Update app.py

app.py

@@ -1,57 +1,33 @@
 import os
 import streamlit as st
 from datetime import datetime
-import json
 import requests
 import uuid
-from datetime import date, datetime
 from pydantic import BaseModel
-from typing import Optional
 
 # Placeholder personas
 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 """
+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."""
 
 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"""
-
-# Data model for API request
+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."""
+
+# Mock API call function
 class ChatRequestClient(BaseModel):
     user_id: str
     user_input: str
     numberOfQuestions: int
-    welcomeMessage: str
+    persona1SystemMessage: str
+    persona2SystemMessage: str
     llm1: str
     tokens1: int
     temperature1: float
-    persona1SystemMessage: str
-    persona2SystemMessage: str
     userMessage2: str
     llm2: str
     tokens2: int
     temperature2: float
 
-# Mock API call function
 def call_chat_api(data: ChatRequestClient):
-    # Replace this with actual API logic
     return {
         "content": f"Response to: {data.user_input}",
         "elapsed_time": 0.5,
@@ -59,53 +35,32 @@ def call_chat_api(data: ChatRequestClient):
         "response_tokens": len(data.user_input.split())  # Mock token count
     }
 
-# Utility functions
-def genuuid():
-    return uuid.uuid4()
-
 def format_elapsed_time(time):
     return "{:.2f}".format(time)
 
 # Layout with three columns
-col1, col2, col3 = st.columns([1, 2, 1])  # Adjust ratios for desired width
+col1, col2, col3 = st.columns([1, 3, 1])  # Adjusted width ratios for better centering
 
 # Left Column: Variables and Settings
 with col1:
     st.sidebar.image('cognizant_logo.jpg')
     st.sidebar.header("Agent Personas Design")
     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
-    )
-    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'
-    )
-    st.sidebar.subheader("Recommendation and Next Best Action AI")
-    persona2SystemMessage = st.sidebar.text_area(
-        "Define Recommendation Persona", value=placeHolderPersona2, height=300
-    )
-    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 to date, ", height=150
-    )
-    st.sidebar.caption(f"Session ID: {genuuid()}")
+    numberOfQuestions = st.sidebar.slider("Number of Questions", 0, 10, 5, 1)
+    persona1SystemMessage = st.sidebar.text_area("Define Intake Persona", value=placeHolderPersona1, height=300)
+    llm1 = st.sidebar.selectbox("Model Selection", ['GPT-4', 'GPT3.5'])
+    temp1 = st.sidebar.slider("Temperature", 0.0, 1.0, 0.6, 0.1)
+    tokens1 = st.sidebar.slider("Tokens", 0, 4000, 500, 100)
+    persona2SystemMessage = st.sidebar.text_area("Define Recommendation Persona", value=placeHolderPersona2, height=300)
+    llm2 = st.sidebar.selectbox("Model Selection", ['GPT-4', 'GPT3.5'], key="persona2")
+    temp2 = st.sidebar.slider("Temperature", 0.0, 1.0, 0.5, 0.1, key="temp2")
+    tokens2 = st.sidebar.slider("Tokens", 0, 4000, 500, 100, key="tokens2")
 
 # Middle Column: Chat Interface
 with col2:
-    st.header("Chat with the Agents")
+    st.markdown("<div style='text-align: center;'><h1>Chat with the Agents</h1></div>", unsafe_allow_html=True)
+
+    # User ID Input
     user_id = st.text_input("User ID:", key="user_id")
 
     if not user_id:
@@ -115,47 +70,47 @@ with col2:
         if "messages" not in st.session_state:
             st.session_state.messages = []
 
-        # Display chat history
-        for message in st.session_state.messages:
-            with st.chat_message(message["role"]):
-                st.markdown(message["content"])
+        # Display chat history in a container
+        with st.container():
+            for message in st.session_state.messages:
+                role = "User" if message["role"] == "user" else "Agent"
+                with st.chat_message(message["role"]):
+                    st.markdown(message["content"])
 
-        # Collect user input
+        # Chat input at the bottom
         if user_input := st.chat_input("Write your message here:"):
             # Add user message
             st.session_state.messages.append({"role": "user", "content": user_input})
             st.chat_message("user").markdown(user_input)
 
-            # Prepare API data
+            # Prepare data for API call
             data = ChatRequestClient(
                 user_id=user_id,
                 user_input=user_input,
                 numberOfQuestions=numberOfQuestions,
-                welcomeMessage="",
+                persona1SystemMessage=persona1SystemMessage,
+                persona2SystemMessage=persona2SystemMessage,
                 llm1=llm1,
                 tokens1=tokens1,
                 temperature1=temp1,
-                persona1SystemMessage=persona1SystemMessage,
-                persona2SystemMessage=persona2SystemMessage,
-                userMessage2=userMessage2,
+                userMessage2="",
                 llm2=llm2,
                 tokens2=tokens2,
-                temperature2=temp2
+                temperature2=temp2,
             )
 
             # Call the API
             response = call_chat_api(data)
 
             # Process response
-            agent_message = response.get("content", "No response received.")
-            elapsed_time = response.get("elapsed_time", 0)
-            count = response.get("count", 0)
-            response_tokens = response.get("response_tokens", 0)
+            agent_message = response["content"]
+            elapsed_time = response["elapsed_time"]
+            count = response["count"]
+            response_tokens = response["response_tokens"]
 
             # Add agent response
             st.session_state.messages.append({"role": "assistant", "content": agent_message})
-            with st.chat_message("assistant"):
-                st.markdown(agent_message)
+            st.chat_message("assistant").markdown(agent_message)
 
 # Right Column: Stats
 with col3:
@@ -166,8 +121,3 @@ with col3:
         st.markdown(f"**Response Tokens:** {response_tokens}")
     else:
         st.markdown("No stats available yet.")
-
-
-
-    
-