data for bar plot

application/functions/sentiment.py

@@ -8,4 +8,25 @@ tokenizer = AutoTokenizer.from_pretrained('application/tokenizers/sentiment_toke
 def sentiment_prediction(data):
     pipe = pipeline('text-classification', model=model, tokenizer = tokenizer)
     result = pipe(data)
+    
     return result
+
+def count_predictions(predictions):
+    l0 = 0
+    l1 = 0
+    l2 = 0
+    all = {}
+
+    for i in predictions:
+        if i['label'] == 'LABEL_0':
+            l0 += 1
+        if i['label'] == 'LABEL_1':
+            l1 += 1
+        if i['label'] == 'LABEL_2':
+            l2 += 1
+
+    all['positive'] = l1
+    all['negative'] = l0
+    all['neutral'] = l2
+
+    return all

application/services/sentiment_service.py

@@ -3,12 +3,14 @@ from flask import(
     jsonify, 
     Blueprint,
     )
-from application.functions.sentiment import sentiment_prediction
+from application.functions.sentiment import sentiment_prediction, count_predictions
 
 sentiment_service = Blueprint("sentiment_service", __name__)    
 
 @sentiment_service.route("/get_sentiment_data", methods=['GET'])
 def get_data():
     data = request.get_json()
-    result = sentiment_prediction(data['sentences'])
-    return result
+    predicitons = sentiment_prediction(data['sentences']) #predykcje
+    count_labels = count_predictions(predicitons) #dane do wykresu
+
+    return jsonify({"predictions": predicitons, "count_labels": count_labels})