Update app.py

app.py

@@ -59,7 +59,7 @@ class SentimentAnalyzer:
 
     def analyze_sentiment_for_graph(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 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","label2","label3"][score1,score2,score3]''' \
         analyze the text : '''{text}'''
         """
         response = openai.Completion.create(
@@ -74,9 +74,15 @@ class SentimentAnalyzer:
 
         # Extract the generated text
         sentiment_scores = response.choices[0].text.strip()
-        #sentiment_scores={sentiment_scores_list}
-        print(sentiment_scores)
-        return sentiment_scores
+        start_index = sentiment_scores.find("[")
+        end_index = sentiment_scores.find("]")
+        list1_text = sentiment_scores[start_index + 1: end_index]
+        list2_text = sentiment_scores[end_index + 2:-1]
+        emotions = list(map(str.strip, list1_text.split(",")))
+        scores = list(map(float, list2_text.split(",")))
+        score_dict={"Labels": emotions, "Score": scores}
+        print(score_dict)
+        return score_dict
 
     def emotion_analysis_for_graph(self,text):
         start_index = text.find("[")
@@ -143,9 +149,7 @@ class LangChain_Document_QA:
         return summary
 
     def _display_graph(self,sentiment_scores):
-        labels = sentiment_scores.keys()
-        scores = sentiment_scores.values()
-        fig = px.bar(x=scores, y=labels, orientation='h', color=labels, color_discrete_map={"Negative": "red", "Positive": "green", "Neutral": "gray"})
+        fig = px.bar(sentiment_scores, orientation='h', color=labels, color_discrete_map={"Negative": "red", "Positive": "green", "Neutral": "gray"})
         fig.update_traces(texttemplate='%{x:.2f}%', textposition='outside')
         fig.update_layout(height=500, width=200)
         return fig
@@ -209,7 +213,6 @@ class LangChain_Document_QA:
 
 
     def _text_box(self,customer_emotion,customer_sentiment_score):
-        customer_score = ", ".join([f"{key}: {value:.2f}" for key, value in customer_sentiment_score.items()])
         return f"customer_emotion:{customer_emotion}\nCustomer_sentiment_score:{customer_sentiment_score}"
 
     def _on_sentiment_btn_click(self):