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| import os | |
| import streamlit as st | |
| from streamlit.logger import get_logger | |
| from langchain.schema.messages import HumanMessage | |
| from utils.mongo_utils import get_db_client | |
| from utils.app_utils import create_memory_add_initial_message, get_random_name, DEFAULT_NAMES_DF | |
| from utils.memory_utils import clear_memory, push_convo2db | |
| from utils.chain_utils import get_chain, custom_chain_predict | |
| from app_config import ISSUES, SOURCES, source2label, issue2label | |
| logger = get_logger(__name__) | |
| openai_api_key = os.environ['OPENAI_API_KEY'] | |
| temperature = 0.8 | |
| # username = "barb-chase" #"ivnban-ctl" | |
| if "sent_messages" not in st.session_state: | |
| st.session_state['sent_messages'] = 0 | |
| if "issue" not in st.session_state: | |
| st.session_state['issue'] = ISSUES[0] | |
| if 'previous_source' not in st.session_state: | |
| st.session_state['previous_source'] = SOURCES[0] | |
| if 'db_client' not in st.session_state: | |
| st.session_state["db_client"] = get_db_client() | |
| if 'texter_name' not in st.session_state: | |
| st.session_state["texter_name"] = get_random_name(names_df=DEFAULT_NAMES_DF) | |
| logger.info(f"texter name is {st.session_state['texter_name']}") | |
| memories = {'memory':{"issue": st.session_state['issue'], "source": st.session_state['previous_source']}} | |
| with st.sidebar: | |
| username = st.text_input("Username", value='Barbara', max_chars=30) | |
| if 'counselor_name' not in st.session_state: | |
| st.session_state["counselor_name"] = username #get_random_name(names_df=DEFAULT_NAMES_DF) | |
| # temperature = st.slider("Temperature", 0., 1., value=0.8, step=0.1) | |
| issue = st.selectbox("Select a Scenario", ISSUES, index=0, format_func=issue2label, | |
| on_change=clear_memory, kwargs={"memories":memories, "username":username, "language":"English"} | |
| ) | |
| supported_languages = ['en', "es"] if issue == "Anxiety" else ['en'] | |
| language = st.selectbox("Select a Language", supported_languages, index=0, | |
| format_func=lambda x: "English" if x=="en" else "Spanish", | |
| on_change=clear_memory, kwargs={"memories":memories, "username":username, "language":"English"} | |
| ) | |
| source = st.selectbox("Select a source Model A", SOURCES, index=0, | |
| format_func=source2label, | |
| ) | |
| st.markdown(f"### Previous Prompt Count: :red[**{st.session_state['sent_messages']}**]") | |
| changed_source = any([ | |
| st.session_state['previous_source'] != source, | |
| st.session_state['issue'] != issue, | |
| st.session_state['counselor_name'] != username, | |
| ]) | |
| if changed_source: | |
| st.session_state["counselor_name"] = username #get_random_name(names_df=DEFAULT_NAMES_DF) | |
| st.session_state["texter_name"] = get_random_name(names_df=DEFAULT_NAMES_DF) | |
| st.session_state['previous_source'] = source | |
| st.session_state['issue'] = issue | |
| st.session_state['sent_messages'] = 0 | |
| create_memory_add_initial_message(memories, | |
| issue, | |
| language, | |
| changed_source=changed_source, | |
| counselor_name=st.session_state["counselor_name"], | |
| texter_name=st.session_state["texter_name"]) | |
| st.session_state['previous_source'] = source | |
| memoryA = st.session_state[list(memories.keys())[0]] | |
| # issue only without "." marker for model compatibility | |
| llm_chain, stopper = get_chain(issue.split(".")[0], language, source, memoryA, temperature, texter_name=st.session_state["texter_name"]) | |
| st.title("💬 Simulator") | |
| for msg in memoryA.buffer_as_messages: | |
| role = "user" if type(msg) == HumanMessage else "assistant" | |
| st.chat_message(role).write(msg.content) | |
| if prompt := st.chat_input(): | |
| st.session_state['sent_messages'] += 1 | |
| if 'convo_id' not in st.session_state: | |
| push_convo2db(memories, username, language) | |
| st.chat_message("user").write(prompt) | |
| responses = custom_chain_predict(llm_chain, prompt, stopper) | |
| # responses = llm_chain.predict(input=prompt, stop=stopper) | |
| # response = update_memory_completion(prompt, st.session_state["memory"], OA_engine, temperature) | |
| for response in responses: | |
| st.chat_message("assistant").write(response) |