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# from prompt_templates import question_answering_prompt_series, question_answering_system
# from openai_interface import GPT_Turbo
from openai import BadRequestError
import logging
import streamlit as st
# from streamlit_option_menu import option_menu
import hydralit_components as hc
import json
import os, requests, re
from datetime import timedelta
import pathlib
import base64
import shutil
def get_base64_of_bin_file(bin_file):
with open(bin_file, 'rb') as file:
data = file.read()
return base64.b64encode(data).decode()
from dotenv import load_dotenv, find_dotenv
load_dotenv(find_dotenv('env'), override=True)
# I use a key that I increment each time I want to change a text_input
if 'key' not in st.session_state:
st.session_state.key = 0
# key = st.session_state['key']
if not pathlib.Path('models').exists():
os.mkdir('models')
# golden_dataset = EmbeddingQAFinetuneDataset.from_json("data/golden_100.json")
## PAGE CONFIGURATION
st.set_page_config(page_title="Limitus",
# page_icon="assets/impact-theory-logo-only.png",
page_icon="http://www.w3.org/2000/svg",
layout="wide",
initial_sidebar_state="collapsed",
menu_items={'Report a bug': "https://www.extremelycoolapp.com/bug"})
# image = "https://is2-ssl.mzstatic.com/image/thumb/Music122/v4/bd/34/82/bd348260-314c-5898-26c0-bef2e0388ebe/source/1200x1200bb.png"
# image = "assets/logos/great_logos.png"
def add_bg_from_local(image_file):
bin_str = get_base64_of_bin_file(image_file)
page_bg_img = f'''
<style>
.stApp {{
background-image: url("data:image/png;base64,{bin_str}");
background-size: 100% auto;
background-repeat: no-repeat;
background-attachment: fixed;
}}
</style>
'''
st.markdown(page_bg_img, unsafe_allow_html=True)
## RERANKER
# reranker = ReRanker('cross-encoder/ms-marco-MiniLM-L-6-v2')
## ENCODING --> tiktoken library
model_ids = ['gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613']
# model_nameGPT = model_ids[1]
# encoding = encoding_for_model(model_nameGPT)
## DATA
data_path = './data/impact_theory_data.json'
cache_path = 'data/impact_theory_cache.parquet'
# data = load_data(data_path)
cache = None # load_content_cache(cache_path)
# guest_list = sorted(list(set([d['guest'] for d in data])))
we_are_not_online = os.getenv('ENV') == 'local'
we_are_online = not we_are_not_online
if we_are_not_online:
# # st.write("Loading secrets from [secrets] section")
# # for streamlit online or local, which uses a [secrets] section
openai_api_key = st.secrets['OPENAI_API_KEY']
st.write(f"Got openai api key {openai_api_key[:7]}")
# # hf_token = st.secrets['secrets']['LLAMA2_ENDPOINT_HF_TOKEN']
# # hf_endpoint = st.secrets['secrets']['LLAMA2_ENDPOINT']
else :
# # st.write("Loading secrets for Huggingface")
# # for Huggingface (no [secrets] section)
openai_api_key = os.getenv('OPENAI_API_KEY')
# st.write(f"Got openai api key {openai_api_key[:10]}")
# hf_token = st.secrets['LLAMA2_ENDPOINT_HF_TOKEN']
# hf_endpoint = st.secrets['LLAMA2_ENDPOINT']
##############
def main():
with st.sidebar:
_, center, _ = st.columns([3, 5, 3])
with center:
st.text("Search Lab")
_, center, _ = st.columns([2, 5, 3])
with center:
if we_are_online:
st.text("Running ONLINE")
# st.text("(UNSTABLE)")
else:
st.text("Running OFFLINE")
st.write("----------")
hybrid_search = st.toggle('Hybrid Search', True)
if hybrid_search:
alpha_input = st.slider(label='Alpha',min_value=0.00,
max_value=1.00, value=0.40, step=0.05, key=1)
retrieval_limit = st.slider(label='Hybrid Search Results',
min_value=10, max_value=300, value=10, step=10)
hybrid_filter = st.toggle('Filter Search using Guest name', True) # i.e. look only at guests' data
rerank = st.toggle('Rerank', True)
if rerank:
reranker_topk = st.slider(label='Reranker Top K',min_value=1, max_value=5, value=3, step=1)
else:
# needed to not fill the LLM with too many responses (> context size)
# we could make it dependent on the model
reranker_topk = 3
rag_it = st.toggle(f"RAG it with SOME MODEL", True)
if rag_it:
# st.write(f"Using LLM '{model_nameGPT}'")
llm_temperature = st.slider(label='LLM T˚', min_value=0.0,
max_value=2.0, value=0.01, step=0.10 )
available_models = ['no_model']
model_name_or_path = st.selectbox(label='Model Name:',
options=available_models,
index=available_models.index('no_model'),
placeholder='Select Model')
delete_models = st.button('Delete models')
if delete_models:
# model_path = os.path.join("models", model_name_or_path.split('/')[-1])
# if os.path.isdir(model_path):
# shutil.rmtree(model_path)
for model in os.listdir("models"):
model_path = os.path.join("models", model)
if os.path.isdir(model_path) and 'finetuned-all-mpnet-base-v2-300' not in model_path:
shutil.rmtree(model_path)
st.write("Models deleted")
if we_are_not_online:
st.write("Experimental and time limited 2'")
c1,c2 = st.columns([8,1])
with c1:
st.write("Finetuning not available")
# check_model(model_name_or_path)
# client, available_classes = get_weaviate_client(Wapi_key, url, model_name_or_path, openai_api_key)
print("Available classes:", "NONE")
# maybe the free sandbox has expired, or the api key is wrong
st.sidebar.write(f"Weaviate sandbox not accessible or expired")
# st.stop()
st.title("Chat with our crypto agents on Discord!")
