import os
import re
import time
import json
from itertools import cycle
import torch
import gradio as gr
from urllib.parse import unquote
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList
from data import extract_leaves, split_document, handle_broken_output, clean_json_text, sync_empty_fields
from examples import examples as input_examples
from nuextract_logging import log_event
MAX_INPUT_SIZE = 10_000
MAX_NEW_TOKENS = 4_000
MAX_WINDOW_SIZE = 4_000
markdown_description = """
- NuMind is a startup developing custom information extraction solutions.
- NuExtract is a zero-shot model. See the blog posts for more info (NuExtract, NuExtract-v1.5).
- We have started to deploy NuMind Enterprise to customize, serve, and monitor NuExtract privately. If that interests you, let's chat 😊.
- Website: https://www.numind.ai/
NuExtract-v1.5
NuExtract-v1.5 is a fine-tuning of Phi-3.5-mini-instruct, trained on a private high-quality dataset for structured information extraction.
It supports long documents and several languages (English, French, Spanish, German, Portuguese, and Italian).
To use the model, provide an input text and a JSON template describing the information you need to extract.
⚠️ In this space we restrict the model inputs to a maximum length of 10k tokens, with anything over 4k being processed in a sliding window. For full model performance, self-host the model or contact us.
⚠️ The model is trained to assume a valid JSON template. Attempts to use invalid JSON could lead to unpredictable results.
"""
def highlight_words(input_text, json_output):
colors = cycle(["#90ee90", "#add8e6", "#ffb6c1", "#ffff99", "#ffa07a", "#20b2aa", "#87cefa", "#b0e0e6", "#dda0dd", "#ffdead"])
color_map = {}
highlighted_text = input_text
leaves = extract_leaves(json_output)
for path, value in leaves:
path_key = tuple(path)
if path_key not in color_map:
color_map[path_key] = next(colors)
color = color_map[path_key]
escaped_value = re.escape(value).replace(r'\ ', r'\s+') # escape value and replace spaces with \s+
pattern = rf"(?<=[ \n\t]){escaped_value}(?=[ \n\t\.\,\?\:\;])"
replacement = f"{unquote(value)}"
highlighted_text = re.sub(pattern, replacement, highlighted_text, flags=re.IGNORECASE)
return highlighted_text
def predict_chunk(text, template, current, model, tokenizer):
current = clean_json_text(current)
input_llm = f"<|input|>\n### Template:\n{template}\n### Current:\n{current}\n### Text:\n{text}\n\n<|output|>" + "{"
input_ids = tokenizer(input_llm, return_tensors="pt", truncation=True, max_length=MAX_INPUT_SIZE).to("cuda")
output = tokenizer.decode(model.generate(**input_ids, max_new_tokens=MAX_NEW_TOKENS)[0], skip_special_tokens=True)
return clean_json_text(output.split("<|output|>")[1])
def sliding_window_prediction(template, text, model, tokenizer, window_size=4000, overlap=128):
# Split text into chunks of n tokens
tokens = tokenizer.tokenize(text)
chunks = split_document(text, window_size, overlap, tokenizer)
# Iterate over text chunks
prev = template
full_pred = ""
for i, chunk in enumerate(chunks):
print(f"Processing chunk {i}...")
pred = predict_chunk(chunk, template, prev, model, tokenizer)
# Handle broken output
pred = handle_broken_output(pred, prev)
# create highlighted text
try:
highlighted_pred = highlight_words(text, json.loads(pred))
except:
highlighted_pred = text
# attempt json parsing
template_dict = None
pred_dict = None
try:
template_dict = json.loads(template)
except:
pass
try:
pred_dict = json.loads(pred)
except:
pass
# Sync empty fields
if template_dict and pred_dict:
synced_pred = sync_empty_fields(pred_dict, template_dict)
synced_pred = json.dumps(synced_pred, indent=4, ensure_ascii=False)
elif pred_dict:
synced_pred = json.dumps(pred_dict, indent=4, ensure_ascii=False)
else:
synced_pred = pred
# Return progress, current prediction, and updated HTML
yield f"Processed chunk {i+1}/{len(chunks)}", synced_pred, highlighted_pred
# Iterate
prev = pred
######
# Load the model and tokenizer
model_name = "numind/NuExtract-v1.5"
auth_token = os.environ.get("HF_TOKEN") or True
model = AutoModelForCausalLM.from_pretrained(model_name,
trust_remote_code=True,
torch_dtype=torch.bfloat16,
device_map="auto", use_auth_token=auth_token)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=auth_token)
model.eval()
def gradio_interface_function(template, text, is_example):
if len(tokenizer.tokenize(text)) > MAX_INPUT_SIZE:
yield "", "Input text too long for space. Download model to use unrestricted.", ""
return # End the function since there was an error
# Initialize the sliding window prediction process
prediction_generator = sliding_window_prediction(template, text, model, tokenizer, window_size=MAX_WINDOW_SIZE)
# Iterate over the generator to return values at each step
for progress, full_pred, html_content in prediction_generator:
# yield gr.update(value=chunk_info), gr.update(value=progress), gr.update(value=full_pred), gr.update(value=html_content)
yield progress, full_pred, html_content
if not is_example:
log_event(text, template, full_pred)
# Set up the Gradio interface
iface = gr.Interface(
description=markdown_description,
fn=gradio_interface_function,
inputs=[
gr.Textbox(lines=2, placeholder="Enter Template here...", label="Template"),
gr.Textbox(lines=2, placeholder="Enter input Text here...", label="Input Text"),
gr.Checkbox(label="Is Example?", visible=False),
],
outputs=[
gr.Textbox(label="Progress"),
gr.Textbox(label="Model Output"),
gr.HTML(label="Model Output with Highlighted Words"),
],
examples=input_examples,
# live=True # Enable real-time updates
)
iface.launch(debug=True, share=True)