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import argparse
import asyncio
import gradio as gr
import numpy as np
import time
import json
import os
import tempfile
import requests
import logging
from aiohttp import ClientSession
from langchain.text_splitter import RecursiveCharacterTextSplitter
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class Chunker:
def __init__(self, strategy, split_seq=".", chunk_len=512):
self.split_seq = split_seq
self.chunk_len = chunk_len
if strategy == "recursive":
self.split = RecursiveCharacterTextSplitter(
chunk_size=chunk_len,
separators=[split_seq]
).split_text
elif strategy == "sequence":
self.split = self.seq_splitter
elif strategy == "constant":
self.split = self.const_splitter
def seq_splitter(self, text):
return text.split(self.split_seq)
def const_splitter(self, text):
return [
text[i * self.chunk_len:(i + 1) * self.chunk_len]
for i in range(int(np.ceil(len(text) / self.chunk_len)))
]
def chunk_text(input_text, strategy, split_seq, chunk_len):
chunker = Chunker(strategy, split_seq, chunk_len)
chunks = chunker.split(input_text)
return chunks
async def embed_sent(sentence, tei_url):
payload = {
"inputs": sentence,
"truncate": True
}
async with ClientSession(
headers={
"Content-Type": "application/json",
}
) as session:
async with session.post(tei_url, json=payload) as resp:
if resp.status != 200:
raise RuntimeError(await resp.text())
result = await resp.json()
return result[0]
async def embed_first_sentence(chunks, tei_url):
if not chunks:
return [], []
first_sentence = chunks[0]
embedded_sentence = await embed_sent(first_sentence, tei_url)
return first_sentence, embedded_sentence
def wake_up_endpoint(url):
logger.info("Starting up TEI endpoint")
n_loop = 0
while requests.get(
url=url,
headers={"Content-Type": "application/json"}
).status_code != 200:
time.sleep(2)
n_loop += 1
if n_loop > 40:
raise gr.Error("TEI endpoint is unavailable")
logger.info("TEI endpoint is up")
async def process_text(input_text, strategy, split_seq, chunk_len, tei_url):
wake_up_endpoint(tei_url)
chunks = chunk_text(input_text, strategy, split_seq, chunk_len)
first_sentence, embedded_sentence = await embed_first_sentence(chunks, tei_url)
return chunks, first_sentence, embedded_sentence
def change_dropdown(choice):
if choice == "recursive":
return [
gr.Textbox(visible=True),
gr.Textbox(visible=True)
]
elif choice == "sequence":
return [
gr.Textbox(visible=True),
gr.Textbox(visible=False)
]
else:
return [
gr.Textbox(visible=False),
gr.Textbox(visible=True)
]
def main(args):
with gr.Blocks(theme='sudeepshouche/minimalist') as demo:
gr.Markdown("## Chunk and Embed")
input_text = gr.Textbox(lines=5, label="Input Text")
with gr.Row():
dropdown = gr.Dropdown(
["recursive", "sequence", "constant"], label="Chunking Strategy",
info="'recursive' uses a Langchain recursive tokenizer, 'sequence' splits texts by a chosen sequence, "
"'constant' makes chunks of the constant size",
scale=2
)
split_seq = gr.Textbox(
lines=1,
interactive=True,
visible=False,
label="Sequence",
info="A text sequence to split on",
placeholder="\n\n"
)
chunk_len = gr.Textbox(
lines=1,
interactive=True,
visible=False,
label="Length",
info="The length of chunks to split into in characters",
placeholder="512"
)
dropdown.change(fn=change_dropdown, inputs=dropdown, outputs=[split_seq, chunk_len])
tei_url = gr.Textbox(lines=1, label="TEI Endpoint URL")
with gr.Row():
clear = gr.ClearButton(components=[input_text, dropdown, split_seq, chunk_len, tei_url])
embed_btn = gr.Button("Submit")
embed_btn.click(
fn=process_text,
inputs=[input_text, dropdown, split_seq, chunk_len, tei_url],
outputs=[gr.JSON(label="Chunks"), gr.Textbox(label="First Chunked Sentence"), gr.JSON(label="Embedded Sentence")]
)
demo.queue()
demo.launch(server_name="0.0.0.0", server_port=args.port)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="A MAGIC example by ConceptaTech")
parser.add_argument("--port", type=int, default=7860, help="Port to expose Gradio app")
args = parser.parse_args()
main(args) |