Update app.py

app.py

@@ -8,22 +8,20 @@
 # 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,
-    AspectTermExtraction as ATEPC,
     TaskCodeOption,
     available_checkpoints,
 )
-from pyabsa import AspectSentimentTripletExtraction as ASTE
+from pyabsa import ABSAInstruction
 from pyabsa.utils.data_utils.dataset_manager import detect_infer_dataset
 
 download_all_available_datasets()
 
-atepc_dataset_items = {dataset.name: dataset for dataset in ATEPC.ATEPCDatasetList()}
-aste_dataset_items = {dataset.name: dataset for dataset in ASTE.ASTEDatasetList()}
-
 
 def get_atepc_example(dataset):
     task = TaskCodeOption.Aspect_Polarity_Classification
@@ -66,18 +64,66 @@ def get_aste_example(dataset):
         return sorted(set(lines), key=lines.index)
 
 
-available_checkpoints("ASTE", True)
+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)
 
-atepc_dataset_dict = {
-    dataset.name: get_atepc_example(dataset.name)
-    for dataset in ATEPC.ATEPCDatasetList()
-}
-aspect_extractor = ATEPC.AspectExtractor(checkpoint="multilingual")
 
-aste_dataset_dict = {
-    dataset.name: get_aste_example(dataset.name) for dataset in ASTE.ASTEDatasetList()
-}
-triplet_extractor = ASTE.AspectSentimentTripletExtractor(checkpoint="multilingual")
+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()
+    }
+    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):
@@ -113,68 +159,108 @@ def perform_aste_inference(text, dataset):
     return pred_triplets, true_triplets, "{}".format(text)
 
 
-demo = gr.Blocks()
+def perform_acos_inference(text, dataset):
+    if not text:
+        text = acos_dataset_dict[dataset][
+            random.randint(0, len(acos_dataset_dict[dataset]) - 1)
+        ]
 
-with demo:
-    with gr.Row():
+    raw_output = quadruple_extractor.predict(text.split('####')[0])
 
-        with gr.Column():
-            gr.Markdown("# <p align='center'>Aspect Sentiment Triplet Extraction !</p>")
+    result = raw_output['Quadruple']
+    result = pd.DataFrame(result)
+    return result, text
 
-            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:")
+demo = gr.Blocks()
 
-                    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],
-                    )
+with demo:
+    with gr.Row():
 
-        with gr.Column():
-            gr.Markdown(
-                "# <p align='center'>Multilingual Aspect-based Sentiment Analysis !</p>"
-            )
+        if quadruple_extractor:
             with gr.Row():
                 with gr.Column():
-                    atepc_input_sentence = gr.Textbox(
+                    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:",
                     )
-                    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",
+                    acos_dataset_ids = gr.Radio(
+                        choices=[dataset.name for dataset in ABSAInstruction.ACOSDatasetList()],
+                        value="Restaurant16",
                         label="Datasets",
                     )
-                    atepc_inference_button = gr.Button("Let's go!")
+                    acos_inference_button = gr.Button("Let's go!")
 
-                    atepc_output_text = gr.TextArea(label="Example:")
-                    atepc_output_df = gr.DataFrame(label="Prediction Results:")
+                    acos_output_text = gr.TextArea(label="Example:")
+                    acos_output_pred_df = gr.DataFrame(label="Predicted Triplets:")
 
-                    atepc_inference_button.click(
-                        fn=perform_atepc_inference,
-                        inputs=[atepc_input_sentence, atepc_dataset_ids],
-                        outputs=[atepc_output_df, atepc_output_text],
+                    acos_inference_button.click(
+                        fn=perform_acos_inference,
+                        inputs=[acos_input_sentence, acos_dataset_ids],
+                        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=perform_aste_inference,
+                            inputs=[aste_input_sentence, aste_dataset_ids],
+                            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=perform_atepc_inference,
+                            inputs=[atepc_input_sentence, atepc_dataset_ids],
+                            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)
@@ -183,4 +269,4 @@ with demo:
         """
     )
 
-demo.launch()
+demo.launch()