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app.py
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import streamlit as st
import pandas as pd
import streamlit.components.v1 as components
import SessionState
from dialogue_helper import header, footer, render, mapper, mapper_safety, run_parsers
from prompts.generic_prompt import load_prefix, generate_response_interactive, select_prompt_interactive
from prompts.generic_prompt_parser import load_prefix as load_prefix_parse
from prompts.skill_selector import convert_sample_to_shot_selector
from transformers import AutoModelForCausalLM, AutoTokenizer
import json
import os
st.set_page_config(page_title="Few-Shot Bot", layout='centered', initial_sidebar_state='auto', page_icon="🤖")
@st.cache(allow_output_mutation=True, max_entries=1) #ttl=1200,
def load_model(args, model_checkpoint, device, shot_selector, safety_level):
if "gpt-j"in model_checkpoint or "neo"in model_checkpoint:
model = AutoModelForCausalLM.from_pretrained(model_checkpoint, low_cpu_mem_usage=True)
tokenizer = AutoTokenizer.from_pretrained("gpt2")
if args.multigpu:
from parallelformers import parallelize
parallelize(model, num_gpus=4, fp16=True, verbose='detail')
else:
model.half().to(device)
max_seq = 2048
else:
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
tokenizer.bos_token = ":"
tokenizer.eos_token = "\n"
model = AutoModelForCausalLM.from_pretrained(model_checkpoint)
max_seq = 1024
model.half().to(device)
available_datasets = mapper.keys()
prompt_dict = {}
prompt_parse = {}
prompt_skill_selector = {}
for d in available_datasets:
if "parse" in d:
prompt_parse[d] = load_prefix_parse(tokenizer=tokenizer, shots_value=mapper[d]["shots"][max_seq],
shot_converter=mapper[d]["shot_converter"],
file_shot=mapper[d]["file_data"]+"valid.json",
name_dataset=d, level=mapper[d]["level"],
shot_separator=mapper[d]["shot_separator"],sample_times=1)[0]
else:
if "safe" != d:
prompt_skill_selector[d] = load_prefix(tokenizer=tokenizer, shots_value=[shot_selector],
shot_converter=convert_sample_to_shot_selector,
file_shot= mapper[d]["file_data"]+"train.json" if "smd" in d else mapper[d]["file_data"]+"valid.json",
name_dataset=d, with_knowledge=None,
shot_separator=mapper[d]["shot_separator"],sample_times=1)[0]
prompt_dict[d] = load_prefix(tokenizer=tokenizer, shots_value=mapper[d]["shots"][max_seq],
shot_converter=mapper[d]["shot_converter"],
file_shot=mapper[d]["file_data"]+"valid.json",
name_dataset=d, with_knowledge=mapper[d]["with_knowledge"],
shot_separator=mapper[d]["shot_separator"],sample_times=1)[0]
## add safety prompts
for d in mapper_safety.keys():
prompt_skill_selector[d] = load_prefix(tokenizer=tokenizer, shots_value=[safety_level],
shot_converter=convert_sample_to_shot_selector,
file_shot= mapper_safety[d]["file_data"],
name_dataset=d, with_knowledge=None,
shot_separator=mapper_safety[d]["shot_separator"],sample_times=1)[0]
return model, tokenizer, max_seq, prompt_dict, prompt_parse, prompt_skill_selector
def get_session_state():
session_state = SessionState.get(sessionstep = 0, dialogue =[],
meta = [], user=[], assistant=[],
user_memory=[], length_gen='', KB_wiki=[], query_mem=[],
prompt=[], skill=[],
temperature='', topk='', topp='', api_key='', api=False)
return session_state
def mychat():
args = type('', (), {})()
args.multigpu = False
device = 0
safety_level = 6
shot_selector = 6
sample_skill = False
# model_checkpoint = "gpt2"
# model_checkpoint = "EleutherAI/gpt-neo-1.3B"
model_checkpoint = "EleutherAI/gpt-j-6B"
dialogue_ss = get_session_state()
form_model = st.sidebar.form(key='my_form')
api_key = form_model.text_input("Insert an API key from AI21 to interact with the 175B model")
submit_button_model = form_model.form_submit_button(label='Submit')
if submit_button_model:
dialogue_ss.api = True
dialogue_ss.api_key = api_key
st.sidebar.write("You are using the API")
max_number_turns = 3
dialogue_ss.meta = [
"I am a chatbot.",
"My name is FSB.",
"I love chatting with people.",
"I am less than 1 years old."
