Update app.py

app.py

@@ -18,28 +18,6 @@ class SentimentAnalyzer:
     def __init__(self):
         # self.model="facebook/bart-large-mnli"
         openai.api_key=os.getenv("OPENAI_API_KEY")
-    def analyze_sentiment(self, text):
-        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] \}>\"\"\" \
-        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 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]
@@ -81,7 +59,7 @@ class SentimentAnalyzer:
         list2_text = sentiment_scores[end_index + 2:-1]
         sentiment = list(map(str.strip, list1_text.split(",")))
         scores = list(map(float, list2_text.split(",")))
-        score_dict={"Labels": sentiment, "Score": scores}
+        score_dict={"Sentiment": sentiment, "Score": scores}
         print(score_dict)
         return score_dict
 
@@ -151,10 +129,11 @@ class LangChain_Document_QA:
 
     def _display_graph(self,sentiment_scores):
         df = pd.DataFrame(sentiment_scores)
-        fig = px.bar(df, x='Score', y='Labels', orientation='h', labels={'Score': 'Score', 'Labels': 'Sentiment'})
+        fig = px.bar(df, x='Score', y='Sentiment', orientation='h', labels={'Score': 'Score', 'Labels': 'Sentiment'})
         fig.update_layout(height=500, width=200)
         return fig
     def _display_graph_emotion(self,customer_emotion_score):
+        
         fig = px.pie(customer_emotion_score, values='Score', names='Emotion', title='Emotion Distribution', hover_data=['Score'])
         #fig.update_traces(texttemplate='Emotion', textposition='outside')
         fig.update_layout(height=500, width=200)
@@ -214,11 +193,12 @@ class LangChain_Document_QA:
 
 
     def _text_box(self,customer_emotion,customer_sentiment_score):
-        sentiment_data = customer_sentiment_score
-        emotion_data = customer_emotion   
-        sentiment_str = ', '.join([f'{label}: {score}' for label, score in zip(sentiment_data['Labels'], sentiment_data['Score'])])
-        emotion_str = ', '.join([f'{emotion}: {score}' for emotion, score in zip(emotion_data['Emotion'], emotion_data['Score'])])
-        return f"Sentiment: {sentiment_str}, Emotion: {emotion_str}"
+        # sentiment_data = customer_sentiment_score
+        # emotion_data = customer_emotion   
+        # sentiment_str = ', '.join([f'{label}: {score}' for label, score in zip(sentiment_data['Labels'], sentiment_data['Score'])])
+        # emotion_str = ', '.join([f'{emotion}: {score}' for emotion, score in zip(emotion_data['Emotion'], emotion_data['Score'])])
+        # return f"Sentiment: {sentiment_str}, Emotion: {emotion_str}"
+        return f"Sentiment: \n{customer_sentiment_score[0]} \n {customer_sentiment_score[1]} Emotion: \n{customer_emotion[0]}\n{customer_emotion[1]}\n"
 
     def _on_sentiment_btn_click(self):
         client=self._history_of_chat()