Implement Phase 4: AI-Powered Feedback Analysis (User Story 2)
Implement automatic AI analysis of feedback submissions using Claude API, including language detection, categorization, summarization, and translation to product owner's preferred language. Tasks Completed (T065-T092): - T065-T069: Unit tests for AI analyzer (5 tests) - T070: Integration test for full AI analysis workflow - T071: Created AIAnalyzer abstract base class interface - T072: Added AnalysisResult dataclass to feedback model - T073: Implemented ClaudeAnalyzer with Anthropic SDK - T074: Integrated Claude API with 45s timeout - T075: Designed single-call analysis prompt - T076: Language detection implementation - T077: Category extraction with validation - T078: Summary generation (1-2 sentences) - T079: Translation extraction - T080: Timeout handling for Claude API - T081: API error handling with proper exceptions - T082: Analysis storage to analysis.md file - T083: Formatted markdown output for analysis - T084: Background analysis trigger on submission - T085: Non-blocking async analysis via threading - T086: Status update to 'analyzing' before analysis - T087: Status update to 'analyzed' on success - T088: Status update to 'analysis_failed' on error - T089: Metadata update with category and language - T090: Environment configuration for ANTHROPIC_API_KEY - T091: Verification that original content.txt preserved (FR-016) - T092: Verification that images not analyzed via OCR (FR-021) Features: - Abstract AIAnalyzer interface for multiple AI providers - ClaudeAnalyzer implementation using Anthropic API - Background threading for non-blocking analysis - Flask app context management in background threads - Comprehensive error handling and status tracking - Original content preservation (FR-016 compliance) - Image storage without OCR (FR-021 compliance) Testing: - 5 unit tests for AI analyzer components - 2 integration tests for full analysis workflow - All 46 tests passing (1 skipped) - Mock-based testing to avoid API calls Files Changed: - app/models/feedback.py: Added AnalysisResult dataclass - app/routes/submission.py: Background analysis integration - app/services/ai_analyzer.py: NEW - AI analysis service - app/services/feedback_storage.py: Analysis storage methods - tests/unit/test_ai_analyzer.py: NEW - Unit tests (5 tests) - tests/integration/test_ai_analysis_flow.py: NEW - Integration tests (2 tests) - tests/integration/test_feedback_submission_flow.py: Threading mock added 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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Claude
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"""Integration test for AI-powered feedback analysis flow"""
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import pytest
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import os
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import yaml
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from unittest.mock import Mock, patch
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@pytest.fixture
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def test_product_for_analysis(app):
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"""Create a test product for analysis testing"""
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with app.app_context():
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# Create test product directory and config
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product_dir = os.path.join(app.config['DATA_DIR'], 'products', 'analysis-test-product')
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os.makedirs(product_dir, exist_ok=True)
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# Create product config
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config_file = os.path.join(product_dir, 'config.yaml')
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config_data = {
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'product_id': 'analysis-test-product',
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'name': 'Analysis Test Product',
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'submission_url_slug': 'analysis-test',
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'owner_language': 'en',
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'assigned_owner_ids': ['usr_0001'],
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'status': 'active'
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}
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with open(config_file, 'w') as f:
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yaml.dump(config_data, f)
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yield 'analysis-test-product'
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@pytest.mark.integration
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def test_full_ai_analysis_flow(client, app, test_product_for_analysis):
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"""T070: Integration test for full AI analysis flow
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Test the complete AI analysis workflow:
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1. User submits feedback in German
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2. System saves feedback to filesystem
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3. Background analysis task is triggered
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4. AI analyzes feedback (category, summary, translation)
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5. Analysis.md is created with results
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6. Metadata is updated with status and language
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7. Original content.txt is preserved
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"""
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# Mock the Claude API response
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mock_api_response = Mock()
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mock_api_response.content = [Mock(text="""
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# Feedback Analysis
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**Category**: bug
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**Original Language**: de
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**Summary**: User reports that the login button is not working on mobile devices
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**Translation**: The login button on mobile devices does not respond when I click it. I tried multiple times but nothing happens.
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""")]
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# Patch the AI analyzer to use mock response
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with patch('app.services.ai_analyzer.anthropic.Anthropic'):
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with patch('app.services.ai_analyzer.ClaudeAnalyzer._call_claude_api', return_value=mock_api_response):
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with patch('os.getenv', return_value='test-api-key'):
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# Step 1-2: Submit feedback in German
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feedback_text = "Der Login-Button auf mobilen Geräten reagiert nicht, wenn ich darauf klicke. Ich habe es mehrmals versucht, aber es passiert nichts."
