| # from sentence_transformers import SentenceTransformer | |
| # 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 | |