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| import random | |
| import gradio as gr | |
| import pandas as pd | |
| # Import necessary components from PyABSA for dataset listing | |
| from pyabsa import AspectTermExtraction as ATEPC | |
| # Import the initialized ATEPC model and utility functions from our local files | |
| from models import aspect_extractor | |
| from utils import load_atepc_examples | |
| # Defining the specific datasets | |
| DESIRED_ATEPC_DATASETS = [ | |
| "Laptop14", | |
| "Restaurant14", | |
| "SemEval", | |
| "Twitter", | |
| "TShirt" | |
| ] | |
| # Pre load Dataset Examples | |
| # This dictionary will store example sentences for each desired dataset, | |
| # which are used when the user leaves the input text box blank. | |
| print("Loading ATEPC dataset examples for Gradio interface...") | |
| atepc_dataset_examples = {} | |
| # Iterate through available ATEPC datasets and load examples for each | |
| # Filter to only include the desired datasets | |
| for dataset_name in DESIRED_ATEPC_DATASETS: | |
| try: | |
| # Check if the dataset name is valid in ATEPC.ATEPCDatasetList | |
| if hasattr(ATEPC.ATEPCDatasetList(), dataset_name): | |
| atepc_dataset_examples[dataset_name] = load_atepc_examples(dataset_name) | |
| else: | |
| print(f"Warning: Dataset '{dataset_name}' not found in ATEPC.ATEPCDatasetList. Skipping.") | |
| except Exception as e: | |
| print(f"Error loading examples for ATEPC dataset '{dataset_name}': {e}") | |
| print("ATEPC dataset examples loading complete.") | |
| # Inference Function for Gradio | |
| def run_atepc_inference(input_text: str, selected_dataset: str) -> tuple[pd.DataFrame, str]: | |
| # Check if the aspect_extractor model was successfully initialized | |
| if aspect_extractor is None: | |
| return pd.DataFrame({"Error": ["Model not initialized. Please check logs."]}, | |
| columns=["Error"]), "Model Unavailable" | |
| analyzed_text = input_text.strip() # Remove leading/trailing whitespace | |
| # If no text is provided, select a random example from the pre-loaded data | |
| if not analyzed_text: | |
| examples = atepc_dataset_examples.get(selected_dataset) | |
| if examples: | |
| analyzed_text = random.choice(examples) | |
| else: | |
| return pd.DataFrame({"Message": ["No examples available for this dataset or input text provided."]}, | |
| columns=["Message"]), "Please provide text or select a valid dataset." | |
| print(f"Performing ATEPC Inference on: '{analyzed_text}' (Dataset: {selected_dataset})") | |
| try: | |
| # Predict aspects and their sentiments | |
| prediction_result = aspect_extractor.predict(analyzed_text, pred_sentiment=True) | |
| # Check if any aspects were detected | |
| if not prediction_result or not prediction_result.get("aspect"): | |
| return pd.DataFrame({"Message": ["No aspects detected for the given text."]}, | |
| columns=["Message"]), analyzed_text | |
| # Create a DataFrame from the prediction results | |
| df_result = pd.DataFrame( | |
| { | |
| "Aspect": prediction_result["aspect"], | |
| "Sentiment": prediction_result["sentiment"], | |
| "Confidence": [round(c, 4) for c in prediction_result["confidence"]], | |
| "Position": prediction_result["position"], | |
| } | |
| ) | |
| return df_result, analyzed_text | |
| except Exception as e: | |
| print(f"Error during ATEPC inference: {e}") | |
| return pd.DataFrame({"Error": [f"An error occurred: {e}"]}, | |
| columns=["Error"]), analyzed_text | |
| # Gradio User Interface Definition | |
| # Initialize the Gradio Blocks interface | |
| with gr.Blocks(title="PyABSA Demonstration: Aspect-based Sentiment Analysis") as sentiment_analysis_app: | |
| # Main title for the entire application | |
| gr.Markdown("# <p align='center'>PyABSA: Aspect-based Sentiment Analysis</p>") | |
| gr.Markdown("---") # Visual separator | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("## 📈 Analyze Aspects and Sentiments") | |
| gr.Markdown( | |
| "This tool identifies specific aspects (entities or attributes) in a sentence " | |
| "and determines the sentiment (positive, negative, neutral) associated with each. " | |
| "For example, in 'The laptop's battery life is excellent', 'battery life' would be " | |
| "identified with a 'positive' sentiment." | |
| ) | |
| # Input area for ATEPC | |
| atepc_input_box = gr.Textbox( | |
| placeholder="Type a sentence here, or leave blank to load a random example from a dataset...", | |
| label="Input Sentence:", | |
| lines=3 | |
| ) | |
| # Dataset selection for ATEPC examples restricted to desired list | |
| atepc_dataset_selection = gr.Radio( | |
| choices=DESIRED_ATEPC_DATASETS, # Use the predefined list | |
| value=DESIRED_ATEPC_DATASETS[0] if DESIRED_ATEPC_DATASETS else None, # Set default to first or None | |
| label="Select Dataset (for random examples):", | |
| interactive=True | |
| ) | |
| # Button to trigger ATEPC inference | |
| atepc_run_button = gr.Button("Analyze Aspects!", variant="primary") | |
| # Output areas for ATEPC | |
| atepc_output_sentence = gr.TextArea(label="Analyzed Sentence:", interactive=False) | |
| atepc_prediction_results_df = gr.DataFrame(label="Aspect Prediction Results:", interactive=False) | |
| # Define the interaction for the ATEPC button click | |
| atepc_run_button.click( | |
| fn=run_atepc_inference, | |
| inputs=[atepc_input_box, atepc_dataset_selection], | |
| outputs=[atepc_prediction_results_df, atepc_output_sentence], | |
| api_name="run_atepc_inference" | |
| ) | |
| gr.Markdown("---") # Visual separator | |
| # Launch the Gradio application | |
| if __name__ == "__main__": | |
| if aspect_extractor is None: | |
| print("Warning: PyABSA ATEPC model failed to initialize. The application may not function correctly.") | |
| sentiment_analysis_app.launch(share=False, debug=True) | |