=lambda x: x[2], reverse=True)[:10]
predictions_formatted = []
for e in predictions:
predictions_formatted.append("""
<tr><th>{title}</th><th>{score}</th><th><a href=\"{link}\"><svg class="bi bi-arrow-right-square-fill" width="1em" height="1em" viewBox="0 0 16 16" fill="currentColor" xmlns="http://www.w3.org/2000/svg">
<path fill-rule="evenodd" d="M2 0a2 2 0 0 0-2 2v12a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V2a2 2 0 0 0-2-2H2zm5.646 10.646a.5.5 0 0 0 .708.708l3-3a.5.5 0 0 0 0-.708l-3-3a.5.5 0 1 0-.708.708L9.793 7.5H5a.5.5 0 0 0 0 1h4.793l-2.147 2.146z"/>
</svg></a></th></tr>
""".format(title=e[1], link=e[0], score=e[2]))
predictions_formatted = list(OrderedDict.fromkeys(predictions_formatted))
return '\n'.join(predictions_formatted), last_update
import run_backend
import sqlite3 as sql
if __name__ == "__main__":
with sql.connect(run_backend.db_name) as conn:
c = conn.cursor()
# Create table
c.execute('''CREATE TABLE videos
(title text, video_id text, score real, update_time integer)'''
)
conn.commit()
run_backend.update_db()
import run_backend queries = ['Data+Science', 'Machine+Learning'] limit_page = 2 run_backend.update_db(queries, limit_page)
import run_backend
run_backend.update_db("novos_videos.json")