Update app.py

app.py

@@ -25,8 +25,8 @@ class SentimentAnalyzer:
         sentiment_scores_str = f"Positive: {sentiment_scores['positive']:.2f}, Neutral: {sentiment_scores['neutral']:.2f}, Negative: {sentiment_scores['negative']:.2f}"
         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 format: emotion: score [with top 1 result having the highest score]
+        prompt = f""" Your task is find the top 3 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 format: \{emotions: scores\} [with top 3 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}'''
         """
@@ -55,16 +55,16 @@ class SentimentAnalyzer:
         }
         return sentiment_scores
 
-    def emotion_analysis_for_graph(self,text):
+    # 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:float(score)
-        }
-        print(score_dict)
-        return score_dict
+    #     list_of_emotion=text.split(":")
+    #     label=list_of_emotion[0]
+    #     score=list_of_emotion[1]
+    #     score_dict={
+    #       label:float(score)
+    #     }
+    #     print(score_dict)
+    #     return score_dict
 
 
 class Summarizer:
@@ -154,11 +154,12 @@ class LangChain_Document_QA:
         except:
           pass
 
-        prompt = f"""As an empathic AI Mental Healthcare Doctor Chatbot, provide effective solutions to patients' mental health concerns. 
+        prompt = f"""As an empathic AI Mental Healthcare Doctor Chatbot, provide effective solutions to patients' mental health concerns. \
             first start the conversation ask existing patient or new patient. if new patient get name,age,gender,contact,address from the patient and start. 
             if existing customer get name,age,gender,contact,address details and start the chat about existing issues and current issues.
               if patient say thanking tone message to end the conversation with a thanking greeting when the patient expresses gratitude.
               Analyse the patient json If asked for information take it from {patient_details}
+              you first get patient details : <get name,age,gender,contact,address from patient> if not match patient json information start new chat else match patient json information ask previous: <description,symptoms,diagnosis,treatment talk about patient> 
               Chat History:[{history}]
               Patient: [{text}]
               Perform as Mental Healthcare Doctor Chatbot
@@ -264,7 +265,7 @@ class LangChain_Document_QA:
           emptyBtn.click(self.clear_func,[],[])
           emptyBtn.click(lambda: None, None, chatbot, queue=False)
 
-          Sentiment_btn.click(self._on_sentiment_btn_click,[],[txt5,plot,plot_3])
+          Sentiment_btn.click(self._on_sentiment_btn_click,[],[txt5])
 
       demo.title = "AI Mental Healthcare ChatBot"
       demo.launch()