app.py

import gradio as gr
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer

# Download VADER lexicon for sentiment analysis
nltk.download('vader_lexicon', quiet=True)

def perform_sentiment_analysis(text):
    """Analyzes the sentiment of the given text using VADER."""
    sia = SentimentIntensityAnalyzer()
    return sia.polarity_scores(text)

def categorize_sentiment(compound_score):
    """Categorizes sentiment based on the compound score."""
    if compound_score > 0.1:  # Adjusted threshold for more balanced classification
        return 'Positive'
    elif compound_score < -0.1:
        return 'Negative'
    else:
        return 'Neutral'

def analyze_sentiment(input_text):
    """Performs sentiment analysis and categorizes the sentiment."""
    scores = perform_sentiment_analysis(input_text)
    sentiment = categorize_sentiment(scores['compound'])
    return {"Sentiment": sentiment, "Scores": scores}

# Improved examples for sentiment analysis
examples = [
    "Absolutely thrilled about my vacation next week! Can't wait!",  # Positive
    "The customer service was terrible. I wouldn't recommend this place to anyone.",  # Negative
    "I'm not sure what to think about the new policy. It has pros and cons.",  # Neutral
    "This product exceeded my expectations! The quality is fantastic.",  # Positive
    "I'm feeling overwhelmed with all these assignments due tomorrow.",  # Negative
    "Did you complete your homework for the AI course?",  # Neutral
]

# Create Gradio interface
demo = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(label="Enter text for sentiment analysis", placeholder="Type your text here..."),
    outputs="json",
    title="Sentiment Analysis Tool",
    description="Analyze the sentiment of any text. Enter your own text or choose an example below.",
    examples=examples
)

demo.launch()