Update app.py

app.py

@@ -58,16 +58,8 @@ class SentimentAnalyzer:
         return message
 
     def analyze_sentiment_for_graph(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]
-        # }
         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 format heigher to lower order: \"\"\"<label1: score1, label2:score2, label3:score3>\"\"\" \
+        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(
@@ -81,7 +73,8 @@ class SentimentAnalyzer:
         )
 
         # Extract the generated text
-        sentiment_scores = response.choices[0].text.strip()
+        sentiment_scores_list = response.choices[0].text.strip()
+        sentiment_scores={sentiment_scores_list}
         print(sentiment_scores)
         return sentiment_scores