app.py

import streamlit as st
import os
import random
import importlib.util
import firebase_admin
from firebase_admin import credentials, firestore
import json


# Set page configuration as the first command
st.set_page_config(
    page_title="TB Chatbot Evaluation",
    page_icon="👋",
)


PASSCODE = os.environ["MY_PASSCODE"]
creds_dict = {
    "type": os.environ.get("FIREBASE_TYPE", "service_account"),
    "project_id": os.environ.get("FIREBASE_PROJECT_ID"),
    "private_key_id": os.environ.get("FIREBASE_PRIVATE_KEY_ID"),
    "private_key": os.environ.get("FIREBASE_PRIVATE_KEY", "").replace("\\n", "\n"),
    "client_email": os.environ.get("FIREBASE_CLIENT_EMAIL"),
    "client_id": os.environ.get("FIREBASE_CLIENT_ID"),
    "auth_uri": os.environ.get("FIREBASE_AUTH_URI", "https://accounts.google.com/o/oauth2/auth"),
    "token_uri": os.environ.get("FIREBASE_TOKEN_URI", "https://oauth2.googleapis.com/token"),
    "auth_provider_x509_cert_url": os.environ.get("FIREBASE_AUTH_PROVIDER_X509_CERT_URL", 
                                                "https://www.googleapis.com/oauth2/v1/certs"),
    "client_x509_cert_url": os.environ.get("FIREBASE_CLIENT_X509_CERT_URL"),
    "universe_domain": "googleapis.com"

}
# Create a temporary JSON file
file_path  = "coco-evaluation-firebase-adminsdk-p3m64-99c4ea22c1.json"
with open(file_path, 'w') as json_file:
    json.dump(creds_dict, json_file, indent=2)

# Initialize Firebase
if not firebase_admin._apps:
    cred = credentials.Certificate("coco-evaluation-firebase-adminsdk-p3m64-99c4ea22c1.json")
    firebase_admin.initialize_app(cred)
db = firestore.client()



# Set passcode for authentication
PASSCODE = os.environ["MY_PASSCODE"]

# Initialize authentication state
if "authenticated" not in st.session_state:
    st.session_state["authenticated"] = False


# Initialize session state variables
def init_session_state():
    if "authenticated" not in st.session_state:
        st.session_state["authenticated"] = False
    if "evaluator_confirmed" not in st.session_state:
        st.session_state["evaluator_confirmed"] = None
    if "model_order" not in st.session_state:
        st.session_state["model_order"] = []
    if "current_index" not in st.session_state:
        st.session_state["current_index"] = 0
    if "models_completed" not in st.session_state:
        st.session_state["models_completed"] = False
    if "evaluation_status" not in st.session_state or not st.session_state["evaluation_status"]:
        st.session_state["evaluation_status"] = {}
    if "all_evaluations" not in st.session_state:
        st.session_state["all_evaluations"] = {}
    if "evaluation_ids" not in st.session_state:
        st.session_state["evaluation_ids"] = {}
    if "start_time" not in st.session_state:
        st.session_state["start_time"] = None
    if "evaluation_durations" not in st.session_state:
        st.session_state["evaluation_durations"] = {}
    if "submitted_evaluations" not in st.session_state:
        st.session_state["submitted_evaluations"] = set()


init_session_state()


# Display Welcome Page
if not st.session_state["authenticated"]:
    st.markdown(f"<h1 style='text-align: center;'>Welcome to the TB Chatbot Evaluation</h1>", unsafe_allow_html=True)
    
    col1, col2, col3 = st.columns([2, 1, 1])
    with col1:
        st.write("Are you an evaluator?")
    with col2:
        if st.button("Yes"):
            st.session_state["evaluator_confirmed"] = True
    with col3:
        if st.button("No"):
            st.session_state["evaluator_confirmed"] = False

    if st.session_state["evaluator_confirmed"]:
        evaluator_id = st.text_input("Enter your Evaluator ID (can be anything)")
        passcode = st.text_input("Enter Passcode to Access Models (password is the same)", type="password")
        if st.button("Submit"):
            print("Hello")
            if passcode == PASSCODE and evaluator_id:
                print("Submitted")
                # Save Evaluator ID
                db.collection("evaluator_ids").document(evaluator_id).set({
                    "evaluator_id": evaluator_id,
                    "timestamp": firestore.SERVER_TIMESTAMP
                })

                # Update session state
                st.session_state["authenticated"] = True
                st.session_state["evaluator_id"] = evaluator_id


# Show the main content only if authenticated
if st.session_state["authenticated"]:
    # Sidebar with only randomized page navigation
    with st.sidebar:
        # Only create the page mapping if it doesn't already exist in the session state
        if "page_mapping" not in st.session_state:
            # Get list of page files and randomize their order
            PAGES_DIR = "pages"
            page_files = [f for f in os.listdir(PAGES_DIR) if f.endswith(".py")]
            random.shuffle(page_files)
            
            # Create generic names for display
            generic_names = [f"Model {chr(65 + i)}" for i in range(len(page_files))]
            
            # Create a dictionary to map display names to file paths
            st.session_state["page_mapping"] = {
                generic_name: os.path.join(PAGES_DIR, page_file) for generic_name, page_file in zip(generic_names, page_files)
            }
        
        # Retrieve the consistent mapping from session state
        pages = st.session_state["page_mapping"]
        
        # Sidebar navigation selectbox with generic labels
        selected_generic_name = st.selectbox("Navigation", list(pages.keys()), label_visibility="collapsed")

    # Load and run the selected page dynamically using the selected generic name
    selected_page_path = pages[selected_generic_name]
    spec = importlib.util.spec_from_file_location(selected_generic_name, selected_page_path)
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)

    # Display main welcome message
    st.markdown("""
        # Welcome to the TB Chatbot Simulation Portal  

        This portal allows you to interact with our TB-powered chatbot, built using OpenAI's GPT-4. Here, you can evaluate multiple chatbot configurations, each designed to foster trust, empathy, and medical accuracy in responses.  

        ## Purpose  

        Your task is to assess the chatbot models by interacting with them using patient scenario-based questions. Evaluate each model based on the following principles:  

        ### Trust  
        - Does the response convey confidence and reliability?  
        - Are the answers factually accurate and rooted in medical expertise?  
                
        ### Medical Accuracy  
        - Are responses aligned with current medical guidelines and best practices?  
        - Do they avoid misinformation or potentially harmful suggestions?  

        ### Empathy  
        - Does the chatbot demonstrate understanding and compassion?  
        - Are responses emotionally sensitive and supportive?  

        Your feedback will help us identify the best model to support our mission of enhancing patient communication and care through AI-driven solutions.  
        """)