Update app.py

app.py

@@ -158,14 +158,15 @@ class LangChain_Document_QA:
  except:
  pass
 
- prompt = f"""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>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. 
- Chat History:['''{history}''']
- Patient: ['''{text}''']
- Perform as Mental Healthcare Doctor Chatbot
+ prompt = f"""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> 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. \
+ Chat History:['''{history}'''] 
+ Patient: ['''{text}''']
+ Perform as Mental Healthcare Doctor Chatbot
  """
  response = openai.Completion.create(
  model="text-davinci-003",