Update app.py

app.py

@@ -20,7 +20,7 @@ class SentimentAnalyzer:
         openai.api_key=os.getenv("OPENAI_API_KEY")
     def emotion_analysis(self,text):
         prompt = f""" Your task is find the top 3 emotion for this converstion {text}: <Sadness, Happiness, Fear, Disgust, Anger> and it's emotion score for the Mental Healthcare Doctor Chatbot and patient conversation text.\
-        your are analyze the text and provide the output in the following list format heigher to lower order: ["emotion1","emotion2","emotion3"][score1,score2,score3]''' [with top 3 result having the highest score]
+        you are analyze the text and provide the output in the following list format heigher to lower order: ["emotion1","emotion2","emotion3"][score1,score2,score3]''' [with top 3 result having the highest score]
         The scores should be in the range of 0.0 to 1.0, where 1.0 represents the highest intensity of the emotion.
         """
         response = openai.Completion.create(
@@ -36,8 +36,8 @@ class SentimentAnalyzer:
         return message
 
     def analyze_sentiment_for_graph(self, text):
-        prompt = f""" Your task is find the setiments from labels for this converstion {text} : <labels = positive, negative, neutral> and it's sentiment score for the Mental Healthcare Doctor Chatbot and patient conversation text.\
-        your are analyze the text and provide the output in the following json format heigher to lower order: '''["label1","label2","label3"][score1,score2,score3]'''
+        prompt = f""" Your task is find the setiments for this converstion {text} : <labels = positive, negative, neutral> and it's sentiment score for the Mental Healthcare Doctor Chatbot and patient conversation text.\
+        you are analyze the text and provide the output in the following json format heigher to lower order: '''["label1","label2","label3"][score1,score2,score3]'''
         """
         response = openai.Completion.create(
         model="text-davinci-003",