app.py

import os
import streamlit as st
from datetime import datetime
import json
import requests
import uuid
from datetime import date, datetime
import requests
from pydantic import BaseModel, Field
from typing import Optional

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.
Do not give a diagnosis """

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"""


class ChatRequestClient(BaseModel):

    user_id: str
    user_input: str
    numberOfQuestions: int
    welcomeMessage: str
    llm1: str
    tokens1: int
    temperature1: float
    persona1SystemMessage: str
    persona2SystemMessage: str
    userMessage2: str
    llm2: str
    tokens2: int
    temperature2: float

def call_chat_api(data: ChatRequestClient):
    url = "https://agent-builder-api.greensea-b20be511.northeurope.azurecontainerapps.io/chat/"
    # Validate and convert the data to a dictionary
    validated_data = data.dict()
    
    # Make the POST request to the FastAPI server
    response = requests.post(url, json=validated_data)
    
    if response.status_code == 200:
        return response.json()  # Return the JSON response if successful
    else:
        return "An error occured"  # Return the raw response text if not successful

def genuuid ():
    return uuid.uuid4()

def format_elapsed_time(time):
    # Format the elapsed time to two decimal places
    return "{:.2f}".format(time)


# Title of the application
# st.image('agentBuilderLogo.png')
st.title('LLM-Powered Agent Interaction')

# Sidebar for inputting personas
st.sidebar.image('cognizant_logo.jpg')
st.sidebar.header("Agent Personas Design")
# st.sidebar.subheader("Welcome Message")
# welcomeMessage = st.sidebar.text_area("Define Intake Persona", value=welcomeMessage, height=300)
st.sidebar.subheader("Intake AI")
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 Intake Persona", value=placeHolderPersona1, height=300)
with st.sidebar.expander("See explanation"):
    st.write("This AI persona will converse with the patient to gather their symptoms. With each round of chat, the object of the AI is to ask more specific follow up questions as it narrows down to the specific diagnosis. However this AI should never give a diagnosis")
    st.image("agentPersona1.png")
llm1 = st.sidebar.selectbox("Model Selection", ['GPT-4', 'GPT3.5'], key='persona1_size')
temp1 = st.sidebar.slider("Temperature", 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("Recommendation and Next Best Action AI")
persona2SystemMessage = st.sidebar.text_area("Define Recommendation Persona", value=placeHolderPersona2, height=300)
with st.sidebar.expander("See explanation"):
    st.write("This AI persona uses the output of the symptom intake AI as its input. This AI’s job is to augment a health professional by assisting with a diagnosis and possible next best action. The teams will need to determine if this should be a tool used directly by the patient, as an assistant to the health professional or a hybrid of the two. ")
    st.image("agentPersona2.png")
llm2 = st.sidebar.selectbox("Model Selection", ['GPT-4', 'GPT3.5'], key='persona2_size')
temp2 = st.sidebar.slider("Temperature", 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')
userMessage2 = st.sidebar.text_area("Define User Message", value="This is the conversation todate, ", height=150)
st.sidebar.caption(f"Session ID: {genuuid()}")
# Main chat interface
st.header("Chat with the Agents")

# Initialize chat history in session state
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Collect user input
if user_input := st.chat_input("Write your message here:"):
    # Add user message to the chat history
    st.session_state.messages.append({"role": "user", "content": user_input})
    st.chat_message("user").markdown(user_input)

    # Prepare data for API call
    data = ChatRequestClient(
        user_id=user_id,
        user_input=user_input,
        numberOfQuestions=numberOfQuestions,
        welcomeMessage="",
        llm1=llm1,
        tokens1=tokens1,
        temperature1=temp1,
        persona1SystemMessage=persona1SystemMessage,
        persona2SystemMessage=persona2SystemMessage,
        userMessage2=userMessage2,
        llm2=llm2,
        tokens2=tokens2,
        temperature2=temp2
    )

    # Call the API
    response = call_chat_api(data)

    # Process the API response
    agent_message = response.get("content", "No response received from the agent.")
    elapsed_time = response.get("elapsed_time", 0)
    count = response.get("count", 0)

    # Add agent response to the chat history
    st.session_state.messages.append({"role": "assistant", "content": agent_message})
    with st.chat_message("assistant"):
        st.markdown(agent_message)

    # Display additional metadata
    st.markdown(f"##### Time taken: {format_elapsed_time(elapsed_time)} seconds")
    st.markdown(f"##### Question Count: {count} of {numberOfQuestions}")