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import re
import copy
import json
import datasets
import gradio as gr
import pandas as pd
from pingpong import PingPong
from pingpong.context import CtxLastWindowStrategy
from gen.openllm import gen_text as open_llm_gen_text
from gen.gemini_chat import gen_text as gemini_gen_text
from gen.gemini_chat import init as gemini_init
from constants.context import DEFAULT_GLOBAL_CTX
from paper.download import get_papers_from_arxiv_ids
from init import (
requested_arxiv_ids_df,
date_dict,
arxivid2data,
dataset_repo_id,
request_arxiv_repo_id,
hf_token,
gemini_api_key
)
from utils import push_to_hf_hub
gemini_init(gemini_api_key)
def get_paper_by_year(year):
months = sorted(date_dict[year].keys())
last_month = months[-1]
days = sorted(date_dict[year][last_month].keys())
last_day = days[-1]
papers = list(set(
[paper["title"] for paper in date_dict[year][last_month][last_day]]
))
return (
gr.Dropdown(choices=months, value=last_month),
gr.Dropdown(choices=days, value=last_day),
gr.Dropdown(choices=papers, value=papers[0])
)
def get_paper_by_month(year, month):
days = sorted(date_dict[year][month].keys())
last_day = days[-1]
papers = list(set(
[paper["title"] for paper in date_dict[year][month][last_day]]
))
return (
gr.Dropdown(choices=days, value=last_day),
gr.Dropdown(choices=papers, value=papers[0])
)
def get_paper_by_day(year, month, day):
papers = list(set(
[paper["title"] for paper in date_dict[year][month][day]]
))
return gr.Dropdown(choices=papers, value=papers[0])
# 2307.02040
def set_papers(year, month, day, title):
title = title.split("]")[1].strip()
papers = []
for paper in date_dict[year][month][day]:
papers.append(paper["title"])
if paper["title"] == title:
arxiv_id = paper["arxiv_id"]
papers = list(set(papers))
return (
arxiv_id,
gr.Dropdown(choices=papers, value=title),
gr.Textbox("")
)
def set_paper(year, month, day, paper_title):
selected_paper = None
for paper in date_dict[year][month][day]:
if paper["title"] == paper_title:
selected_paper = paper
break
return (
selected_paper['arxiv_id'],
gr.Markdown(f"# {selected_paper['title']}"),
gr.Markdown(
"[](https://arxiv.org/abs/%s)" % (selected_paper['arxiv_id'], selected_paper['arxiv_id']) + " "
"[](https://huggingface.co/papers/%s)" % selected_paper['arxiv_id']
),
gr.Markdown(selected_paper["summary"]),
gr.Markdown(f"### 🙋 {selected_paper['0_question']}"),
gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_answers:eli5']}"),
gr.Markdown(f"↪ **(Technical)** {selected_paper['0_answers:expert']}"),
gr.Markdown(f"### 🙋🙋 {selected_paper['0_additional_depth_q:follow up question']}"),
gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_additional_depth_q:answers:eli5']}"),
gr.Markdown(f"↪ **(Technical)** {selected_paper['0_additional_depth_q:answers:expert']}"),
gr.Markdown(f"### 🙋🙋 {selected_paper['0_additional_breath_q:follow up question']}"),
gr.Markdown(f"↪ **(ELI5)** {selected_paper['0_additional_breath_q:answers:eli5']}"),
gr.Markdown(f"↪ **(Technical)** {selected_paper['0_additional_breath_q:answers:expert']}"),
gr.Markdown(f"### 🙋 {selected_paper['1_question']}"),
gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_answers:eli5']}"),
gr.Markdown(f"↪ **(Technical)** {selected_paper['1_answers:expert']}"),
gr.Markdown(f"### 🙋🙋 {selected_paper['1_additional_depth_q:follow up question']}"),
gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_additional_depth_q:answers:eli5']}"),
gr.Markdown(f"↪ **(Technical)** {selected_paper['1_additional_depth_q:answers:expert']}"),
gr.Markdown(f"### 🙋🙋 {selected_paper['1_additional_breath_q:follow up question']}"),
gr.Markdown(f"↪ **(ELI5)** {selected_paper['1_additional_breath_q:answers:eli5']}"),
gr.Markdown(f"↪ **(Technical)** {selected_paper['1_additional_breath_q:answers:expert']}"),
gr.Markdown(f"### 🙋 {selected_paper['2_question']}"),
gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_answers:eli5']}"),
gr.Markdown(f"↪ **(Technical)** {selected_paper['2_answers:expert']}"),
gr.Markdown(f"### 🙋🙋 {selected_paper['2_additional_depth_q:follow up question']}"),
gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_additional_depth_q:answers:eli5']}"),
gr.Markdown(f"↪ **(Technical)** {selected_paper['2_additional_depth_q:answers:expert']}"),
gr.Markdown(f"### 🙋🙋 {selected_paper['2_additional_breath_q:follow up question']}"),
gr.Markdown(f"↪ **(ELI5)** {selected_paper['2_additional_breath_q:answers:eli5']}"),
gr.Markdown(f"↪ **(Technical)** {selected_paper['2_additional_breath_q:answers:expert']}"),
