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import gradio as gr
import random
from recurrentgpt import RecurrentGPT
from human_simulator import Human
from sentence_transformers import SentenceTransformer
from utils import get_init, parse_instructions
import re
###################################################################################################################################################################
###################################################################################################################################################################
# TODO: add user database
# from urllib.parse import quote_plus
# from pymongo import MongoClient
# uri = "mongodb://%s:%s@%s" % (quote_plus("xxx"),
# quote_plus("xxx"), "localhost")
# client = MongoClient(uri, maxPoolSize=None)
# db = client.recurrentGPT_db
# log = db.log
###################################################################################################################################################################
###################################################################################################################################################################
_CACHE = {}
# TODO : try with different embedding models
## Build the semantic search model
embedder = SentenceTransformer('multi-qa-mpnet-base-cos-v1')
# TODO: make a prompt class
def init_prompt(text_type, description, structure, units, tone):
if description == "":
description = ""
else:
description = " about " + description
return f"""
Please write a {text_type} about {description} with {units} {structure}. Follow the format below precisely:
Begin with the title of the {text_type}.
Next, write an outline for the first {structure}. The outline should describe the {description} and the beginning of the {text_type}.
Write the first three paragraphs with their indication of the {text_type} based on your outline. Write in a {tone} style and take your time to set the context.
Write a summary that captures the key information of the three paragraphs.
Finally, write three different instructions for what to write next, each containing around five sentences. Each instruction should present a possible, interesting continuation of the {text_type}.
The output format should follow these guidelines:
Name: <name of the {text_type}>
Outline: <outline for the first {structure}>
Paragraph 1: <content for paragraph 1>
Paragraph 2: <content for paragraph 2>
Paragraph 3: <content for paragraph 3>
Summary: <content of summary>
Instruction 1: <content for instruction 1>
Instruction 2: <content for instruction 2>
Instruction 3: <content for instruction 3>
Make sure to be precise and follow the output format strictly.
"""
def init(text_type, description, request: gr.Request):
if tex_type == "":
text_type = "Linkedin post"
global _CACHE
cookie = request.headers['cookie']
cookie = cookie.split('; _gat_gtag')[0]
# prepare first init
init_paragraphs = get_init(text=init_prompt(text_type, description, structure, units, tone))
# print(init_paragraphs)
start_input_to_human = {
'output_paragraph': init_paragraphs['Paragraph 3'],
'input_paragraph': '\n\n'.join([init_paragraphs['Paragraph 1'], init_paragraphs['Paragraph 2']]),
'output_memory': init_paragraphs['Summary'],
"output_instruction": [init_paragraphs['Instruction 1'], init_paragraphs['Instruction 2'], init_paragraphs['Instruction 3']]
}
_CACHE[cookie] = {"start_input_to_human": start_input_to_human,
"init_paragraphs": init_paragraphs}
written_paras = f"""Title: {init_paragraphs['name']}
Outline: {init_paragraphs['Outline']}
Paragraphs:
{start_input_to_human['input_paragraph']}"""
long_memory = parse_instructions([init_paragraphs['Paragraph 1'], init_paragraphs['Paragraph 2']])
# short memory, long memory, current written paragraphs, 3 next instructions
return start_input_to_human['output_memory'], long_memory, written_paras, init_paragraphs['Instruction 1'], init_paragraphs['Instruction 2'], init_paragraphs['Instruction 3']