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data = {
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'feedback_text': feedback_text
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}
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# Also mock the background threading to run synchronously in tests
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with patch('app.routes.submission.threading.Thread') as mock_thread:
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# Make the thread run immediately in the test with proper args
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def run_sync():
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target = mock_thread.call_args[1]['target']
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args = mock_thread.call_args[1]['args']
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# Call with app context - first arg is app instance
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with args[0].app_context():
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target(*args)
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mock_thread.return_value.start.side_effect = run_sync
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response = client.post('/submit/analysis-test',
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data=data,
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follow_redirects=True)
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assert response.status_code == 200
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# Step 6-7: Verify feedback was saved and analyzed
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with app.app_context():
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data_dir = app.config['DATA_DIR']
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products_dir = os.path.join(data_dir, 'products', 'analysis-test-product', 'feedback')
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# Find the created feedback directory
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feedback_dirs = [d for d in os.listdir(products_dir)
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if os.path.isdir(os.path.join(products_dir, d))]
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assert len(feedback_dirs) > 0, "No feedback directory was created"
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feedback_dir = os.path.join(products_dir, feedback_dirs[0])
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# Verify original content.txt is preserved (FR-016)
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content_file = os.path.join(feedback_dir, 'content.txt')
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assert os.path.exists(content_file)
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with open(content_file, 'r', encoding='utf-8') as f:
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saved_content = f.read()
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assert feedback_text in saved_content, "Original content not preserved"
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# Verify analysis.md was created
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analysis_file = os.path.join(feedback_dir, 'analysis.md')
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assert os.path.exists(analysis_file), "Analysis file not created"
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with open(analysis_file, 'r', encoding='utf-8') as f:
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analysis_content = f.read()
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# Verify analysis contains expected sections
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assert '# Feedback Analysis' in analysis_content
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assert 'Category' in analysis_content
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assert 'bug' in analysis_content
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assert 'Original Language' in analysis_content
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assert 'de' in analysis_content
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assert 'Summary' in analysis_content
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assert 'Translation' in analysis_content
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# Verify metadata was updated
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metadata_file = os.path.join(feedback_dir, 'metadata.yaml')
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assert os.path.exists(metadata_file)
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with open(metadata_file, 'r') as f:
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metadata = yaml.safe_load(f)
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# Status should be 'analyzed' after successful analysis
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assert metadata['status'] in ['analyzed', 'analyzing']
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# Original language should be detected and stored
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assert metadata.get('original_language') == 'de'
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# Category should be stored
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assert metadata.get('category') == 'bug'
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@pytest.mark.integration
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def test_analysis_preserves_images(client, app, test_product_for_analysis):
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"""Integration test: Verify images are stored but not analyzed via OCR (FR-021)
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Per FR-021, images should be stored as attachments but not processed for OCR.
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Only text content should be analyzed.
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"""
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import io
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feedback_text = "Screenshot of the error"
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data = {
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'feedback_text': feedback_text,
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'files': [
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(io.BytesIO(b'PNG fake image data'), 'screenshot.png')
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]
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}
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# Mock AI to ensure it only receives text, not image data
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mock_api_response = Mock()
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mock_api_response.content = [Mock(text="""
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# Feedback Analysis
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**Category**: bug
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**Original Language**: en
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**Summary**: User provided screenshot of error
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**Translation**: (same as original)
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""")]
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with patch('app.services.ai_analyzer.anthropic.Anthropic'):
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with patch('app.services.ai_analyzer.ClaudeAnalyzer._call_claude_api', return_value=mock_api_response) as mock_call:
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with patch('os.getenv', return_value='test-api-key'):
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with patch('app.routes.submission.threading.Thread') as mock_thread:
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# Make the thread run immediately in the test with proper args
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def run_sync():
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target = mock_thread.call_args[1]['target']
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args = mock_thread.call_args[1]['args']
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# Call with app context - first arg is app instance
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with args[0].app_context():
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target(*args)
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mock_thread.return_value.start.side_effect = run_sync
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response = client.post('/submit/analysis-test',
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data=data,
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content_type='multipart/form-data',
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follow_redirects=True)
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assert response.status_code == 200
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# Verify AI was called with text only, not image data
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if mock_call.called:
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call_args = str(mock_call.call_args)
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# Should contain text feedback
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assert 'Screenshot of the error' in call_args or 'screenshot' in call_args.lower()
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# Should NOT contain image binary data
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assert b'PNG' not in call_args.encode() if isinstance(call_args, str) else b'PNG' not in call_args
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# Verify image was stored as attachment
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with app.app_context():
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data_dir = app.config['DATA_DIR']
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products_dir = os.path.join(data_dir, 'products', 'analysis-test-product', 'feedback')
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feedback_dirs = [d for d in os.listdir(products_dir)
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if os.path.isdir(os.path.join(products_dir, d))]
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feedback_dir = os.path.join(products_dir, feedback_dirs[0])
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attachments_dir = os.path.join(feedback_dir, 'attachments')
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assert os.path.exists(attachments_dir)
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assert 'screenshot.png' in os.listdir(attachments_dir)
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