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import gradio as gr
from transformers import pipeline
# Initialize the sentiment analysis pipeline globally
# This will download and cache the model on the first run.
# The default model is 'distilbert-base-uncased-finetuned-sst-2-english'
try:
sentiment_analyzer = pipeline("sentiment-analysis")
except Exception as e:
print(f"Error initializing sentiment analysis pipeline: {e}")
sentiment_analyzer = None
def sentiment_analysis(text: str) -> dict:
"""
Analyze the sentiment of the given text using a Hugging Face transformers model.
Args:
text (str): The text to analyze.
Returns:
dict: A dictionary containing the sentiment assessment.
"""
if not sentiment_analyzer:
return {
"assessment": "error",
"polarity": 0.0,
"details": "Sentiment analyzer not available."
}
# Handle empty or whitespace-only input
if not text or not text.strip():
return {
"assessment": "neutral", # Or specific "empty_input"
"polarity": 0.0,
"model_score": 0.0,
"details": "Input text is empty."
}
try:
# The pipeline returns a list of dictionaries, e.g., [{'label': 'POSITIVE', 'score': 0.99}]
# We take the first result as we are analyzing the whole text as one segment.
result = sentiment_analyzer(text)[0]
label = result['label']
score = result['score']
assessment = "neutral" # Default assessment
polarity = 0.0
if label == "POSITIVE":
assessment = "positive"
polarity = score # Score is confidence, directly maps to positive polarity
elif label == "NEGATIVE":
assessment = "negative"
# To align with a -1 to 1 range like TextBlob, make polarity negative for negative sentiment
polarity = -score
# Note: Subjectivity is not directly provided by this specific transformer model.
# We return polarity, assessment, and the raw model score for more detail.
return {
"polarity": round(polarity, 2),
"assessment": assessment,
"model_score": round(score, 4)
}
except Exception as e:
print(f"Error during sentiment analysis for text '{text[:50]}...': {e}")
return {
"assessment": "error",
"polarity": 0.0,
"details": f"Error processing text: {str(e)}"
}
# Create the Gradio interface
demo = gr.Interface(
fn=sentiment_analysis,
inputs=gr.Textbox(placeholder="Enter text to analyze..."),
outputs=gr.JSON(),
title="Advanced Text Sentiment Analysis (Transformers)",
description="Analyze the sentiment of text using a Hugging Face Transformers model. Provides polarity, assessment, and model score."
)
# Launch the interface and MCP server
if __name__ == "__main__":
demo.launch(mcp_server=True) |