Update app.py

app.py

@@ -19,8 +19,8 @@ class SentimentAnalyzer:
  # self.model="facebook/bart-large-mnli"
  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, Joy, 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 1 result having the highest score]
+ 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]
  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(
@@ -36,7 +36,7 @@ class SentimentAnalyzer:
  return message
 
  def analyze_sentiment_for_graph(self, text):
- prompt = f""" Your task is find the top 3 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.\
+ 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]'''
  """
  response = openai.Completion.create(