def step(short_memory, long_memory, instruction1, instruction2, instruction3, current_paras, request: gr.Request, ):
if current_paras == "":
return "", "", "", "", "", ""
global _CACHE
# print(list(_CACHE.keys()))
# print(request.headers.get('cookie'))
cookie = request.headers['cookie']
cookie = cookie.split('; _gat_gtag')[0]
cache = _CACHE[cookie]
if "writer" not in cache:
start_input_to_human = cache["start_input_to_human"]
start_input_to_human['output_instruction'] = [
instruction1, instruction2, instruction3]
init_paragraphs = cache["init_paragraphs"]
human = Human(input=start_input_to_human,
memory=None, embedder=embedder)
human.step()
start_short_memory = init_paragraphs['Summary']
writer_start_input = human.output
# Init writerGPT
writer = RecurrentGPT(input=writer_start_input, short_memory=start_short_memory, long_memory=[
init_paragraphs['Paragraph 1'], init_paragraphs['Paragraph 2']], memory_index=None, embedder=embedder)
cache["writer"] = writer
cache["human"] = human
writer.step()
else:
human = cache["human"]
writer = cache["writer"]
output = writer.output
output['output_memory'] = short_memory
#randomly select one instruction out of three
instruction_index = random.randint(0,2)
output['output_instruction'] = [instruction1, instruction2, instruction3][instruction_index]
human.input = output
human.step()
writer.input = human.output
writer.step()
long_memory = [[v] for v in writer.long_memory]
# short memory, long memory, current written paragraphs, 3 next instructions
return writer.output['output_memory'], long_memory, current_paras + '\n\n' + writer.output['input_paragraph'], human.output['output_instruction'], *writer.output['output_instruction']
def controled_step(short_memory, long_memory, selected_instruction, current_paras, request: gr.Request, ):
if current_paras == "":
return "", "", "", "", "", ""
global _CACHE
# print(list(_CACHE.keys()))
# print(request.headers.get('cookie'))
cookie = request.headers['cookie']
cookie = cookie.split('; _gat_gtag')[0]
cache = _CACHE[cookie]
if "writer" not in cache:
start_input_to_human = cache["start_input_to_human"]
start_input_to_human['output_instruction'] = selected_instruction
init_paragraphs = cache["init_paragraphs"]
human = Human(input=start_input_to_human,
memory=None, embedder=embedder)
human.step()
start_short_memory = init_paragraphs['Summary']
writer_start_input = human.output
# Init writerGPT
writer = RecurrentGPT(input=writer_start_input, short_memory=start_short_memory, long_memory=[
init_paragraphs['Paragraph 1'], init_paragraphs['Paragraph 2']], memory_index=None, embedder=embedder)
cache["writer"] = writer
cache["human"] = human
writer.step()
else:
human = cache["human"]
writer = cache["writer"]
output = writer.output
output['output_memory'] = short_memory
output['output_instruction'] = selected_instruction
human.input = output
human.step()
writer.input = human.output
writer.step()
# short memory, long memory, current written paragraphs, 3 next instructions
return writer.output['output_memory'], parse_instructions(writer.long_memory), current_paras + '\n\n' + writer.output['input_paragraph'], *writer.output['output_instruction']
# SelectData is a subclass of EventData
def on_select(instruction1, instruction2, instruction3, evt: gr.SelectData):
selected_plan = int(evt.value.replace("Instruction ", ""))
selected_plan = [instruction1, instruction2, instruction3][selected_plan-1]
return selected_plan
with gr.Blocks(title="RecurrentGPT", css="footer {visibility: hidden}", theme='sudeepshouche/minimalist') as demo:
gr.Markdown(
"""
# RecurrentGPT
Interactive Generation of (Arbitrarily) Long Texts with Human-in-the-Loop
""")
with gr.Tab("Auto-Generation"):
with gr.Row():
with gr.Column():
with gr.Box():
with gr.Row():
with gr.Column(scale=1, min_width=200):
novel_type = gr.Textbox(
