Create test_real_functionality.py

tests/test_real_functionality.py

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+# tests/test_real_functionality.py
+import pytest
+import sys
+import os
+from PIL import Image
+import numpy as np
+
+# Add parent directory to import app
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# Import actual functions from app.py
+try:
+    from app import (
+        preprocess_image_for_ocr,
+        clean_extracted_text,
+        gradio_generate_soap
+    )
+    APP_AVAILABLE = True
+except ImportError:
+    APP_AVAILABLE = False
+
+@pytest.mark.skipif(not APP_AVAILABLE, reason="app.py not available")
+class TestRealFunctionality:
+    """Test actual app functions (not mocks)"""
+    
+    def test_clean_extracted_text_real(self):
+        """Test the actual clean_extracted_text function"""
+        dirty_text = "  Line 1  \n\n  Line 2 with | pipes  \n   \n  Line_3  "
+        clean_text = clean_extracted_text(dirty_text)
+        
+        assert "Line 1" in clean_text
+        assert "Line 2 with  pipes" in clean_text
+        assert "Line 3" in clean_text
+        assert "|" not in clean_text
+
+    def test_preprocess_image_for_ocr_real(self):
+        """Test actual image preprocessing"""
+        # Create test image
+        test_image = Image.new('RGB', (400, 300), color='white')
+        
+        # Test the real function
+        result = preprocess_image_for_ocr(test_image)
+        
+        assert isinstance(result, np.ndarray)
+        assert len(result.shape) == 2  # Should be grayscale
+        assert result.shape[0] >= 300  # Height should be at least 300
+        assert result.shape[1] >= 300  # Width should be at least 300
+
+    def test_gradio_generate_soap_empty_input_real(self):
+        """Test actual Gradio function with empty input"""
+        result = gradio_generate_soap("", None)
+        assert "❌" in result or "Please enter" in result
+
+    def test_gradio_generate_soap_text_input_real(self):
+        """Test actual Gradio function with text input"""
+        medical_text = """
+        Patient: John Smith, 45-year-old male
+        Chief Complaint: Chest pain for 2 hours
+        History: Sharp chest pain, 7/10 intensity
+        Physical Exam: VS: BP 150/90, HR 110
+        Assessment: Chest pain evaluation needed
+        Plan: EKG, cardiac enzymes
+        """
+        
+        # This will fail if generate_soap_note function isn't loaded
+        # but that's good - it tells us the real state of the app
+        result = gradio_generate_soap(medical_text, None)
+        
+        # Check if it's an error message or actual SOAP content
+        if "❌" not in result:
+            # Should contain SOAP sections if successful
+            assert any(section in result.upper() for section in 
+                      ["SUBJECTIVE", "OBJECTIVE", "ASSESSMENT", "PLAN"])
+
+# tests/test_integration_real.py
+import pytest
+import sys
+import os
+from PIL import Image, ImageDraw, ImageFont
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+class TestRealIntegration:
+    """Test real integration without mocks"""
+    
+    def test_image_with_text_creation(self):
+        """Test creating an image with text for OCR testing"""
+        # Create a real image with text
+        img = Image.new('RGB', (800, 600), color='white')
+        draw = ImageDraw.Draw(img)
+        
+        # Add medical text to image
+        medical_text = [
+            "Patient: John Doe",
+            "Age: 45 years old",
+            "Chief Complaint: Chest pain",
+            "Vital Signs: BP 140/90",
+            "Assessment: Further evaluation needed"
+        ]
+        
+        y_position = 50
+        for line in medical_text:
+            draw.text((50, y_position), line, fill='black')
+            y_position += 40
+        
+        # Verify image was created
+        assert img.size == (800, 600)
+        assert img.mode == 'RGB'
+        
+        # Convert to array to verify content
+        img_array = np.array(img)
+        
+        # Should not be all white (has text)
+        assert not np.all(img_array == 255)
+
+    def test_examples_dictionary_structure(self):
+        """Test that examples dictionary has proper structure"""
+        # Test the actual examples from the app
+        try:
+            from app import examples
+            
+            # Check required keys exist
+            required_keys = ['chest_pain', 'diabetes', 'pediatric']
+            for key in required_keys:
+                assert key in examples
+                
+            # Check content quality
+            for key, content in examples.items():
+                assert len(content) > 100  # Substantial content
+                assert "Patient:" in content
+                assert any(word in content.lower() for word in 
+                          ["complaint", "history", "exam", "assessment", "plan"])
+                
+        except ImportError:
+            pytest.skip("examples not available in app.py")
+
+    def test_file_extensions_validation(self):
+        """Test file extension validation logic"""
+        allowed_extensions = ['.png', '.jpg', '.jpeg', '.txt', '.doc', '.docx', '.pdf']
+        
+        # Test valid extensions
+        valid_files = ['image.png', 'document.pdf', 'notes.txt', 'scan.jpg']
+        for filename in valid_files:
+            file_ext = os.path.splitext(filename.lower())[1]
+            assert file_ext in allowed_extensions
+        
+        # Test invalid extensions
+        invalid_files = ['file.xyz', 'document.zip', 'image.bmp']
+        for filename in invalid_files:
+            file_ext = os.path.splitext(filename.lower())[1]
+            assert file_ext not in allowed_extensions
+
