update_ckpt

app.py

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+import os
+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.utils.data_utils.dataset_manager import detect_infer_dataset
+
+download_all_available_datasets()
+
+dataset_items = {dataset.name: dataset for dataset in ATEPC.ATEPCDatasetList()}
+
+URL = 'https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fyangheng%2Fpyabsa_inference&label=Inference%20Count&labelColor=%2337d67a&countColor=%23f47373&style=flat&labelStyle=none'
+
+
+def get_example(dataset):
+    task = TaskCodeOption.Aspect_Polarity_Classification
+    dataset_file = detect_infer_dataset(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)
+
+
+dataset_dict = {dataset.name: get_example(dataset.name) for dataset in ATEPC.ATEPCDatasetList()}
+aspect_extractor = ATEPC.AspectExtractor(checkpoint='multilingual')
+
+
+def perform_inference(text, dataset):
+    if not text:
+        text = dataset_dict[dataset][random.randint(0, len(dataset_dict[dataset]) - 1)]
+
+    result = aspect_extractor.predict(example=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']
+    })
+    requests.get(URL)
+    return result, '{}'.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.**")
+    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.")
+
+        with gr.Column():
+            output_text = gr.TextArea(label="Example:")
+            output_df = gr.DataFrame(label="Prediction Results:")
+            output_dfs.append(output_df)
+
+        inference_button.click(fn=perform_inference,
+                               inputs=[input_sentence, dataset_ids],
+                               outputs=[output_df, output_text])
+
+    gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=https://huggingface.co/spaces/yangheng/Multilingual-Aspect-Based-Sentiment-Analysis)")
+    gr.Markdown("![Visitors]({})".format(URL))
+
+demo.launch(share=True)

requirements.txt

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+pyabsa>=2.0.0b0