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# -*- coding: utf-8 -*-
# file: app.py
# time: 17:08 2023/3/6
# author: YANG, HENG <hy345@exeter.ac.uk> (杨恒)
# github: https://github.com/yangheng95
# huggingface: https://huggingface.co/yangheng
# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en
# Copyright (C) 2023. All Rights Reserved.
import random
import autocuda
import gradio as gr
import pandas as pd
from pyabsa import (
download_all_available_datasets,
TaskCodeOption,
available_checkpoints,
)
from pyabsa import ABSAInstruction
from pyabsa.utils.data_utils.dataset_manager import detect_infer_dataset
import requests
download_all_available_datasets()
def get_atepc_example(dataset):
task = TaskCodeOption.Aspect_Polarity_Classification
dataset_file = detect_infer_dataset(atepc_dataset_items[dataset], task)
for fname in dataset_file:
lines = []
if isinstance(fname, str):
fname = [fname]
for f in fname:
print("loading: {}".format(f))
fin = open(f, "r", encoding="utf-8")
lines.extend(fin.readlines())
fin.close()
for i in range(len(lines)):
lines[i] = (
lines[i][: lines[i].find("$LABEL$")]
.replace("[B-ASP]", "")
.replace("[E-ASP]", "")
.strip()
)
return sorted(set(lines), key=lines.index)
def get_aste_example(dataset):
task = TaskCodeOption.Aspect_Sentiment_Triplet_Extraction
dataset_file = detect_infer_dataset(aste_dataset_items[dataset], task)
for fname in dataset_file:
lines = []
if isinstance(fname, str):
fname = [fname]
for f in fname:
print("loading: {}".format(f))
fin = open(f, "r", encoding="utf-8")
lines.extend(fin.readlines())
fin.close()
return sorted(set(lines), key=lines.index)
def get_acos_example(dataset):
task = "ACOS"
dataset_file = detect_infer_dataset(acos_dataset_items[dataset], task)
for fname in dataset_file:
lines = []
if isinstance(fname, str):
fname = [fname]
for f in fname:
print("loading: {}".format(f))
fin = open(f, "r", encoding="utf-8")
lines.extend(fin.readlines())
fin.close()
lines = [line.split("####")[0] for line in lines]
return sorted(set(lines), key=lines.index)
try:
from pyabsa import AspectTermExtraction as ATEPC
atepc_dataset_items = {
dataset.name: dataset for dataset in ATEPC.ATEPCDatasetList()
}
atepc_dataset_dict = {
dataset.name: get_atepc_example(dataset.name)
for dataset in ATEPC.ATEPCDatasetList()
}
aspect_extractor = ATEPC.AspectExtractor(checkpoint="multilingual")
except Exception as e:
print(e)
atepc_dataset_items = {}
atepc_dataset_dict = {}
aspect_extractor = None
try:
from pyabsa import AspectSentimentTripletExtraction as ASTE
aste_dataset_items = {dataset.name: dataset for dataset in ASTE.ASTEDatasetList()}
aste_dataset_dict = {
dataset.name: get_aste_example(dataset.name)
for dataset in ASTE.ASTEDatasetList()[:-1]
}
triplet_extractor = ASTE.AspectSentimentTripletExtractor(checkpoint="multilingual")
except Exception as e:
print(e)
aste_dataset_items = {}
aste_dataset_dict = {}
triplet_extractor = None
try:
from pyabsa import ABSAInstruction
acos_dataset_items = {
dataset.name: dataset for dataset in ABSAInstruction.ACOSDatasetList()
}
acos_dataset_dict = {
dataset.name: get_acos_example(dataset.name)
for dataset in ABSAInstruction.ACOSDatasetList()
}
quadruple_extractor = ABSAInstruction.ABSAGenerator("multilingual")
except Exception as e:
print(e)
acos_dataset_items = {}
acos_dataset_dict = {}
quadruple_extractor = None
def perform_atepc_inference(text, dataset):
if not text:
text = atepc_dataset_dict[dataset][
random.randint(0, len(atepc_dataset_dict[dataset]) - 1)
]
result = aspect_extractor.predict(text, pred_sentiment=True)
result = pd.DataFrame(
{
"aspect": result["aspect"],
"sentiment": result["sentiment"],
# 'probability': result[0]['probs'],
"confidence": [round(x, 4) for x in result["confidence"]],
"position": result["position"],
}
)
return result, "{}".format(text)
def perform_aste_inference(text, dataset):
if not text:
text = aste_dataset_dict[dataset][
random.randint(0, len(aste_dataset_dict[dataset]) - 1)
]
result = triplet_extractor.predict(text)
pred_triplets = pd.DataFrame(result["Triplets"])
true_triplets = pd.DataFrame(result["True Triplets"]) if result["True Triplets"] else None
return pred_triplets, true_triplets, "{}".format(text.split("####")[0])
def perform_acos_inference(text, dataset):
if not text:
text = acos_dataset_dict[dataset][
random.randint(0, len(acos_dataset_dict[dataset]) - 1)
]
raw_output = quadruple_extractor.predict(text.split("####")[0], max_length=128)
result = raw_output["Quadruples"]
result = pd.DataFrame(result)
return result, text
def run_demo(text, dataset, task):
if len(text) > 3000:
raise RuntimeError('Text is too long!')