# st.image('./assets/impact-theory-logo.png', width=400)
# st.image('assets/it_tom_bilyeu.png', use_column_width=True)
st.image('data/logos/great_logo.png', use_column_width=True)
# st.subheader(f"Chat with the Impact Theory podcast: ")
st.write('\n')
# st.stop()
st.write("\u21D0 Open the sidebar to change Search settings \n ") # https://home.unicode.org also 21E0, 21B0 B2 D0
if not hybrid_search:
st.stop()
col1, _ = st.columns([3,7])
with col1:
guest = st.selectbox('Select A Guest',
options=["1","2"],
index=None,
placeholder='Select Guest')
col1, col2 = st.columns([7,3])
with col1:
if guest is None:
msg = f'Select a guest before asking your question:'
else:
msg = f'Enter your question about {guest}:'
textbox = st.empty()
# best solution I found to be able to change the text inside a text_input box afterwards, using a key
query = textbox.text_input(msg,
value="",
placeholder="You can refer to the guest with PRONOUNS",
key=st.session_state.key)
# st.write(f"Guest = {guest}")
# st.write(f"key = {st.session_state.key}")
st.write('\n\n\n\n\n')
reworded_query = {'changed': False, 'status': 'error'} # at start, the query is empty
valid_response = [] # at start, the query is empty, so prevent the search
if query:
if guest is None:
st.session_state.key += 1
query = textbox.text_input(msg,
value="",
placeholder="YOU MUST SELECT A GUEST BEFORE ASKING A QUESTION",
key=st.session_state.key)
# st.write(f"key = {st.session_state.key}")
st.stop()
else:
# st.write(f'It looks like you selected {guest} as a filter (It is ignored for now).')
with col2:
# let's add a nice pulse bar while generating the response
with hc.HyLoader('', hc.Loaders.pulse_bars, primary_color= 'red', height=50): #"#0e404d" for image green
with col1:
if st.toggle('Rewrite query with LLM', True):
# let's use Llama2, and fall back on GPT3.5 if it fails
# reworded_query = reword_query(query, guest,
# model_name='gpt-3.5-turbo-0125')
new_query = query
guest_lastname = 'john'
query = new_query
st.write(f"New query: {query}")
# hybrid_response = client.hybrid_search(query,
# class_name,
# # properties=['content'], #['title', 'summary', 'content'],
# alpha=alpha_input,
# display_properties=client.display_properties,
# where_filter=where_filter,
# limit=retrieval_limit)
hybrid_response = 'a response'
response = hybrid_response
if rerank:
# rerank results with cross encoder
# ranked_response = reranker.rerank(response, query,
# apply_sigmoid=True, # score between 0 and 1
# top_k=reranker_topk)
# logger.info(ranked_response)
# expanded_response = expand_content(ranked_response, cache,
# content_key='doc_id',
# create_new_list=True)
response = expanded_response
# make sure token count < threshold
# token_threshold = 8000 if model_nameGPT == model_ids[0] else 3500
valid_response = response
# st.write(f"Number of results: {len(valid_response)}")
# I jumped out of col1 to get all page width, so need to retest query
if query:
# creates container for LLM response to position it above search results
chat_container, response_box = [], st.empty()
# # RAG time !! execute chat call to LLM
if rag_it:
# st.subheader("Response from Impact Theory (context)")
# will appear under the answer, moved it into the response box
# generate LLM prompt
prompt = "some prompt" #generate_prompt_series(query=query, results=valid_response)
