| import os
|
| import gradio as gr
|
| import transformers
|
| from pyabsa import AspectTermExtraction as ATEPC
|
| import warnings
|
|
|
|
|
| warnings.filterwarnings("ignore")
|
| transformers.PretrainedConfig.is_decoder = False
|
| transformers.PretrainedConfig.output_attentions = False
|
| transformers.PretrainedConfig.output_hidden_states = False
|
|
|
|
|
|
|
| CHECKPOINT_PATH = "model"
|
|
|
| print(f"Loading model from: {CHECKPOINT_PATH}...")
|
|
|
| model = ATEPC.AspectExtractor(checkpoint=CHECKPOINT_PATH)
|
|
|
| def predict_absa(text):
|
| if not text.strip():
|
| return "Please enter some text to analyze."
|
|
|
|
|
| result = model.predict(text, print_result=False)
|
|
|
| if not result['aspect']:
|
| return "No aspects found in the input text."
|
|
|
|
|
| output = []
|
| for aspect, sentiment in zip(result['aspect'], result['sentiment']):
|
| output.append({
|
| "Aspect": aspect,
|
| "Sentiment": sentiment
|
| })
|
|
|
| return output
|
|
|
|
|
| demo = gr.Interface(
|
| fn=predict_absa,
|
| inputs=gr.Textbox(
|
| lines=3,
|
| placeholder="Enter a sentence here (e.g., 'The coffee was great but the price was too high.')",
|
| label="Input Text"
|
| ),
|
| outputs=gr.JSON(label="ABSA Results"),
|
| title="DeBERTa-v3 Aspect Based Sentiment Analysis",
|
| description="This demo uses a fine-tuned DeBERTa-v3 model to extract aspects and classify their sentiment polarities.",
|
| examples=[
|
| ["The food was delicious but the service was extremely slow."],
|
| ["The battery life of this laptop is amazing, though the screen is a bit dim."],
|
| ["I love the interface, but the mobile app crashes frequently."]
|
| ],
|
| cache_examples=False
|
| )
|
|
|
| if __name__ == "__main__":
|
| demo.launch()
|
|
|
