""" Client test. Run server: python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6_9b NOTE: For private models, add --use-auth_token=True NOTE: --infer_devices=True (default) must be used for multi-GPU in case see failures with cuda:x cuda:y mismatches. Currently, this will force model to be on a single GPU. Then run this client as: python client_test.py For HF spaces: HOST="https://h2oai-h2ogpt-chatbot.hf.space" python client_test.py Result: Loaded as API: https://h2oai-h2ogpt-chatbot.hf.space ✔ {'instruction_nochat': 'Who are you?', 'iinput_nochat': '', 'response': 'I am h2oGPT, a large language model developed by LAION.', 'sources': ''} For demo: HOST="https://gpt.h2o.ai" python client_test.py Result: Loaded as API: https://gpt.h2o.ai ✔ {'instruction_nochat': 'Who are you?', 'iinput_nochat': '', 'response': 'I am h2oGPT, a chatbot created by LAION.', 'sources': ''} NOTE: Raw output from API for nochat case is a string of a python dict and will remain so if other entries are added to dict: {'response': "I'm h2oGPT, a large language model by H2O.ai, the visionary leader in democratizing AI.", 'sources': ''} """ import ast import time import os import markdown # pip install markdown import pytest from bs4 import BeautifulSoup # pip install beautifulsoup4 debug = False os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' def get_client(serialize=True): from gradio_client import Client client = Client(os.getenv('HOST', "http://localhost:7860"), serialize=serialize) if debug: print(client.view_api(all_endpoints=True)) return client def get_args(prompt, prompt_type, chat=False, stream_output=False, max_new_tokens=50, langchain_mode='Disabled'): from collections import OrderedDict kwargs = OrderedDict(instruction=prompt if chat else '', # only for chat=True iinput='', # only for chat=True context='', # streaming output is supported, loops over and outputs each generation in streaming mode # but leave stream_output=False for simple input/output mode stream_output=stream_output, prompt_type=prompt_type, temperature=0.1, top_p=0.75, top_k=40, num_beams=1, max_new_tokens=max_new_tokens, min_new_tokens=0, early_stopping=False, max_time=20, repetition_penalty=1.0, num_return_sequences=1, do_sample=True, chat=chat, instruction_nochat=prompt if not chat else '', iinput_nochat='', # only for chat=False langchain_mode=langchain_mode, top_k_docs=4, document_choice=['All'], ) if chat: # add chatbot output on end. Assumes serialize=False kwargs.update(dict(chatbot=[])) return kwargs, list(kwargs.values()) @pytest.mark.skip(reason="For manual use against some server, no server launched") def test_client_basic(): return run_client_nochat(prompt='Who are you?', prompt_type='human_bot', max_new_tokens=50) def run_client_nochat(prompt, prompt_type, max_new_tokens): kwargs, args = get_args(prompt, prompt_type, chat=False, max_new_tokens=max_new_tokens) api_name = '/submit_nochat' client = get_client(serialize=True) res = client.predict( *tuple(args), api_name=api_name, ) print("Raw client result: %s" % res, flush=True) res_dict = dict(prompt=kwargs['instruction_nochat'], iinput=kwargs['iinput_nochat'], response=md_to_text(ast.literal_eval(res)['response']), sources=ast.literal_eval(res)['sources']) print(res_dict) return res_dict @pytest.mark.skip(reason="For manual use against some server, no server launched") def test_client_chat(): return run_client_chat(prompt='Who are you?', prompt_type='human_bot', stream_output=False, max_new_tokens=50, langchain_mode='Disabled') def run_client_chat(prompt, prompt_type, stream_output, max_new_tokens, langchain_mode): client = get_client(serialize=False) kwargs, args = get_args(prompt, prompt_type, chat=True, stream_output=stream_output, max_new_tokens=max_new_tokens, langchain_mode=langchain_mode) return run_client(client, prompt, args, kwargs) def run_client(client, prompt, args, kwargs, do_md_to_text=True, verbose=False): res = client.predict(*tuple(args), api_name='/instruction') args[-1] += [res[-1]] res_dict = kwargs res_dict['prompt'] = prompt if not kwargs['stream_output']: res = client.predict(*tuple(args), api_name='/instruction_bot') res_dict['response'] = res[0][-1][1] print(md_to_text(res_dict['response'], do_md_to_text=do_md_to_text)) return res_dict, client else: job = client.submit(*tuple(args), api_name='/instruction_bot') res1 = '' while not job.done(): outputs_list = job.communicator.job.outputs if outputs_list: res = job.communicator.job.outputs[-1] res1 = res[0][-1][-1] res1 = md_to_text(res1, do_md_to_text=do_md_to_text) print(res1) time.sleep(0.1) full_outputs = job.outputs() if verbose: print('job.outputs: %s' % str(full_outputs)) # ensure get ending to avoid race # -1 means last response if streaming # 0 means get text_output, ignore exception_text # 0 means get list within text_output that looks like [[prompt], [answer]] # 1 means get bot answer, so will have last bot answer res_dict['response'] = md_to_text(full_outputs[-1][0][0][1], do_md_to_text=do_md_to_text) return res_dict, client def md_to_text(md, do_md_to_text=True): if not do_md_to_text: return md assert md is not None, "Markdown is None" html = markdown.markdown(md) soup = BeautifulSoup(html, features='html.parser') return soup.get_text() if __name__ == '__main__': test_client_basic()