update_app

app.py

@@ -1,19 +1,33 @@
-import os
+# -*- 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 gradio as gr
 import pandas as pd
-import requests
-
-from pyabsa import download_all_available_datasets, AspectTermExtraction as ATEPC, TaskCodeOption
+from pyabsa import (
+    download_all_available_datasets,
+    AspectTermExtraction as ATEPC,
+    TaskCodeOption,
+    available_checkpoints,
+)
+from pyabsa import AspectSentimentTripletExtraction as ASTE
 from pyabsa.utils.data_utils.dataset_manager import detect_infer_dataset
 
 download_all_available_datasets()
 
-dataset_items = {dataset.name: dataset for dataset in ATEPC.ATEPCDatasetList()}
+atepc_dataset_items = {dataset.name: dataset for dataset in ATEPC.ATEPCDatasetList()}
+aste_dataset_items = {dataset.name: dataset for dataset in ASTE.ASTEDatasetList()}
+
 
-def get_example(dataset):
+def get_atepc_example(dataset):
     task = TaskCodeOption.Aspect_Polarity_Classification
-    dataset_file = detect_infer_dataset(dataset_items[dataset], task)
+    dataset_file = detect_infer_dataset(atepc_dataset_items[dataset], task)
 
     for fname in dataset_file:
         lines = []
@@ -21,65 +35,165 @@ def get_example(dataset):
             fname = [fname]
 
         for f in fname:
-            print('loading: {}'.format(f))
-            fin = open(f, 'r', encoding='utf-8')
+            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()
+            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)
 
 
-dataset_dict = {dataset.name: get_example(dataset.name) for dataset in ATEPC.ATEPCDatasetList()}
-aspect_extractor = ATEPC.AspectExtractor(checkpoint='multilingual')
+available_checkpoints("ASTE", True)
 
+atepc_dataset_dict = {
+    dataset.name: get_atepc_example(dataset.name)
+    for dataset in ATEPC.ATEPCDatasetList()
+}
+aspect_extractor = ATEPC.AspectExtractor(checkpoint="multilingual")
 
-def perform_inference(text, dataset):
+aste_dataset_dict = {
+    dataset.name: get_aste_example(dataset.name) for dataset in ASTE.ASTEDatasetList()
+}
+triplet_extractor = ASTE.AspectSentimentTripletExtractor(checkpoint="english")
+
+
+def perform_atepc_inference(text, dataset):
     if not text:
-        text = dataset_dict[dataset][random.randint(0, len(dataset_dict[dataset]) - 1)]
+        text = atepc_dataset_dict[dataset][
+            random.randint(0, len(atepc_dataset_dict[dataset]) - 1)
+        ]
+
+    result = aspect_extractor.predict(text, pred_sentiment=True)
 
-    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)
 
-    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"])
+    return pred_triplets, true_triplets, "{}".format(text)
 
 
 demo = gr.Blocks()
 
 with demo:
-    gr.Markdown("# <p align='center'>Multilingual Aspect-based Sentiment Analysis !</p>")
-    gr.Markdown("""### Repo: [PyABSA V2](https://github.com/yangheng95/PyABSA)
-                ### Author: [Heng Yang](https://github.com/yangheng95) (杨恒)
-                [![Downloads](https://pepy.tech/badge/pyabsa)](https://pepy.tech/project/pyabsa) 
-                [![Downloads](https://pepy.tech/badge/pyabsa/month)](https://pepy.tech/project/pyabsa)
-                """
-                )
-    gr.Markdown("Your input text should be no more than 80 words, that's the longest text we used in trainer. However, you can try longer text in self-trainer ")
-    gr.Markdown("**You don't need to split each Chinese (Korean, etc.) token as the provided, just input the natural language text.**")
+
+    gr.Markdown("# <p align='center'>Aspect Sentiment Triplet Extraction !</p>")
+
+    gr.Markdown(
+        "Your input text should be no more than 80 words, that's the longest text we used in trainer. "
+        "However, you can try longer text in self-trainer "
+    )
+    gr.Markdown(
+        "**You don't need to split each Chinese (Korean, etc.) token as the provided,"
+        " just input the natural language text.**"
+    )
     output_dfs = []
+
+
     with gr.Row():
         with gr.Column():
-            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)")
-            dataset_ids = gr.Radio(choices=[dataset.name for dataset in ATEPC.ATEPCDatasetList()[:-1]], value='Laptop14', label="Datasets")
-            inference_button = gr.Button("Let's go!")
-            gr.Markdown("There is a [demo](https://huggingface.co/spaces/yangheng/PyABSA-ATEPC-Chinese) specialized for the Chinese langauge")
-            gr.Markdown("This demo support many other language as well, you can try and explore the results of other languages by yourself.")
+            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/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!")
 
         with gr.Column():
-            output_text = gr.TextArea(label="Example:")
-            output_df = gr.DataFrame(label="Prediction Results:")
-            output_dfs.append(output_df)
+            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:")
 
-        inference_button.click(fn=perform_inference,
-                               inputs=[input_sentence, dataset_ids],
-                               outputs=[output_df, output_text])
+        aste_inference_button.click(
+            fn=perform_aste_inference,
+            inputs=[aste_input_sentence, aste_dataset_ids],
+            outputs=[aste_output_pred_df, aste_output_true_df, aste_output_text],
+        )
 
+
+    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!")
+
+        with gr.Column():
+            atepc_output_text = gr.TextArea(label="Example:")
+            atepc_output_df = gr.DataFrame(label="Prediction Results:")
+
+        atepc_inference_button.click(
+            fn=perform_atepc_inference,
+            inputs=[atepc_input_sentence, atepc_dataset_ids],
+            outputs=[atepc_output_df, atepc_output_text],
+        )
+    gr.Markdown(
+        """### Repo: [PyABSA V2](https://github.com/yangheng95/PyABSA)
+                ### Author: [Heng Yang](https://github.com/yangheng95) (杨恒)
+                [![Downloads](https://pepy.tech/badge/pyabsa)](https://pepy.tech/project/pyabsa) 
+                [![Downloads](https://pepy.tech/badge/pyabsa/month)](https://pepy.tech/project/pyabsa)
+                """
+    )
+    gr.Markdown(
+        "This demo support many other language as well, you can try and explore the results of other languages by yourself."
+    )
 demo.launch()