]
persona_used = "#### Persona\n"+"<br>".join(dialogue_ss.meta)
st.sidebar.markdown(f"{persona_used}", unsafe_allow_html=True)
# dialogue_ss.length_gen = st.sidebar.slider("Max Length", value=50, min_value = 10, max_value=100)
temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
topp = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.9)
with st.spinner("Initial models loading, please be patient"):
model, tokenizer, max_seq, prompt_dict, prompt_parse, prompt_skill_selector = load_model(args, model_checkpoint, device, shot_selector, safety_level) # bad_word_ids
chatlogholder = st.empty()
with chatlogholder:
if len(dialogue_ss.dialogue)==0:
components.html(header+footer, height=400)
else:
components.html(render(dialogue_ss.dialogue, None), height=400, scrolling=True)
form = st.form(key='chatinput', clear_on_submit=True)
chatinput = form.text_input("", placeholder="Type a message...", key='chatinput')
submit = form.form_submit_button('Send')
try:
if submit:
print("API key:", dialogue_ss.api_key)
dialogue_ss.user_memory.append([])
dialogue_ss.KB_wiki.append([])
dialogue_ss.sessionstep += 1
dialogue_ss.dialogue.append([chatinput,""])
# prepare the input for the model
dialogue = {"dialogue":[],"meta":[],"user":[],
"assistant":[],"user_memory":[],
"KB_wiki": [], "query_mem":[]}
dialogue["dialogue"] = dialogue_ss.dialogue
dialogue["meta"] = dialogue_ss.meta
dialogue["assistant"] = dialogue_ss.meta
dialogue["user"] = dialogue_ss.user
dialogue["user_memory"] = dialogue_ss.user_memory
dialogue["KB_wiki"] = dialogue_ss.KB_wiki
dialogue["query"] = ""
with chatlogholder:
components.html(render(dialogue_ss.dialogue, None), height=400, scrolling=True)
skill, skill_dist = select_prompt_interactive(model, tokenizer,
shot_converter=convert_sample_to_shot_selector,
dialogue=dialogue, prompt_dict=prompt_skill_selector,
device=device, max_seq=max_seq, max_shot=shot_selector)
if "unsa" in skill:
skill = "safe"
# print(f"FSB (Safety) >>> {response}")
print(f"Skill: {skill}")
print(skill_dist)
## parse user dialogue dialogue ==> msc-parse
dialogue = run_parsers(args, model, tokenizer, device=device, max_seq=max_seq,
dialogue=dialogue, skill=skill,
prefix_dict=prompt_parse, api=dialogue_ss.api, api_key=dialogue_ss.api_key)
## generate response based on skills
prompt = prompt_dict[skill].get(mapper[skill]["max_shot"][max_seq])
response = generate_response_interactive(model, tokenizer, shot_converter=mapper[skill]["shot_converter_inference"],
dialogue=dialogue, prefix=prompt,
device=device, with_knowledge=mapper[skill]["with_knowledge"],
meta_type=mapper[skill]["meta_type"], gen_len=50,
beam=1, max_seq=max_seq, eos_token_id=198,
do_sample=True, multigpu=False, api=dialogue_ss.api, api_key=dialogue_ss.api_key,
temperature=temperature, topp=topp)
print(f"FSB ({skill}) >>> {response}")
dialogue_ss.skill.append(skill)
dialogue_ss.prompt.append(prompt + f'*{mapper[skill]["shot_converter_inference"](dialogue)}*' + f" ***{response}***")
dialogue_ss.dialogue[-1][1] = response
dialogue_ss.dialogue = dialogue_ss.dialogue[-max_number_turns:]
dialogue_ss.user_memory = dialogue["user_memory"][-max_number_turns:]
dialogue_ss.KB_wiki = dialogue["KB_wiki"][-max_number_turns:]
dialogue_ss.user = dialogue["user"]
with chatlogholder:
components.html(render(dialogue_ss.dialogue, {"query":dialogue["query"],"wiki":dialogue["KB_wiki"][-1]}), height=400, scrolling=True)
with st.expander("Full chat dial"):
for i_p, prompt in enumerate(dialogue_ss.prompt):
st.markdown(f"### Turn {i_p}: Prompt of the {dialogue_ss.skill[i_p]} skill.")
st.markdown(prompt.replace("\n", "<br>"), unsafe_allow_html=True)
except:
raise
def main():
main_txt = """Welcome To Few-Shot Bot"""
sub_txt = "Just have fun"
subtitle = """**Instructions:** Type in some text and click "Chat" to generate a response. Optionally, adjust settings on the left.
"""
# display_app_header(main_txt,sub_txt,is_sidebar = False)
# st.markdown(subtitle)
st.sidebar.markdown(f'## Generation Settings')
# st.sidebar.markdown("""TEST TEST""")
mychat()
if __name__ == "__main__":
main()