Spaces:
Running
Running
File size: 5,884 Bytes
89b8989 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
import pytest
from unittest.mock import patch, MagicMock, ANY
from PIL import Image
from io import BytesIO
import json
import sys
import os
import torch
# Add project root to path
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
from backend.app.services.food_analyzer_service import HybridFoodAnalyzer
# Test data
SAMPLE_NUTRITION_DATA = {
"calories": 95,
"protein": 0.5,
"fat": 0.3,
"carbohydrates": 25.0,
"fiber": 4.4,
"sugar": 19.0,
"sodium": 2,
"cholesterol": 0,
"saturated_fat": 0.1,
"calcium": 1,
"iron": 1,
"potassium": 195,
"vitamin_a": 2,
"vitamin_c": 14,
"vitamin_d": 0
}
SAMPLE_IMAGE = None
def create_test_image():
"""Helper function to create a test image"""
img = Image.new('RGB', (100, 100), color='red')
img_byte_arr = BytesIO()
img.save(img_byte_arr, format='JPEG')
return img_byte_arr.getvalue()
@pytest.fixture
def mock_analyzer():
with patch('transformers.AutoImageProcessor.from_pretrained') as mock_processor, \
patch('transformers.AutoModelForImageClassification.from_pretrained') as mock_model, \
patch('anthropic.Anthropic') as mock_anthropic:
# Setup mock processor
mock_processor.return_value = MagicMock()
# Setup mock model
mock_model.return_value = MagicMock()
mock_model.return_value.config.id2label = {0: "apple"}
mock_model.return_value.return_value = MagicMock(logits=torch.tensor([[0.9, 0.1]]))
# Setup mock anthropic
mock_anthropic.return_value = MagicMock()
mock_anthropic.return_value.messages.create.return_value.content = [
MagicMock(text=json.dumps(SAMPLE_NUTRITION_DATA))
]
analyzer = HybridFoodAnalyzer("test_api_key")
analyzer.processor = mock_processor.return_value
analyzer.model = mock_model.return_value
analyzer.claude_client = mock_anthropic.return_value
yield analyzer
def test_hybrid_food_analyzer_init(mock_analyzer):
"""Test initialization of HybridFoodAnalyzer"""
assert mock_analyzer is not None
assert hasattr(mock_analyzer, 'processor')
assert hasattr(mock_analyzer, 'model')
assert hasattr(mock_analyzer, 'claude_client')
def test_recognize_food_success(mock_analyzer):
"""Test successful food recognition"""
# Create test image
img = Image.new('RGB', (100, 100), color='red')
img_byte_arr = BytesIO()
img.save(img_byte_arr, format='JPEG')
# Call method
result = mock_analyzer.recognize_food(img_byte_arr.getvalue())
# Assertions
assert result["success"] is True
assert "food_name" in result
assert "chinese_name" in result
assert "confidence" in result
assert result["food_name"] == "apple"
assert result["chinese_name"] == "蘋果"
def test_recognize_food_error(mock_analyzer):
"""Test error handling in food recognition"""
# Setup mock to raise exception
mock_analyzer.model.side_effect = Exception("Test error")
# Call method with invalid image
result = mock_analyzer.recognize_food(b"invalid_image")
# Assertions
assert result["success"] is False
assert "error" in result
def test_analyze_nutrition_success(mock_analyzer):
"""Test successful nutrition analysis"""
# Call method
result = mock_analyzer.analyze_nutrition("apple")
# Assertions
assert result["success"] is True
assert "nutrition" in result
assert result["nutrition"] == SAMPLE_NUTRITION_DATA
mock_analyzer.claude_client.messages.create.assert_called_once()
def test_analyze_nutrition_error(mock_analyzer):
"""Test error handling in nutrition analysis"""
# Setup mock to raise exception
mock_analyzer.claude_client.messages.create.side_effect = Exception("API error")
# Call method
result = mock_analyzer.analyze_nutrition("invalid_food")
# Assertions
assert result["success"] is False
assert "error" in result
def test_process_image_success(mock_analyzer):
"""Test successful image processing"""
# Setup
test_image = create_test_image()
# Call method
result = mock_analyzer.process_image(test_image)
# Assertions
assert result["success"] is True
assert "food_name" in result
assert "nutrition" in result
assert "analysis" in result
assert "healthScore" in result["analysis"]
assert "recommendations" in result["analysis"]
assert "warnings" in result["analysis"]
def test_calculate_health_score(mock_analyzer):
"""Test health score calculation"""
# Test with sample nutrition data
score = mock_analyzer.calculate_health_score(SAMPLE_NUTRITION_DATA)
# Assert score is within expected range
assert isinstance(score, (int, float))
assert 0 <= score <= 100
def test_generate_recommendations(mock_analyzer):
"""Test generation of dietary recommendations"""
# Call method
recommendations = mock_analyzer.generate_recommendations(SAMPLE_NUTRITION_DATA)
# Assertions
assert isinstance(recommendations, list)
assert all(isinstance(rec, str) for rec in recommendations)
def test_generate_warnings(mock_analyzer):
"""Test generation of dietary warnings"""
# Call method
warnings = mock_analyzer.generate_warnings(SAMPLE_NUTRITION_DATA)
# Assertions
assert isinstance(warnings, list)
assert all(isinstance(warning, str) for warning in warnings)
def test_calculate_health_score_incomplete_data(mock_analyzer):
"""Test health score calculation with incomplete nutrition data"""
# Test with incomplete nutrition data
incomplete_nutrition = {"calories": 100}
health_score = mock_analyzer.calculate_health_score(incomplete_nutrition)
assert 0 <= health_score <= 100
|