# GPTllm = GPT_Turbo(model=model_nameGPT,
# api_key=openai_api_key)
# try:
# # inserts chat stream from LLM
# for resp in GPTllm.get_chat_completion(prompt=prompt,
# temperature=llm_temperature,
# max_tokens=350,
# show_response=True,
# stream=True):
# with response_box:
# content = resp.choices[0].delta.content
# if content:
# chat_container.append(content)
# result = "".join(chat_container).strip()
# response_box.markdown(f"### Response from Impact Theory (RAG):\n\n{result}")
# except BadRequestError as e:
# logger.info('Making request with smaller context')
# valid_response = validate_token_threshold(response,
# question_answering_prompt_series,
# query=query,
# tokenizer=encoding,
# token_threshold=3500,
# verbose=True)
# # if reranker is off, we may receive a LOT of responses
# # so we must reduce the context size manually
# if not rerank:
# valid_response = valid_response[:reranker_topk]
# prompt = generate_prompt_series(query=query, results=valid_response)
# for resp in GPTllm.get_chat_completion(prompt=prompt,
# temperature=llm_temperature,
# max_tokens=350, # expand for more verbose answers
# show_response=True,
# stream=True):
# try:
# # inserts chat stream from LLM
# with response_box:
# content = resp.choice[0].delta.content
# if content:
# chat_container.append(content)
# result = "".join(chat_container).strip()
# response_box.markdown(f"### Response from Impact Theory (RAG):\n\n{result}")
# except Exception as e:
# print(e)
st.markdown("----")
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 = 300 #hit["length"] # get length
time_string = str(timedelta(seconds=show_length)) # convert show_length to readable time string
with col1:
st.write("col 1")
# st.write(search_result(i=i,
# url=episode_url,
# guest=hit['guest'],
# title=title,
# 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.markdown(f"[]({episode_url})")
# st.markdown(f'<a href="{episode_url}">'
# f'<img src={image} '
# f'caption={title.split("|")[0]} width=200, use_column_width=False />'
# f'</a>',
# unsafe_allow_html=True)
st.image('assets/download.jpg')
# st.image(image, caption=title.split('|')[0], width=200, use_column_width=False)
# let's use all width for the content
st.write("something something")
def get_answer(query, valid_response, GPTllm):
# generate LLM prompt
return 'from get_answer'
# prompt = generate_prompt_series(query=query,
# results=valid_response)
# return GPTllm.get_chat_completion(prompt=prompt,
# system_message='answer this question based on the podcast material',
# temperature=0,
# max_tokens=500,
# stream=False,
# show_response=False)
# def reword_query(query, guest, model_name='llama2-13b-chat', response_processing=True):
# """ Asks LLM to rewrite the query when the guest name is missing.
# Args:
# query (str): user query
# guest (str): guest name
# model_name (str, optional): name of a LLM model to be used
# """
# # tags = {'llama2-13b-chat': {'start': '<s>', 'end': '</s>', 'instruction': '[INST]', 'system': '[SYS]'},
# # 'gpt-3.5-turbo-0613': {'start': '<|startoftext|>', 'end': '', 'instruction': "```", 'system': ```}}
# prompt_fields = {
# "you_are":f"You are an expert in linguistics and semantics, analyzing the question asked by a user to a vector search system, \
# and making sure that the question is well formulated and understandable by any average reader.",
# "your_task":f"Your task is to detect if the name of the guest ({guest}) is mentioned in the question '{query}', \
# If that is not the case, rewrite the question using the guest name, \
# without changing the meaning of the question. \
# Most of the time, the user will have used a pronoun to designate the guest, in which case, \
# simply replace the pronoun with the guest name. \
# If the guest name is already present in the question, return the original question as is.",
# "final_instruction":f"Only regenerate the requested rewritten question or the original, WITHOUT ANY COMMENT OR REPHRASING. \
