File size: 2,002 Bytes
11f6c89 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | import os
import gradio as gr
import transformers
from pyabsa import AspectTermExtraction as ATEPC
import warnings
# 1. Compatibility setup for Hugging Face Spaces
warnings.filterwarnings("ignore")
transformers.PretrainedConfig.is_decoder = False
transformers.PretrainedConfig.output_attentions = False
transformers.PretrainedConfig.output_hidden_states = False
# 2. Path to the model
# When uploaded to HF, the 'model' folder should be in the root
CHECKPOINT_PATH = "model"
print(f"Loading model from: {CHECKPOINT_PATH}...")
# Load the model once at startup
model = ATEPC.AspectExtractor(checkpoint=CHECKPOINT_PATH)
def predict_absa(text):
if not text.strip():
return "Please enter some text to analyze."
# Run prediction
result = model.predict(text, print_result=False)
if not result['aspect']:
return "No aspects found in the input text."
# Format results for display
output = []
for aspect, sentiment in zip(result['aspect'], result['sentiment']):
output.append({
"Aspect": aspect,
"Sentiment": sentiment
})
return output
# 3. Create Gradio Interface
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()
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