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#!/usr/bin/env python3
"""
CLI Demo for AI News Sentiment Analyzer
Demonstrates the functionality via command line interface
"""
import argparse
import sys
from datetime import datetime
from api_handler import AINewsAnalyzer
def print_header():
"""Print a nice header for the CLI"""
print("🤖 AI News Sentiment Analyzer")
print("=" * 50)
print()
def print_sentiment_emoji(sentiment):
"""Return emoji based on sentiment"""
emoji_map = {
'positive': '😊',
'negative': '😞',
'neutral': '😐'
}
return emoji_map.get(sentiment, '🤷')
def display_articles(df, max_articles=10):
"""Display articles in a formatted way"""
if df.empty:
print("❌ No articles found.")
return
print(f"📰 Found {len(df)} articles")
print("\nSentiment Distribution:")
sentiment_counts = df['sentiment_label'].value_counts()
for sentiment, count in sentiment_counts.items():
emoji = print_sentiment_emoji(sentiment)
percentage = (count / len(df)) * 100
print(f" {emoji} {sentiment.title()}: {count} articles ({percentage:.1f}%)")
print(f"\n📄 Top {min(max_articles, len(df))} Articles:")
print("-" * 80)
for idx, (_, article) in enumerate(df.head(max_articles).iterrows(), 1):
sentiment_emoji = print_sentiment_emoji(article['sentiment_label'])
score = article['sentiment_polarity']
published = article['published_at'].strftime('%Y-%m-%d %H:%M')
print(f"{idx:2}. {sentiment_emoji} [{article['source']}] {published}")
print(f" {article['title']}")
print(f" Sentiment: {article['sentiment_label'].title()} (Score: {score:.2f})")
if article['description'] and len(article['description']) > 100:
description = article['description'][:100] + "..."
else:
description = article['description'] or "No description available"
print(f" 📝 {description}")
print(f" 🔗 {article['url']}")
print()
def display_sentiment_analysis(df):
"""Display detailed sentiment analysis"""
if df.empty:
return
print("\n📊 Sentiment Analysis Summary:")
print("-" * 40)
# Overall statistics
avg_polarity = df['sentiment_polarity'].mean()
avg_subjectivity = df['sentiment_subjectivity'].mean()
print(f"Average Polarity: {avg_polarity:.3f} (Range: -1.0 to +1.0)")
print(f"Average Subjectivity: {avg_subjectivity:.3f} (Range: 0.0 to 1.0)")
if avg_polarity > 0.1:
overall_mood = "📈 Generally Positive"
elif avg_polarity < -0.1:
overall_mood = "📉 Generally Negative"
else:
overall_mood = "➡️ Generally Neutral"
print(f"Overall Mood: {overall_mood}")
# Most positive and negative articles
if len(df[df['sentiment_label'] == 'positive']) > 0:
most_positive = df.loc[df['sentiment_polarity'].idxmax()]
print(f"\n😊 Most Positive: \"{most_positive['title']}\" ({most_positive['sentiment_polarity']:.2f})")
if len(df[df['sentiment_label'] == 'negative']) > 0:
most_negative = df.loc[df['sentiment_polarity'].idxmin()]
print(f"😞 Most Negative: \"{most_negative['title']}\" ({most_negative['sentiment_polarity']:.2f})")
def display_sources(df):
"""Display source breakdown"""
if df.empty:
return
print("\n📺 News Sources:")
print("-" * 30)
source_counts = df['source'].value_counts()
for source, count in source_counts.head(10).items():
print(f" 📰 {source}: {count} articles")
def main():
parser = argparse.ArgumentParser(description='AI News Sentiment Analyzer CLI Demo')
parser.add_argument('--query', '-q',
default='artificial intelligence',
help='Search query for news articles (default: "artificial intelligence")')
parser.add_argument('--days', '-d',
type=int,
default=7,
help='Number of days to look back (default: 7)')
parser.add_argument('--sources', '-s',
help='Comma-separated list of news sources (e.g., "techcrunch,wired")')
parser.add_argument('--max-articles', '-m',
type=int,
default=10,
help='Maximum number of articles to display (default: 10)')
parser.add_argument('--sentiment-only',
action='store_true',
help='Show only sentiment analysis summary')
parser.add_argument('--positive-only',
action='store_true',
help='Show only positive articles')
parser.add_argument('--negative-only',
action='store_true',
help='Show only negative articles')
args = parser.parse_args()
print_header()
try:
# Initialize analyzer
analyzer = AINewsAnalyzer()
print(f"🔍 Searching for: \"{args.query}\"")
print(f"📅 Looking back: {args.days} days")
if args.sources:
print(f"📰 Sources: {args.sources}")
print()
# Fetch and analyze news
df = analyzer.get_ai_news_with_sentiment(
query=args.query,
days=args.days,
sources=args.sources
)
if df.empty:
print("❌ No articles found. Try adjusting your search parameters.")
return
# Filter by sentiment if requested
if args.positive_only:
df = df[df['sentiment_label'] == 'positive']
print("🔽 Filtered to show only POSITIVE articles")
elif args.negative_only:
df = df[df['sentiment_label'] == 'negative']
print("🔽 Filtered to show only NEGATIVE articles")
# Display results based on options
if args.sentiment_only:
display_sentiment_analysis(df)
else:
display_articles(df, args.max_articles)
display_sentiment_analysis(df)
display_sources(df)
print(f"\n✅ Analysis complete! Processed {len(df)} articles.")
except KeyboardInterrupt:
print("\n👋 Analysis interrupted by user.")
sys.exit(0)
except Exception as e:
print(f"❌ Error occurred: {e}")
print("Please check your API key and internet connection.")
sys.exit(1)
if __name__ == "__main__":
main() |