Spaces:

Andreagus
/

financial_news_fast_inference

Sleeping

App Files Files Community

financial_news_fast_inference / aggregator.py

Andreagus

Update aggregator.py

1d9007f verified 4 months ago

raw history blame contribute delete

No virus

8.91 kB

	from bezinga_caller import bezinga_get
	from finub_caller import get_finhub
	from marketaux_caller import get_marketaux
	from newsapi_caller import get_newsapi
	from newsdata_caller import get_newsdata
	from vantage_caller import get_vantage
	from transformers import pipeline


	def get_articles_sentiment(ticker, model):
	pipe = pipeline("text-classification", model=model)

	# getting a list of article of given ticket from different sources
	try:
	bezinga_list = bezinga_get(ticker)
	print("BEZINGA returned")
	bezinga_results = pipe(bezinga_list)
	print("evaluated")
	except Exception as e:
	print(e)
	bezinga_results = []

	try:
	newsapi_list = get_newsapi(ticker)
	print("NEWSAPI returned")
	newsapi_results = pipe(newsapi_list)
	print("evaluated")
	except Exception as e:
	print(e)
	newsapi_results = []
	try:
	newsdata_list = get_newsdata(ticker)
	print("NEWSDATA returned")
	newsdata_results = pipe(newsdata_list)
	print("evaluated")
	except Exception as e:
	print(e)
	newsdata_results = []

	try:
	finhub_list = get_finhub(ticker)
	print("FINHUB returned")
	finhub_results = pipe(finhub_list)
	print("evaluated")
	except Exception as e:
	print(e)
	finhub_results = []

	try:
	vantage_list = get_vantage(ticker)
	print("VANTAGE returned")
	vantage_results = pipe(vantage_list)
	print("evaluated")
	except Exception as e:
	print(e)
	vantage_results = []


	# replacing values for calculations and doing the sentiment for each source
	def replace_values(result):
	# Replace values in the label column
	for dict in result:
	if dict["label"] == "LABEL_1":
	dict["label"] = 2
	else:
	dict["label"] = 1


	total_articles = len(bezinga_results) + len(finhub_results) + len(newsapi_results) + len(newsdata_results) + len(vantage_results)

	bezinga_positives = []
	bezinga_negatives = []

	finhub_positives = []
	finhub_negatives = []

	newsapi_positives = []
	newsapi_negatives = []

	newsdata_positives = []
	newsdata_negatives = []

	vantage_positives = []
	vantage_negatives = []

	try:
	replace_values(bezinga_results)

	bezinga_label_mean = float(sum(d['label'] for d in bezinga_results)) / len(bezinga_results)



	for dict in bezinga_results:
	if dict["label"] == 2:
	bezinga_positives.append(dict)
	else:
	bezinga_negatives.append(dict)

	if len(bezinga_positives) > 0:
	bezinga_positive_score_mean = float(sum(d['score'] for d in bezinga_positives)) / len(bezinga_positives)

	if len(bezinga_negatives) > 0:
	bezinga_negative_score_mean = float(sum(d['score'] for d in bezinga_negatives)) / len(bezinga_negatives)
	except Exception as e:
	print(e)

	# finhub
	if len(finhub_results) > 0:
	replace_values(finhub_results)

	finhub_label_mean = float(sum(d['label'] for d in finhub_results)) / len(finhub_results)



	for dict in finhub_results:
	if dict["label"] == 2:
	finhub_positives.append(dict)
	else:
	finhub_negatives.append(dict)

	if len(finhub_positives) > 0:
	finhub_positive_score_mean = float(sum(d['score'] for d in finhub_positives)) / len(finhub_positives)

	if len(finhub_negatives) > 0:
	finhub_negative_score_mean = float(sum(d['score'] for d in finhub_negatives)) / len(finhub_negatives)

	# newsapi
	if len(newsapi_results) > 0:
	replace_values(newsapi_results)

	newsapi_label_mean = float(sum(d['label'] for d in newsapi_results) + 1) / (len(newsapi_results) + 2)

	for dict in newsapi_results:
	if dict["label"] == 2:
	newsapi_positives.append(dict)
	else:
	newsapi_negatives.append(dict)

	if len(newsapi_positives) > 0:
	newsapi_positive_score_mean = float(sum(d['score'] for d in newsapi_positives)) / len(newsapi_positives)

	if len(newsapi_negatives) > 0:
	newsapi_negative_score_mean = float(sum(d['score'] for d in newsapi_negatives)) / len(newsapi_negatives)


