app.shell.py

from tiktoken import get_encoding, encoding_for_model
from weaviate_interface import WeaviateClient
from prompt_templates import question_answering_prompt_series, question_answering_system
from openai_interface import GPT_Turbo
from app_features import convert_seconds, generate_prompt_series, search_result, validate_token_threshold, \
    load_content_cache, load_data
from reranker import ReRanker
from loguru import logger

from datetime import timedelta

import streamlit as st
import sys
import json
import os

# load environment variables
from dotenv import load_dotenv

load_dotenv('.env', override=True)

## PAGE CONFIGURATION
st.set_page_config(page_title="Impact Theory",
                   page_icon=None,
                   layout="wide",
                   initial_sidebar_state="auto",
                   menu_items=None)
##############
# START CODE #
##############
data_path = './data/impact_theory_data.json'
## RETRIEVER
api_key = os.environ['WEAVIATE_API_KEY']
url = os.environ['WEAVIATE_ENDPOINT']
openai_api_key = os.environ['OPENAI_API_KEY']
retriever = WeaviateClient(api_key=api_key,
                           endpoint=url,
                           openai_api_key=openai_api_key)
## RERANKER
re_ranker = ReRanker()

## LLM

llm = GPT_Turbo()

## ENCODING
encoding = encoding_for_model('gpt-3.5-turbo-0613')

## INDEX NAME
index_name = 'Impact_theory_minilm_256'

##############
#  END CODE  #
##############
data = load_data(data_path)
# creates list of guests for sidebar
guest_list = sorted(list(set([d['guest'] for d in data])))


def main():
    with st.sidebar:
        guest = st.selectbox('Select Guest', options=guest_list, index=None, placeholder='Select Guest')

    st.image('./assets/impact-theory-logo.png', width=400)
    st.subheader(f"Chat with the Impact Theory podcast: ")
    st.write('\n')
    col1, _ = st.columns([7, 3])
    with col1:
        query = st.text_input('Enter your question: ')
        st.write('\n\n\n\n\n')

        if query:
            ##############
            # START CODE #
            ##############

            st.write('Hmmm...this app does not seem to be working yet.  Please check back later.')
            if guest:
                st.write(f'However, it looks like you selected {guest} as a filter.')
            # make hybrid call to weaviate
            retriever.display_properties.append('summary')
            hybrid_response = retriever.hybrid_search(request=query,
                                                     class_name=index_name)

            # rerank results
            ranked_response = re_ranker.rerank(results=hybrid_response, query=query,top_k=3)
            # validate token count is below threshold
            valid_response = validate_token_threshold(ranked_response,
                                                      question_answering_prompt_series,
                                                      query=query,
                                                      tokenizer=encoding,  # variable from ENCODING,
                                                      token_threshold=3000,
                                                      verbose=True)
            ##############
            #  END CODE  #
            ##############

            # # generate LLM prompt
            print(valid_response)
            prompt = generate_prompt_series(query=query, results=valid_response)

            # # prep for streaming response
            st.subheader("Response from Impact Theory (context)")
            with st.spinner('Generating Response...'):
                st.markdown("----")
                # creates container for LLM response
                chat_container, response_box = [], st.empty()
                for resp in llm.get_chat_completion(prompt=prompt,
                                                    system_message='Answer this question using the material provided in the prompt.',
                                                    temperature=0, max_tokens=500, stream=True):
                    try:
                        # inserts chat stream from LLM
                        #             with response_box:
                        content = resp.choices[0].delta.content
                        if content:
                            chat_container.append(content)
                            result = "".join(chat_container).strip()
                            st.write(f'{result}')
                    except Exception as e:
                        print(e)
                        continue
            # ##############
            # # START CODE #
            # ##############
            st.subheader("Search Results")
            for i, hit in enumerate(valid_response):
                col1, col2 = st.columns([7, 3], gap='large')
                image = hit['thumbnail_url']  # get thumbnail_url
                episode_url = hit['episode_url']  # get episode_url
                title = hit['title']  # get title
                show_length = hit['length']  # get length
                time_string = str(timedelta(seconds=show_length))  # convert show_length to readable time string
                # ##############
                # #  END CODE  #
                # ##############
                with col1:
                    st.write(search_result(i=i,
                                           url=episode_url,
                                           guest=hit['guest'],
                                           title=title,
                                           content=hit['content'],
                                           length=time_string),
                             unsafe_allow_html=True)
                    st.write('\n\n')
                with col2:
                    # st.write(f"<a href={episode_url} <img src={image} width='200'></a>",
                    #             unsafe_allow_html=True)
                    st.image(image, caption=title.split('|')[0], width=200, use_column_width=False)


if __name__ == '__main__':
    main()