Update app.py

app.py

@@ -9,26 +9,31 @@ import requests
 from pydantic import BaseModel, Field
 from typing import Optional
 
-welcomeMessage = "Welcome to the Ford Car Selection AI Agent I am here to talk about your motoring needs, please start by giving me your key requirement"
 
 
-placeHolderPersona1 = """# MISSION
-You are a car sales chatbot focusing on gaining enough information to make a selection. Your mission is to ask questions to help a customer fully articulate their needs in a clear manner.
-When they ask you a question or give you a need ask a suitable follow up question
-
-# RULES
+placeHolderPersona1 = """## Mission Statement
+My mission is to utilize my expertise to aid in the medical triaging process by providing a clear, concise, and accurate assessment of potential arthritis related conditions.
+ 
+# Triaging process 
+Ensure you stay on the topic of asking questions to triage the potential of Rheumatoid arthritis.
 Ask only one question at a time.
 Provide some context or clarification around the follow-up questions you ask.
 Do not converse with the customer.
-Be as concise as possible"""
+Be as concise as possible.
+Do not give a diagnosis """
 
-placeHolderPersona2 = """# MISSION
-You are a car selection expert you will be given a dialoge between a customer and a sales executive. Your job is to select the perfect car for the customer based on their conversation
-# RULES
-You will need to select 1 primary choice car and one secondary choice car
-You will need to give some justification as to why you have chosen these cars
-Do not converse with the customer.
-Be as concise as possible"""
+placeHolderPersona2 = """## Mission
+To analyse a clinical triaging discussion between a patient and AI doctor interactions with a focus on Immunology symptoms, medical history, and test results to deduce the most probable Immunology diagnosis.
+
+## Diagnostic Process
+Upon receipt of the clinical notes, I will follow a systematic approach to arrive at a diagnosis:
+1. Review the patient's presenting symptoms and consider their relevance to immunopathology.
+2. Cross-reference the gathered information with my knowledge base of immunology to identify patterns or indicators of specific immune disorders.
+3. Formulate a diagnosis from the potential conditions.
+4. Determine the most likely diagnosis and assign a confidence score from 1-100, with 100 being absolute certainty.
+
+# Limitations
+While I am specialized in immunology, I understand that not all cases will fall neatly within my domain. In instances where the clinical notes point to a condition outside of my expertise, I will provide the best possible diagnosis with the acknowledgment that my confidence score will reflect the limitations of my specialization in those cases"""
 
 
 
@@ -48,7 +53,7 @@ class ChatRequestClient(BaseModel):
     temperature2: float
 
 def call_chat_api(data: ChatRequestClient):
-    url = "https://agent-builder-api.greensea-b20be511.northeurope.azurecontainerapps.io/chat/"
+    url = "http://127.0.0.1:8000/chat/"
     # Validate and convert the data to a dictionary
     validated_data = data.dict()
     
@@ -62,7 +67,23 @@ def call_chat_api(data: ChatRequestClient):
 
 def genuuid ():
     return uuid.uuid4()
+
+def format_elapsed_time(time):
+    # Format the elapsed time to two decimal places
+    return "{:.2f}".format(time)
+
+def update_history(response):
+    # Initialize the history list if it doesn't exist
+    if 'history' not in st.session_state:
+        st.session_state.history = []
     