)
def set_date(title):
title = title.split("]")[1].strip()
for _, (year, months) in enumerate(date_dict.items()):
for _, (month, days) in enumerate(months.items()):
for _, (day, papers) in enumerate(days.items()):
for paper in papers:
if paper['title'] == title:
return (
gr.Dropdown(value=year),
gr.Dropdown(choices=sorted(months), value=month),
gr.Dropdown(choices=sorted(days), value=day),
)
def change_exp_type(exp_type):
if exp_type == "ELI5":
return (
gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False),
gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False),
gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False),
)
else:
return (
gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True),
gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True),
gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True), gr.Markdown(visible=False), gr.Markdown(visible=True),
)
def _filter_duplicate_arxiv_ids(arxiv_ids_to_be_added):
ds1 = datasets.load_dataset(request_arxiv_repo_id)
ds2 = datasets.load_dataset(dataset_repo_id)
unique_arxiv_ids = set()
for d in ds1['train']:
arxiv_ids = d['Requested arXiv IDs']
unique_arxiv_ids = set(list(unique_arxiv_ids) + arxiv_ids)
if len(ds2) > 1:
for d in ds2['train']:
arxiv_id = d['arxiv_id']
unique_arxiv_ids.add(arxiv_id)
return list(set(arxiv_ids_to_be_added) - unique_arxiv_ids)
def _is_arxiv_id_valid(arxiv_id):
pattern = r"^\d{4}\.\d{5}$"
return bool(re.match(pattern, arxiv_id))
def _get_valid_arxiv_ids(arxiv_ids_str):
valid_arxiv_ids = []
invalid_arxiv_ids = []
for arxiv_id in arxiv_ids_str.split(","):
arxiv_id = arxiv_id.strip()
if _is_arxiv_id_valid(arxiv_id):
valid_arxiv_ids.append(arxiv_id)
else:
invalid_arxiv_ids.append(arxiv_id)
return valid_arxiv_ids, invalid_arxiv_ids
def add_arxiv_ids_to_queue(queue, arxiv_ids_str):
valid_arxiv_ids, invalid_arxiv_ids = _get_valid_arxiv_ids(arxiv_ids_str)
if len(invalid_arxiv_ids) > 0:
gr.Warning(f"found invalid arXiv ids as in {invalid_arxiv_ids}")
if len(valid_arxiv_ids) > 0:
valid_arxiv_ids = _filter_duplicate_arxiv_ids(valid_arxiv_ids)
if len(valid_arxiv_ids) > 0:
papers = get_papers_from_arxiv_ids(valid_arxiv_ids)
valid_arxiv_ids = [[f"[{paper['paper']['id']}] {paper['title']}"] for paper in papers]
gr.Warning(f"Processing [{valid_arxiv_ids}]. Other requested arXiv IDs not found on this list should be already processed or being processed...")
valid_arxiv_ids = pd.DataFrame({'Requested arXiv IDs': valid_arxiv_ids})
queue = pd.concat([queue, valid_arxiv_ids])
queue.reset_index(drop=True)
ds = datasets.Dataset.from_pandas(valid_arxiv_ids)
push_to_hf_hub(ds, request_arxiv_repo_id, hf_token)
else:
gr.Warning(f"All requested arXiv IDs are already processed or being processed...")
else:
gr.Warning(f"No valid arXiv IDs found...")
return (
queue, gr.Textbox("")
)
# Chat
def before_chat_begin():
return (
gr.Button(interactive=False),
gr.Button(interactive=False),
gr.Button(interactive=False)
)
def _build_prompts(ppmanager, global_context, win_size=3):
dummy_ppm = copy.deepcopy(ppmanager)
dummy_ppm.ctx = global_context
lws = CtxLastWindowStrategy(win_size)
return lws(dummy_ppm)
async def chat_stream(idx, local_data, user_prompt, chat_state, ctx_num_lconv=3):
paper = arxivid2data[idx]['paper']
ppm = chat_state["ppmanager_type"].from_json(json.dumps(local_data))
ppm.add_pingpong(
PingPong(
user_prompt,
""
)
)
prompt = _build_prompts(ppm, DEFAULT_GLOBAL_CTX % paper["full_text"].replace("\n", " ")[:30000], ctx_num_lconv)
# async for result in open_llm_gen_text(
# prompt,
# hf_model='meta-llama/Llama-2-70b-chat-hf', hf_token=hf_token,
# parameters={
# 'max_new_tokens': 4906,
# 'do_sample': True,
# 'return_full_text': False,
# 'temperature': 0.7,
# 'top_k': 10,
# 'repetition_penalty': 1.2
# }
# ):
try:
async for result in gemini_gen_text(prompt):
ppm.append_pong(result)
yield "", ppm.build_uis(), str(ppm), gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False)
yield "", ppm.build_uis(), str(ppm), gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)
except Exception as e:
gr.Warning(str(e))
ppm.replace_last_pong("Gemini refused to answer. This happens becase there were some safety issues in the answer.")
yield "", ppm.build_uis(), str(ppm), gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)
def chat_reset(local_data, chat_state):
ppm = chat_state["ppmanager_type"].from_json(json.dumps(local_data))
ppm.pingpongs = []
return "", ppm.build_uis(), str(ppm), gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True) |