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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."
}
}
}
} |