+    # Append the agent's response to the history
+    st.session_state.history.append("Agent: " + response['content'])
+def display_history():
+    # Display each item in the history
+    for item in st.session_state.history:
+        st.text_area(label="", value=item, height=75) 
+
 # Title of the application
 # st.image('agentBuilderLogo.png')
 st.title('LLM-Powered Agent Interaction')
@@ -70,18 +91,24 @@ st.title('LLM-Powered Agent Interaction')
 # Sidebar for inputting personas
 st.sidebar.image('agentBuilderLogo.png')
 st.sidebar.header("Agent Personas Design")
-st.sidebar.subheader("Welcome Message")
-welcomeMessage = st.sidebar.text_area("Define Persona 1", value=welcomeMessage, height=150)
+# st.sidebar.subheader("Welcome Message")
+# welcomeMessage = st.sidebar.text_area("Define Triaging Persona", value=welcomeMessage, height=150)
 st.sidebar.subheader("Personas 1 Settings")
-numberOfQuestions = st.sidebar.slider("Number of Questions", min_value=0, max_value=2, step=1, value=5, key='persona1_questions')
-persona1SystemMessage = st.sidebar.text_area("Define Persona 1", value=placeHolderPersona1, height=150)
+numberOfQuestions = st.sidebar.slider("Number of Questions", min_value=0, max_value=10, step=1, value=5, key='persona1_questions')
+persona1SystemMessage = st.sidebar.text_area("Define Triaging Persona", value=placeHolderPersona1, height=150)
+with st.sidebar.expander("See explanation"):
+    st.write("Personas: the individual members of the business function / agent equivalent to employee’s. They have job or personality specific design and are crafted to think, and reason based on this job or personality specific design. They have free reign to feedback to the task however they see most appropriate ")
+    st.image("agentPersona1.png")
 llm1 = st.sidebar.selectbox("Model Selection", ['GPT-4', 'GPT3.5'], key='persona1_size')
 temp1 = st.sidebar.slider("Tempreature", min_value=0.0, max_value=1.0, step=0.1, value=0.6, key='persona1_temp')
 tokens1 = st.sidebar.slider("Tokens", min_value=0, max_value=4000, step=100, value=500, key='persona1_tokens')
 
 # Persona 2
 st.sidebar.subheader("Personas 2 Settings")
-persona2SystemMessage = st.sidebar.text_area("Define Persona 2", value=placeHolderPersona2, height=150)
+persona2SystemMessage = st.sidebar.text_area("Define Selection Persona", value=placeHolderPersona2, height=150)
+with st.sidebar.expander("See explanation"):
+    st.write("Personas: the individual members of the business function / agent equivalent to employee’s. They have job or personality specific design and are crafted to think, and reason based on this job or personality specific design. They have free reign to feedback to the task however they see most appropriate ")
+    st.image("agentPersona2.png")
 llm2 = st.sidebar.selectbox("Model Selection", ['GPT-4', 'GPT3.5'], key='persona2_size')
 temp2 = st.sidebar.slider("Tempreature", min_value=0.0, max_value=1.0, step=0.1, value=0.5, key='persona2_temp')
 tokens2 = st.sidebar.slider("Tokens", min_value=0, max_value=4000, step=100, value=500, key='persona2_tokens')
@@ -101,7 +128,7 @@ if st.button("Send"):
         user_id=user_id,
         user_input=user_input,
         numberOfQuestions=numberOfQuestions,
-        welcomeMessage=welcomeMessage,
+        welcomeMessage="",
         llm1=llm1,
         tokens1=tokens1,
         temperature1=temp1,
@@ -113,9 +140,18 @@ if st.button("Send"):
         temperature2=temp2
     )
     response = call_chat_api(data)
+    
+    
+    st.markdown(f"##### Time take: {format_elapsed_time(response['elapsed_time'])}")
+    st.markdown(f"##### Question Count : {response['count']} of {numberOfQuestions}")
+    
+    
+    # {"count":count, "user_id":user_id,"time_stamp":time_stamp, "elapsed_time":elapsed_time, "content":content}
+
     st.session_state.history.append("You: " + user_input)
-    st.session_state.history.append("Agent: " + response)  # Using 'response' after it's defined
+                
+    st.session_state.history.append("Agent: " + response['content'])  # Using 'response' after it's defined
+    for message in st.session_state.history:
+        st.write(message)
+    
 
-# Display the chat history
-for message in st.session_state.history:
-    st.text(message)