	# newsdata
	if len(newsdata_results) > 0:
	replace_values(newsdata_results)

	newsdata_label_mean = float(sum(d['label'] for d in newsdata_results)) / len(newsdata_results)

	for dict in newsdata_results:
	if dict["label"] == 2:
	newsdata_positives.append(dict)
	else:
	newsdata_negatives.append(dict)

	if len(newsdata_positives) > 0:
	newsdata_positive_score_mean = float(sum(d['score'] for d in newsdata_positives)) / len(newsdata_positives)

	if len(newsdata_negatives) > 0:
	newsdata_negative_score_mean = float(sum(d['score'] for d in newsdata_negatives)) / len(newsdata_negatives)

	# vantage
	if len(vantage_results) > 0:
	replace_values(vantage_results)

	vantage_label_mean = float(sum(d['label'] for d in vantage_results)) / len(vantage_results)

	for dict in vantage_results:
	if dict["label"] == 2:
	vantage_positives.append(dict)
	else:
	vantage_negatives.append(dict)

	if len(vantage_positives) > 0:
	vantage_positive_score_mean = float(sum(d['score'] for d in vantage_positives)) / len(vantage_positives)

	if len(vantage_negatives) > 0:
	vantage_negative_score_mean = float(sum(d['score'] for d in vantage_negatives)) / len(vantage_negatives)

	total_positives = len(bezinga_positives) + len(finhub_positives) + len(newsapi_positives) + len(newsdata_positives) + len(vantage_positives)
	total_negatives = len(bezinga_negatives) + len(finhub_negatives) + len(newsapi_negatives) + len(newsdata_negatives) + len(vantage_negatives)

	results_dict = {
	"bezinga": {
	"bezinga_articles": len(bezinga_results),
	"bezinga_positives": len(bezinga_positives),
	"bezinga_negatives": len(bezinga_negatives),
	"bezinga_sentiment_mean": bezinga_label_mean if len(bezinga_results) > 0 else 0,
	"bezinga_positive_score_mean": bezinga_positive_score_mean if len(bezinga_results) > 0 else 0,
	"bezinga_negative_score_mean": bezinga_negative_score_mean if len(bezinga_results) > 0 else 0
	},
	"finhub": {
	"finhub_articles": len(finhub_results),
	"finhub_positives": len(finhub_positives),
	"finhub_negatives": len(finhub_negatives),
	"finhub_sentiment_mean": finhub_label_mean if len(finhub_results) > 0 else 0,
	"finhub_positive_score_mean": finhub_positive_score_mean if len(finhub_results) > 0 else 0,
	"finhub_negative_score_mean": finhub_negative_score_mean if len(finhub_results) > 0 else 0
	},
	"newsapi": {
	"newsapi_articles": len(newsapi_results),
	"newsapi_positives": len(newsapi_positives),
	"newsapi_negatives": len(newsapi_negatives),
	"newsapi_sentiment_mean": newsapi_label_mean if len(newsapi_results) > 0 else 0,
	"newsapi_positive_score_mean": newsapi_positive_score_mean if len(newsapi_results) > 0 else 0,
	"newsapi_negative_score_mean": newsapi_negative_score_mean if len(newsapi_results) > 0 else 0
	},
	"newsdata": {
	"newsdata_articles": len(newsdata_results),
	"newsdata_positives": len(newsdata_positives),
	"newsdata_negatives": len(newsdata_negatives),
	"newsdata_sentiment_mean": newsdata_label_mean if len(newsdata_results) > 0 else 0,
	"newsdata_positive_score_mean": newsdata_positive_score_mean if len(newsdata_results) > 0 else 0,
	"newsdata_negative_score_mean": newsdata_negative_score_mean if len(newsdata_results) > 0 else 0
	},
	"vantage": {
	"vantage_articles": len(vantage_results),
	"vantage_positives": len(vantage_positives),
	"vantage_negatives": len(vantage_negatives),
	"vantage_sentiment_mean": vantage_label_mean if len(vantage_results) > 0 else 0,
	"vantage_positive_score_mean": vantage_positive_score_mean if len(vantage_results) > 0 else 0,
	"vantage_negative_score_mean": vantage_negative_score_mean if len(vantage_results) > 0 else 0
	},
	"total_articles": total_articles,
	"total_positives": total_positives,
	"total_negatives": total_negatives
	}

	return results_dict