label="Novel Type", placeholder="e.g. science fiction")
with gr.Column(scale=2, min_width=400):
description = gr.Textbox(label="Description")
btn_init = gr.Button(
"Init Novel Generation", variant="primary")
gr.Examples(["Science Fiction", "Romance", "Mystery", "Fantasy",
"Historical", "Horror", "Thriller", "Western", "Young Adult", ], inputs=[novel_type])
written_paras = gr.Textbox(
label="Written Paragraphs (editable)", max_lines=21, lines=21)
with gr.Column():
with gr.Box():
gr.Markdown("### Memory Module\n")
short_memory = gr.Textbox(
label="Short-Term Memory (editable)", max_lines=3, lines=3)
long_memory = gr.Textbox(
label="Long-Term Memory (editable)", max_lines=6, lines=6)
# long_memory = gr.Dataframe(
# # label="Long-Term Memory (editable)",
# headers=["Long-Term Memory (editable)"],
# datatype=["str"],
# row_count=3,
# max_rows=3,
# col_count=(1, "fixed"),
# type="array",
# )
with gr.Box():
gr.Markdown("### Instruction Module\n")
with gr.Row():
instruction1 = gr.Textbox(
label="Instruction 1 (editable)", max_lines=4, lines=4)
instruction2 = gr.Textbox(
label="Instruction 2 (editable)", max_lines=4, lines=4)
instruction3 = gr.Textbox(
label="Instruction 3 (editable)", max_lines=4, lines=4)
selected_plan = gr.Textbox(
label="Revised Instruction (from last step)", max_lines=2, lines=2)
btn_step = gr.Button("Next Step", variant="primary")
btn_init.click(init, inputs=[novel_type, description], outputs=[
short_memory, long_memory, written_paras, instruction1, instruction2, instruction3])
btn_step.click(step, inputs=[short_memory, long_memory, instruction1, instruction2, instruction3, written_paras], outputs=[
short_memory, long_memory, written_paras, selected_plan, instruction1, instruction2, instruction3])
with gr.Tab("Human-in-the-Loop"):
with gr.Row():
with gr.Column():
with gr.Box():
with gr.Row():
with gr.Column(scale=1, min_width=200):
novel_type = gr.Textbox(
label="Novel Type", placeholder="e.g. science fiction")
with gr.Column(scale=2, min_width=400):
description = gr.Textbox(label="Description")
btn_init = gr.Button(
"Init Novel Generation", variant="primary")
gr.Examples(["Science Fiction", "Romance", "Mystery", "Fantasy",
"Historical", "Horror", "Thriller", "Western", "Young Adult", ], inputs=[novel_type])
written_paras = gr.Textbox(
label="Written Paragraphs (editable)", max_lines=23, lines=23)
with gr.Column():
with gr.Box():
gr.Markdown("### Memory Module\n")
short_memory = gr.Textbox(
label="Short-Term Memory (editable)", max_lines=3, lines=3)
long_memory = gr.Textbox(
label="Long-Term Memory (editable)", max_lines=6, lines=6)
with gr.Box():
gr.Markdown("### Instruction Module\n")
with gr.Row():
instruction1 = gr.Textbox(
label="Instruction 1", max_lines=3, lines=3, interactive=False)
instruction2 = gr.Textbox(
label="Instruction 2", max_lines=3, lines=3, interactive=False)
instruction3 = gr.Textbox(
label="Instruction 3", max_lines=3, lines=3, interactive=False)
with gr.Row():
with gr.Column(scale=1, min_width=100):
selected_plan = gr.Radio(["Instruction 1", "Instruction 2", "Instruction 3"], label="Instruction Selection",)
# info="Select the instruction you want to revise and use for the next step generation.")
with gr.Column(scale=3, min_width=300):
selected_instruction = gr.Textbox(
label="Selected Instruction (editable)", max_lines=5, lines=5)
btn_step = gr.Button("Next Step", variant="primary")
btn_init.click(init, inputs=[novel_type, description], outputs=[
short_memory, long_memory, written_paras, instruction1, instruction2, instruction3])
btn_step.click(controled_step, inputs=[short_memory, long_memory, selected_instruction, written_paras], outputs=[
short_memory, long_memory, written_paras, instruction1, instruction2, instruction3])
selected_plan.select(on_select, inputs=[
instruction1, instruction2, instruction3], outputs=[selected_instruction])
demo.queue(concurrency_count=1)
if __name__ == "__main__":
demo.launch() |