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| """ | |
| UI components for RoBERTa sentiment analysis screen | |
| """ | |
| import gradio as gr | |
| from processors.roberta_analysis import compare_sentiment_roberta | |
| def create_roberta_screen(): | |
| """ | |
| Create the RoBERTa sentiment analysis screen components | |
| Returns: | |
| tuple: (run_roberta_btn, roberta_output, sentence_level, visualization_style, visualization_container, roberta_status) | |
| """ | |
| with gr.Column() as roberta_screen: | |
| gr.Markdown("## RoBERTa Sentiment Analysis") | |
| gr.Markdown(""" | |
| This tab uses the RoBERTa transformer model to perform sentiment analysis on the LLM responses | |
| and compare their emotional tones. RoBERTa was trained on a diverse dataset and can detect subtle | |
| differences in sentiment that simpler rule-based classifiers might miss. | |
| Click 'Run Sentiment Analysis' to analyze your dataset. | |
| """) | |
| with gr.Row(): | |
| run_roberta_btn = gr.Button("Run Sentiment Analysis", variant="primary", size="large") | |
| # Status message area | |
| roberta_status = gr.Markdown(visible=False) | |
| # Hidden output to store raw analysis results | |
| roberta_output = gr.JSON(label="Sentiment Analysis Results", visible=False) | |
| # Placeholder for visualization container - this won't be directly updated | |
| # but is included for backward compatibility | |
| visualization_container = gr.Markdown(visible=False) | |
| return run_roberta_btn, roberta_output, visualization_container, roberta_status | |
| def process_roberta_request(dataset): | |
| """ | |
| Process the RoBERTa sentiment analysis request | |
| Args: | |
| dataset (dict): The input dataset | |
| sentence_level (bool): Whether to perform sentence-level analysis | |
| visualization_style (str): Visualization style preference | |
| Returns: | |
| dict: Analysis results | |
| """ | |
| if not dataset or "entries" not in dataset or not dataset["entries"]: | |
| return {"error": "No dataset loaded. Please create or load a dataset first."} | |
| # Initialize the results structure | |
| results = {"analyses": {}} | |
| # Get the prompt text from the first entry | |
| prompt_text = dataset["entries"][0].get("prompt", "") | |
| if not prompt_text: | |
| return {"error": "No prompt found in dataset"} | |
| # Initialize the analysis container for this prompt | |
| results["analyses"][prompt_text] = {} | |
| # Get model names and responses | |
| model1_name = dataset["entries"][0].get("model", "Model 1") | |
| model2_name = dataset["entries"][1].get("model", "Model 2") | |
| model1_response = dataset["entries"][0].get("response", "") | |
| model2_response = dataset["entries"][1].get("response", "") | |
| try: | |
| # Perform RoBERTa sentiment analysis | |
| print("Starting RoBERTa sentiment analysis...") | |
| sentiment_results = compare_sentiment_roberta( | |
| texts=[model1_response, model2_response], | |
| model_names=[model1_name, model2_name] | |
| ) | |
| # Store the results | |
| results["analyses"][prompt_text]["roberta_sentiment"] = sentiment_results | |
| # Add metadata about the analysis | |
| results["analyses"][prompt_text]["roberta_sentiment"]["metadata"] = { | |
| "sentence_level": "", | |
| "visualization_style": "" | |
| } | |
| return results | |
| except Exception as e: | |
| import traceback | |
| error_msg = f"Error in RoBERTa sentiment analysis: {str(e)}\n{traceback.format_exc()}" | |
| print(error_msg) | |
| # Return error information | |
| return { | |
| "error": str(e), | |
| "traceback": traceback.format_exc(), | |
| "analyses": { | |
| prompt_text: { | |
| "roberta_sentiment": { | |
| "error": str(e), | |
| "models": [model1_name, model2_name], | |
| "message": "RoBERTa sentiment analysis failed. Try again or use a different analysis method." | |
| } | |
| } | |
| } | |
| } |