Update app.py

app.py

@@ -18,11 +18,6 @@ class SentimentAnalyzer:
         # self.model="facebook/bart-large-mnli"
         openai.api_key=os.getenv("OPENAI_API_KEY")
     def analyze_sentiment(self, text):
-        # pipe = pipeline("zero-shot-classification", model=self.model)
-        # label=["positive","negative","neutral"]
-        # result = pipe(text, label)
-        # sentiment_scores= {result['labels'][0]:result['scores'][0],result['labels'][1]:result['scores'][1],result['labels'][2]:result['scores'][2]}
-        # sentiment_scores_str = f"Positive: {sentiment_scores['positive']:.2f}, Neutral: {sentiment_scores['neutral']:.2f}, Negative: {sentiment_scores['negative']:.2f}"
         prompt = f""" Your task is find the top 3 setiments : <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 order: \"\"\"
         <\{result['labels'][0]: result['scores'][0], result['labels'][1]: result['scores'][1], result['labels'][2]: result['scores'][2] \}>\"\"\" \
@@ -42,6 +37,8 @@ class SentimentAnalyzer:
         sentiment_scores_str = response.choices[0].text.strip()
         print(sentiment_scores_str)
         return sentiment_scores_str
+
+        
     def emotion_analysis(self,text):
         prompt = f""" Your task is find the top 3 emotion : <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]
@@ -70,8 +67,7 @@ class SentimentAnalyzer:
         #     result['labels'][2]: result['scores'][2]
         # }
         prompt = f""" Your task is find the top 3 setiments : <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 order: \"\"\"
-        <\{result['labels'][0]: result['scores'][0], result['labels'][1]: result['scores'][1], result['labels'][2]: result['scores'][2] \}>\"\"\" \
+        your are analyze the text and provide the output in the following json format heigher to lower order: \"\"\"<label1: score1, label2:score2, label3:score3>\"\"\" \
         analyze the text : '''{text}'''
         """
         response = openai.Completion.create(