| import os
|
| import transformers
|
| from pyabsa import AspectTermExtraction as ATEPC
|
| import warnings
|
|
|
| def main():
|
|
|
| warnings.filterwarnings("ignore")
|
| transformers.PretrainedConfig.is_decoder = False
|
| transformers.PretrainedConfig.output_attentions = False
|
| transformers.PretrainedConfig.output_hidden_states = False
|
|
|
|
|
| CHECKPOINT_PATH = "model"
|
|
|
| if not os.path.exists(CHECKPOINT_PATH):
|
| print(f"Error: Checkpoint not found at {CHECKPOINT_PATH}")
|
| return
|
|
|
| print(f"Loading model from: {CHECKPOINT_PATH}...")
|
|
|
|
|
|
|
| model = ATEPC.AspectExtractor(checkpoint=CHECKPOINT_PATH)
|
|
|
| print("\n" + "="*50)
|
| print("READY TO USE ATEPC MODEL")
|
| print("="*50)
|
| print("Type a sentence to extract aspects and sentiments.")
|
| print("Type 'exit' or 'quit' to stop.\n")
|
|
|
| while True:
|
| text = input("Input text: ").strip()
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|
|
| if text.lower() in ['exit', 'quit', '']:
|
| break
|
|
|
|
|
| result = model.predict(text, print_result=False)
|
|
|
| print(f"Results for: \"{text}\"")
|
| if not result['aspect']:
|
| print(" - No aspects found.")
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| else:
|
| for aspect, sentiment in zip(result['aspect'], result['sentiment']):
|
| print(f" -> [{aspect:^12}] | Sentiment: {sentiment}")
|
| print("-" * 30)
|
|
|
| if __name__ == "__main__":
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| main()
|
|
|