try:
data = {
"text": text,
"dataset": dataset,
"task": task,
}
response = requests.post("https://pyabsa.pagekite.me/api/inference", json=data)
result = response.json()
print(response.json())
if task == "ATEPC":
return (
pd.DataFrame(
{
"aspect": result["aspect"],
"sentiment": result["sentiment"],
# 'probability': result[0]['probs'],
"confidence": [round(x, 4) for x in result["confidence"]],
"position": result["position"],
}
),
result["text"],
)
elif task == "ASTE":
return (
pd.DataFrame(result["pred_triplets"]),
pd.DataFrame(result["true_triplets"]),
result["text"],
)
elif task == "ACOS":
return pd.DataFrame(result["Quadruples"]), result["text"]
except Exception as e:
print(e)
print("Failed to connect to the server, running locally...")
return inference(text, dataset, task)
def inference(text, dataset, task):
if task == "ATEPC":
return perform_atepc_inference(text, dataset)
elif task == "ASTE":
return perform_aste_inference(text, dataset)
elif task == "ACOS":
return perform_acos_inference(text, dataset)
else:
raise Exception("No such task: {}".format(task))
if __name__ == "__main__":
demo = gr.Blocks()
with demo:
with gr.Row():
if quadruple_extractor:
with gr.Row():
with gr.Column():
gr.Markdown(
"# <p align='center'> ABSA Quadruple Extraction (Experimental) </p>"
)
acos_input_sentence = gr.Textbox(
placeholder="Leave this box blank and choose a dataset will give you a random example...",
label="Example:",
)
acos_dataset_ids = gr.Radio(
choices=[
dataset.name
for dataset in ABSAInstruction.ACOSDatasetList()
],
value="Laptop14",
label="Datasets",
)
acos_inference_button = gr.Button("Let's go!")
acos_output_text = gr.TextArea(label="Example:")
acos_output_pred_df = gr.DataFrame(label="Predicted Triplets:")
acos_inference_button.click(
fn=run_demo,
inputs=[
acos_input_sentence,
acos_dataset_ids,
gr.Text("ACOS", visible=False),
],
outputs=[acos_output_pred_df, acos_output_text],
)
with gr.Row():
if triplet_extractor:
with gr.Column():
gr.Markdown(
"# <p align='center'>Aspect Sentiment Triplet Extraction !</p>"
)
with gr.Row():
with gr.Column():
aste_input_sentence = gr.Textbox(
placeholder="Leave this box blank and choose a dataset will give you a random example...",
label="Example:",
)
gr.Markdown(
"You can find code and dataset at [ASTE examples](https://github.com/yangheng95/PyABSA/tree/v2/examples-v2/aspect_sentiment_triplet_extration)"
)
aste_dataset_ids = gr.Radio(
choices=[
dataset.name
for dataset in ASTE.ASTEDatasetList()[:-1]
],
value="Restaurant14",
label="Datasets",
)
aste_inference_button = gr.Button("Let's go!")
aste_output_text = gr.TextArea(label="Example:")
aste_output_pred_df = gr.DataFrame(
label="Predicted Triplets:"
)
aste_output_true_df = gr.DataFrame(
label="Original Triplets:"
)
aste_inference_button.click(
fn=run_demo,
inputs=[
aste_input_sentence,
aste_dataset_ids,
gr.Text("ASTE", visible=False),
],
outputs=[
aste_output_pred_df,
aste_output_true_df,
aste_output_text,
],
)
if aspect_extractor:
with gr.Column():
gr.Markdown(
"# <p align='center'>Multilingual Aspect-based Sentiment Analysis !</p>"
)
with gr.Row():
with gr.Column():
atepc_input_sentence = gr.Textbox(
placeholder="Leave this box blank and choose a dataset will give you a random example...",
label="Example:",
)
gr.Markdown(
"You can find the datasets at [github.com/yangheng95/ABSADatasets](https://github.com/yangheng95/ABSADatasets/tree/v1.2/datasets/text_classification)"
)
atepc_dataset_ids = gr.Radio(
choices=[
dataset.name
for dataset in ATEPC.ATEPCDatasetList()[:-1]
],
value="Laptop14",
label="Datasets",
)
atepc_inference_button = gr.Button("Let's go!")
atepc_output_text = gr.TextArea(label="Example:")
atepc_output_df = gr.DataFrame(label="Prediction Results:")
atepc_inference_button.click(
fn=run_demo,
inputs=[
atepc_input_sentence,
atepc_dataset_ids,
gr.Text("ATEPC", visible=False),
],
outputs=[atepc_output_df, atepc_output_text],
)
gr.Markdown(
"""### GitHub Repo: [PyABSA V2](https://github.com/yangheng95/PyABSA)
### Author: [Heng Yang](https://github.com/yangheng95) (杨恒)
[](https://pepy.tech/project/pyabsa)
[](https://pepy.tech/project/pyabsa)
"""
)
demo.launch()
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