+# tests/test_error_scenarios_real.py
+import pytest
+import sys
+import os
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+class TestRealErrorScenarios:
+    """Test real error handling scenarios"""
+    
+    def test_missing_dependencies_handling(self):
+        """Test what happens when dependencies are missing"""
+        # Test if app handles missing dependencies gracefully
+        try:
+            import easyocr
+            EASYOCR_AVAILABLE = True
+        except ImportError:
+            EASYOCR_AVAILABLE = False
+        
+        try:
+            import pytesseract
+            TESSERACT_AVAILABLE = True
+        except ImportError:
+            TESSERACT_AVAILABLE = False
+        
+        # At least one OCR method should be available for the app to work
+        if not EASYOCR_AVAILABLE and not TESSERACT_AVAILABLE:
+            pytest.skip("No OCR dependencies available")
+
+    def test_torch_availability(self):
+        """Test PyTorch availability and device detection"""
+        import torch
+        
+        # Test device detection logic
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        assert device in ["cuda", "cpu"]
+        
+        # Test tensor creation on detected device
+        test_tensor = torch.tensor([1, 2, 3], dtype=torch.float32)
+        assert test_tensor is not None
+
+    def test_image_format_handling(self):
+        """Test handling of different image formats"""
+        from PIL import Image
+        
+        # Test RGB image
+        rgb_img = Image.new('RGB', (100, 100), color='white')
+        assert rgb_img.mode == 'RGB'
+        
+        # Test grayscale image
+        gray_img = Image.new('L', (100, 100), color=128)
+        assert gray_img.mode == 'L'
+        
+        # Test RGBA image
+        rgba_img = Image.new('RGBA', (100, 100), color=(255, 255, 255, 255))
+        assert rgba_img.mode == 'RGBA'
+
+# tests/test_performance_real.py
+import pytest
+import time
+import sys
+import os
+from PIL import Image
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+class TestRealPerformance:
+    """Test actual performance characteristics"""
+    
+    def test_image_preprocessing_speed(self):
+        """Test image preprocessing performance"""
+        try:
+            from app import preprocess_image_for_ocr
+            
+            # Create test image
+            test_image = Image.new('RGB', (1000, 800), color='white')
+            
+            # Time the preprocessing
+            start_time = time.time()
+            result = preprocess_image_for_ocr(test_image)
+            end_time = time.time()
+            
+            processing_time = end_time - start_time
+            
+            # Should process reasonably quickly (under 5 seconds)
+            assert processing_time < 5.0
+            assert result is not None
+            
+        except ImportError:
+            pytest.skip("preprocess_image_for_ocr not available")
+
+    def test_text_cleaning_speed(self):
+        """Test text cleaning performance"""
+        try:
+            from app import clean_extracted_text
+            
+            # Create large text with lots of cleaning needed
+            dirty_text = "  \n  ".join([f"Line {i} with | pipes and _ underscores  " 
+                                       for i in range(1000)])
+            
+            start_time = time.time()
+            clean_text = clean_extracted_text(dirty_text)
+            end_time = time.time()
+            
+            processing_time = end_time - start_time
+            
+            # Should clean text quickly (under 1 second)
+            assert processing_time < 1.0
+            assert len(clean_text) > 0
+            assert "|" not in clean_text
+            
+        except ImportError:
+            pytest.skip("clean_extracted_text not available")
+
+# tests/test_configuration_real.py
+import pytest
+import sys
+import os
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+class TestRealConfiguration:
+    """Test actual app configuration"""
+    
+    def test_environment_variables(self):
+        """Test environment variable handling"""
+        # Test HF_TOKEN detection
+        hf_token = os.environ.get('HF_TOKEN') or os.environ.get('HUGGINGFACE_TOKEN')
+        
+        # If no token, app should handle gracefully
+        if not hf_token:
+            # This is fine for testing - app should handle missing tokens
+            pass
+        else:
+            # If token exists, it should be a string
+            assert isinstance(hf_token, str)
+            assert len(hf_token) > 10  # Tokens should be substantial
+
+    def test_cuda_device_configuration(self):
+        """Test CUDA device configuration"""
+        import torch
+        
+        # Test device selection logic
+        cuda_visible = os.environ.get("CUDA_VISIBLE_DEVICES", "")
+        
+        if cuda_visible == "":
+            # Should fall back to CPU
+            expected_device = "cpu"
+        elif torch.cuda.is_available():
+            expected_device = "cuda"
+        else:
+            expected_device = "cpu"
+        
+        # Verify device is valid
+        assert expected_device in ["cuda", "cpu"]
+
+    def test_model_configuration(self):
+        """Test model configuration parameters"""
+        # Test model ID
+        model_id = "google/gemma-3n-e2b-it"
+        assert isinstance(model_id, str)
+        assert "/" in model_id  # Should be in format "org/model"
+        
+        # Test model parameters that would be used
+        max_new_tokens = 512
+        temperature = 0.3
+        
+        assert isinstance(max_new_tokens, int)
+        assert max_new_tokens > 0
+        assert isinstance(temperature, float)
+        assert 0 <= temperature <= 1.0