Update app.py

app.py

@@ -15,17 +15,34 @@ import plotly.express as px
 
 class SentimentAnalyzer:
     def __init__(self):
-        self.model="facebook/bart-large-mnli"
+        # 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}"
+        # 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 : <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: \"\"\"Positive: <sentiment_scores['positive']:.2f>, Neutral: <sentiment_scores['neutral']:.2f>, Negative: <sentiment_scores['negative']:.2f>\"\"\"
+        analyze the text : '''{text}'''
+        """
+        response = openai.Completion.create(
+        model="text-davinci-003",
+        prompt=prompt,
+        temperature=1,
+        max_tokens=60,
+        top_p=1,
+        frequency_penalty=0,
+        presence_penalty=0
+        )
+
+        # Extract the generated text
+        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 of the Mental Healthcare Doctor Chatbot and patient conversation 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]
         The scores should be in the range of 0.0 to 1.0, where 1.0 represents the highest intensity of the emotion.\
         analyze the text : '''{text}'''