# Your answer must be as close as possible to the original question, \
# and exactly identical, word for word, if the user mentions the guest name, i.e. {guest}.",
# "question":f"{query}"
# }
# # prompt created by chatGPT :-)
# # and Llama still outputs the original question and precedes the answer with 'rewritten question'
# prompt_fields2 = {
# "you_are": (
# "You are an expert in linguistics and semantics. Your role is to analyze questions asked to a vector search system."
# ),
# "your_task": (
# f"Detect if the guest's FULL name, {guest}, is mentioned in the user's question. "
# "If not, rewrite the question by replacing pronouns or indirect references with the guest's name." \
# "If yes, return the original question as is, without any change at all, not even punctuation,"
# "except a question mark that you MUST add if it's missing."
# ),
# "question": (
# f"Original question: '{query}'. "
# "Rewrite this question to include the guest's FULL name if it's not already mentioned."
# "Add a question mark if it's missing, nothing else."
# ),
# "final_instruction": (
# "Create a rewritten question or keep the original question as is. "
# "Do not include any labels, titles, or additional text before or after the question."
# "The Only thing you can and MUST add is a question mark if it's missing."
# "Return a json object, with the key 'original_question' for the original question, \
# and 'rewritten_question' for the rewritten question \
# and 'changed' being True if you changed the answer, otherwise False."
# ),
# }
# if model_name == 'llama2-13b-chat':
# # special tags are used:
# # `<s>` - start prompt tag
# # `[INST], [/INST]` - Opening and closing model instruction tags
# # `<<<SYS>>>, <</SYS>>` - Opening and closing system prompt tags
# llama_prompt = """
# <s>[INST] <<SYS>>
# {you_are}
# <</SYS>>
# {your_task}\n
# ```
# \n\n
# Question: {question}\n
# {final_instruction} [/INST]
# Answer:
# """
# prompt = llama_prompt.format(**prompt_fields2)
# headers = {"Authorization": f"Bearer {hf_token}",
# "Content-Type": "application/json",}
# json_body = {
# "inputs": prompt,
# "parameters": {"max_new_tokens":400,
# "repetition_penalty": 1.0,
# "temperature":0.01}
# }
# response = requests.request("POST", hf_endpoint, headers=headers, data=json.dumps(json_body))
# response = json.loads(response.content.decode("utf-8"))
# # ^ will not process the badly formatted generated text, so we do it ourselves
# if isinstance(response, dict) and 'error' in response:
# print("Found error")
# print(response)
# # return {'error': response['error'], 'rewritten_question': query, 'changed': False, 'status': 'error'}
# # I test this here otherwise it gets in col 2 or 1, which are too
# # if reworded_query['status'] == 'error':
# # st.write(f"Error in LLM response: 'error':{reworded_query['error']}")
# # st.write("The LLM could not connect to the server. Please try again later.")
# # st.stop()
# return reword_query(query, guest, model_name='gpt-3.5-turbo-0125')
# if response_processing:
# if isinstance(response, list) and isinstance(response[0], dict) and 'generated_text' in response[0]:
# print("Found generated text")
# response0 = response[0]['generated_text']
# pattern = r'\"(\w+)\":\s*(\".*?\"|\w+)'
# matches = re.findall(pattern, response0)
# # let's build a dictionary
# result = {key: json.loads(value) if value.startswith("\"") else value for key, value in matches}
# return result | {'status': 'success'}
# else:
# print("Found no answer")
# return reword_query(query, guest, model_name='gpt-3.5-turbo-0125')
# # return {'original_question': query, 'rewritten_question': query, 'changed': False, 'status': 'no properly formatted answer' }
# else:
# return response
# # return response
# # assert 'error' not in response, f"Error in LLM response: {response['error']}"
# # assert 'generated_text' in response[0], f"Error in LLM response: {response}, no 'generated_text' field"
# # # let's extract the rewritten question
# # return response[0]['generated_text'] .split("Rewritten question: '")[-1][:-1]
# else:
# # we assume / force openai
# model_ids = ['gpt-3.5-turbo-0125', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613']
# if model_name not in model_ids:
# model_name = model_ids[0]
# GPTllm = GPT_Turbo(model=model_name, api_key=openai_api_key)
# openai_prompt = """
# {your_task} \n
# {final_instruction} /n
# ```
# \n\n
# Question: {question}\n
# Answer:
# """
# prompt = openai_prompt.format(**prompt_fields)
# try:
# # https://platform.openai.com/docs/guides/text-generation/chat-completions-api
# resp = GPTllm.get_chat_completion(prompt=prompt,
# system_message=prompt_fields['you_are'],
# user_message = None,
# temperature=0.01,
# max_tokens=1500, # it's a long question...
# show_response=True,
# stream=False)
# if resp.choices[0].finish_reason == 'stop':
# if guest in resp.choices[0].message.content:
# new_question = resp.choices[0].message.content
# return {'rewritten_question': new_question,
# 'changed': True, 'status': 'success'}
# else:
# raise Exception("LLM did not stop") # to go to the except block
# except Exception:
# return {'rewritten_question': query, 'changed': False, 'status': 'not success'}
if __name__ == '__main__':
main()
# streamlit run app.py --server.allowRunOnSave True
|