adding gradio interface for aspect polarity detection

app.py

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+import locale
+locale.getpreferredencoding = lambda: "UTF-8"
+from pyabsa import available_checkpoints
+from pyabsa import ATEPCCheckpointManager
+import gradio as gr
+
+
+
+def annotate_sentiment(tokens, aspect_positions, sentiment_list):
+    """
+    Function to post-process the output from the aspect extractor model.
+    Annotates tokens based on sentiment, either positive (+) or negative (-).
+    
+    :param tokens: list of tokens to be processed
+    :param aspect_positions: list of positions in tokens that refer to aspects
+    :param sentiment_list: list of sentiments corresponding to each aspect
+    :return: list of tuples where each tuple contains a token and its corresponding sentiment (if any)
+    """
+    annotated_tokens = []
+    aspect_index = 0
+    for i, token_group in enumerate(tokens):
+        if aspect_index < len(aspect_positions) and i == aspect_positions[aspect_index]:
+            for token in token_group:
+                sentiment = '+' if sentiment_list[aspect_index] == 'Positive' else '-'
+                annotated_tokens.append((token, sentiment))
+            aspect_index += 1
+        else:
+            for token in token_group:
+                annotated_tokens.append((token, None))
+
+        # Add space between groups of tokens, if it's not the last group
+        if i != len(tokens) - 1:
+            annotated_tokens.append((' ', None))
+    
+    return annotated_tokens
+
+
+def annotate_text_sentiment(text):
+    """
+    Interface function to extract aspects and their sentiments from the given text.
+    Uses an aspect extractor model, then processes its output for sentiment annotation.
+    
+    :param text: string to be processed
+    :return: list of tuples where each tuple contains a token from the text and its corresponding sentiment (if any)
+    """
+    aspect_extraction_result = aspect_extractor.extract_aspect(inference_source=[text], pred_sentiment=True)[0]
+    tokens = aspect_extraction_result["tokens"]
+    
+    # If no aspects found, aspect_positions and sentiment_list are empty
+    if len(aspect_extraction_result['position']) == 0:
+        aspect_positions = []
+        sentiment_list = []
+    else:
+        aspect_positions = [position[0]-1 for position in aspect_extraction_result['position']]
+        sentiment_list = aspect_extraction_result["sentiment"]
+
+    annotated_tokens = annotate_sentiment(tokens, aspect_positions, sentiment_list)
+    return annotated_tokens
+
+
+# Initializing the aspect extractor model
+checkpoint_map = available_checkpoints()
+aspect_extractor = ATEPCCheckpointManager.get_aspect_extractor(checkpoint='english',
+                                   auto_device=True  # False means load model on CPU
+                                   )
+
+
+
+demo = gr.Interface(
+    annotate_text_sentiment,
+    [
+        gr.Textbox(
+            label="Enter the text text",
+            info="aspect and sentiment detection",
+            lines=3,
+        ),
+    ],
+    gr.HighlightedText(
+        label="Aspect Detector based on DEBERT",
+        combine_adjacent=True,
+        show_legend=True,
+    ).style(color_map={"+": "green", "-": "red"}),
+    theme=gr.themes.Base()
+)
+
+
+demo.launch()

requirements.txt

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+pyabsa==1.16.27
+gradio
+locale