data for bar plot
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@ -8,4 +8,25 @@ tokenizer = AutoTokenizer.from_pretrained('application/tokenizers/sentiment_toke
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def sentiment_prediction(data):
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pipe = pipeline('text-classification', model=model, tokenizer = tokenizer)
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result = pipe(data)
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return result
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return result
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def count_predictions(predictions):
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l0 = 0
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l1 = 0
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l2 = 0
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all = {}
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for i in predictions:
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if i['label'] == 'LABEL_0':
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l0 += 1
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if i['label'] == 'LABEL_1':
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l1 += 1
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if i['label'] == 'LABEL_2':
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l2 += 1
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all['positive'] = l1
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all['negative'] = l0
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all['neutral'] = l2
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return all
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@ -3,12 +3,14 @@ from flask import(
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jsonify,
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Blueprint,
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)
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from application.functions.sentiment import sentiment_prediction
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from application.functions.sentiment import sentiment_prediction, count_predictions
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sentiment_service = Blueprint("sentiment_service", __name__)
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@sentiment_service.route("/get_sentiment_data", methods=['GET'])
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def get_data():
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data = request.get_json()
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result = sentiment_prediction(data['sentences'])
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return result
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predicitons = sentiment_prediction(data['sentences']) #predykcje
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count_labels = count_predictions(predicitons) #dane do wykresu
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return jsonify({"predictions": predicitons, "count_labels": count_labels})
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