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Rename emojify_text.py to ner_tool.py
Browse files- emojify_text.py +0 -33
- ner_tool.py +23 -0
emojify_text.py
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import emoji
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from transformers import Tool
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class EmojifyTextTool(Tool):
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name = "emojify_text"
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description = "Emojifies text by adding relevant emojis to enhance expressiveness."
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inputs = ["text"]
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outputs = ["text"] # Explicitly specify the output component
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def __call__(self, text: str):
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# Define a dictionary mapping keywords to emojis
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keyword_to_emoji = {
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"happy": "๐",
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"sad": "๐ข",
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"love": "โค๏ธ",
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"confused": "๐",
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"excited": "๐",
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# Add more keywords and corresponding emojis as needed
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}
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# Emojify the input text based on keywords
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emojified_text = self._emojify_keywords(text, keyword_to_emoji)
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# Print the emojified text
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print(f"Emojified Text: {emojified_text}")
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return {"emojified_text": emojified_text} # Return a dictionary with the specified output component
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def _emojify_keywords(self, text, keyword_to_emoji):
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# Replace keywords in the text with corresponding emojis
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for keyword, emoji_char in keyword_to_emoji.items():
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text = text.replace(keyword, emoji_char)
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return text
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ner_tool.py
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from transformers import pipeline
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from transformers import Tool
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class NamedEntityRecognitionTool(Tool):
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name = "ner_tool"
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description = "Identifies and labels entities such as persons, organizations, and locations in a given text."
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inputs = ["text"]
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outputs = ["entities"]
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def __call__(self, text: str):
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# Initialize the named entity recognition pipeline
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ner_analyzer = pipeline("ner")
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# Perform named entity recognition on the input text
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entities = ner_analyzer(text)
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# Print the identified entities
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print(f"Identified Entities: {entities}")
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# Extract entity labels and return as a list
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entity_labels = [entity["label"] for entity in entities]
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return entity_labels
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