pages/training_adherence.py

import streamlit as st
import numpy as np
from collections import defaultdict
from langchain_core.messages import HumanMessage
from utils.app_utils import are_models_alive
from models.ta_models.ta_utils import TA_predict_convo, ta_push_convo_comparison, post_process_response
from models.ta_models.config import QUESTION2PHASE, NAME2QUESTION, TA_OPTIONS

st.set_page_config(page_title="Conversation Simulator - Scoring")

if not are_models_alive():
    st.switch_page("pages/model_loader.py")

if "memory" not in st.session_state:
    st.switch_page("pages/convosim.py")

memory = st.session_state['memory']
progress_text = "Scoring Conversation using AI models ..."

@st.cache_data(show_spinner=False)
def get_ta_responses():
    my_bar = st.progress(0, text=progress_text)
    data = defaultdict(defaultdict)
    for i, question in enumerate(QUESTION2PHASE.keys()):
        # responses = ["Yes, The helper showed some respect.",
        #             "Yes. The helper is good! No doubt",
        #             "N/A, Texter disengaged.",
        #             "No. While texter is trying is lacking.",
        #             "No \n\n This is an explanation."]
        # full_response = np.random.choice(responses)
        full_convo, prompt, full_response = TA_predict_convo(memory, question, make_explanation=True, conversation_id=st.session_state['convo_id'])
        response, explanation = post_process_response(full_response)
        data[question]["response"] = response
        data[question]["explanation"] = explanation
        my_bar.progress((i+1) / len(QUESTION2PHASE.keys()), text = progress_text)
        import time
        time.sleep(2)
    my_bar.empty()
    return data

with st.container():
    col1, col2 = st.columns(2)
    if col1.button("Go Back"):
        get_ta_responses.clear()
        st.switch_page("pages/convosim.py")
    expl = col2.checkbox("Show Scoring Explanations")

tab1, tab2 = st.tabs(["Scoring", "Conversation"])
data = get_ta_responses()

with tab2:
    for msg in memory.buffer_as_messages:
        role = "user" if type(msg) == HumanMessage else "assistant"
        st.chat_message(role).write(msg.content)

with tab1:
    for question in QUESTION2PHASE.keys():
        with st.container(border=True):
            question_str = NAME2QUESTION[question].split(' Answer')[0]
            st.radio(
                f"**{question_str}**", options=TA_OPTIONS, 
                index=TA_OPTIONS.index(data[question]['response']), horizontal=True,
                key=f"{question}_manual"
            )
            if expl:
                st.text_area(
                    label="", value=data[question]["explanation"], key=f"{question}_explanation_manual"
                )
                # st.write(data[question]["explanation"])

    with st.container():
        col1, col2 = st.columns(2)
        if col1.button("Go Back", key="goback2"):
            get_ta_responses.clear()
            st.switch_page("pages/convosim.py")
        if col2.button("Submit Scoring", type="primary"):
            ytrue = {
                question: {
                    "response": st.session_state[f"{question}_manual"],
                    "explanation": st.session_state[f"{question}_explanation_manual"] if expl else "",
                } 
                for question in QUESTION2PHASE.keys()
            }
            ta_push_convo_comparison(ytrue, data)
            get_ta_responses.clear()
            st.switch_page("pages/convosim.py")