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| """Quick tests for the analysis tools.""" | |
| import sys | |
| import os | |
| # Add src to path | |
| sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src')) | |
| from tools import WordCounter, KeywordExtractor, SentimentAnalyzer | |
| def test_word_counter(): | |
| """Test word counter tool.""" | |
| print("Testing Word Counter...") | |
| tool = WordCounter() | |
| test_text = "This is a test. This is only a test. Testing is important." | |
| result = tool.run(test_text) | |
| print(f" Total words: {result['total_words']}") | |
| print(f" Unique words: {result['unique_words']}") | |
| print(f" Top words: {result['top_10_words'][:3]}") | |
| print(" ✓ Word Counter works!\n") | |
| def test_keyword_extractor(): | |
| """Test keyword extractor tool.""" | |
| print("Testing Keyword Extractor...") | |
| tool = KeywordExtractor() | |
| test_text = """ | |
| Machine learning is a subset of artificial intelligence. | |
| Deep learning algorithms use neural networks to process data. | |
| Natural language processing helps computers understand human language. | |
| """ | |
| result = tool.run(test_text) | |
| print(f" Keywords found: {result['num_keywords']}") | |
| print(f" Top 3 keywords:") | |
| for kw in result['keywords'][:3]: | |
| print(f" - {kw['word']}: {kw['score']}") | |
| print(" ✓ Keyword Extractor works!\n") | |
| def test_sentiment_analyzer(): | |
| """Test sentiment analyzer tool.""" | |
| print("Testing Sentiment Analyzer...") | |
| tool = SentimentAnalyzer() | |
| # Positive text | |
| positive_text = "This is wonderful! I love it. Great experience, highly recommended!" | |
| result = tool.run(positive_text) | |
| print(f" Positive text sentiment: {result['sentiment_label']} ({result['sentiment_score']})") | |
| # Negative text | |
| negative_text = "This is terrible. I hate it. Awful experience, very disappointed." | |
| result = tool.run(negative_text) | |
| print(f" Negative text sentiment: {result['sentiment_label']} ({result['sentiment_score']})") | |
| # Neutral text | |
| neutral_text = "The product arrived on Tuesday. It has a blue color." | |
| result = tool.run(neutral_text) | |
| print(f" Neutral text sentiment: {result['sentiment_label']} ({result['sentiment_score']})") | |
| print(" ✓ Sentiment Analyzer works!\n") | |
| if __name__ == "__main__": | |
| print("=" * 60) | |
| print("ReAct Text Analyzer - Tool Tests") | |
| print("=" * 60 + "\n") | |
| try: | |
| test_word_counter() | |
| test_keyword_extractor() | |
| test_sentiment_analyzer() | |
| print("=" * 60) | |
| print("✅ All tests passed!") | |
| print("=" * 60) | |
| print("\nYou can now run the Streamlit app:") | |
| print(" cd src && streamlit run app.py") | |
| print("\nOr use the quick start script:") | |
| print(" ./run.sh") | |
| except Exception as e: | |
| print(f"\n❌ Test failed: {e}") | |
| import traceback | |
| traceback.print_exc() | |