Update app.py

app.py

@@ -26,7 +26,7 @@ class SentimentAnalyzer:
         return sentiment_scores_str
     def emotion_analysis(self,text):
         prompt = f""" Your task is find the top 1 emotion : <Sadness, Happiness, Joy, Fear, Disgust, Anger> and it's emotion score of the text.\
-        your are analyze the text and provide the output in the following dict format: '''emotions: scores''' [with top 1 result having the highest score]
+        your are analyze the text and provide the output in the following list format: [emotions][scores]''' [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}'''
         """
@@ -57,12 +57,19 @@ class SentimentAnalyzer:
 
     def emotion_analysis_for_graph(self,text):
 
-        list_of_emotion=text.split(":")
-        label=list_of_emotion[0]
-        score=list_of_emotion[1]
-        score_dict={
-          label:score
-        }
+        # list_of_emotion=text.split(":")
+        # label=list_of_emotion[0]
+        # score=list_of_emotion[1]
+        # score_dict={
+        #   label:score
+        # }
+        # print(score_dict)
+        emotions_match = re.search(r"<(.*?)>", text)
+        emotions = emotions_match.group(1).split(", ")
+        scores_match = re.search(r"\[.*?\]\[(.*?)\]", text)
+        scores = scores_match.group(1).split(", ")
+        scores = [float(score.strip()) for score in scores]
+        score_dict={"Emotion": emotions, "Score": scores}
         print(score_dict)
         return score_dict
 
@@ -128,7 +135,11 @@ class LangChain_Document_QA:
         fig.update_traces(texttemplate='%{x:.2f}%', textposition='outside')
         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')
+        fig.update_traces(texttemplate='%{x:.2f}%', textposition='outside')
+        fig.update_layout(height=500, width=200)
+        return fig
     def _history_of_chat(self):
         history = history_state.value
         formatted_history = ""
@@ -199,7 +210,7 @@ class LangChain_Document_QA:
 
         customer_emotion_score = sentiment.emotion_analysis_for_graph(customer_emotion)
 
-        customer_emotion_fig=self._display_graph(customer_emotion_score)
+        customer_emotion_fig=self._display_graph_emotion(customer_emotion_score)
         customer_emotion_fig.update_layout(title="Emotion Analysis",width=770)
         print("scores :{}",scores)
         print("customer_fig :{}",customer_fig)