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#!/usr/bin/env python3
"""
Test script to verify the Aircraft Classifier Gradio app functionality
"""
import torch
import numpy as np
from PIL import Image
import sys
import os
# Add current directory to path
sys.path.append('.')
def test_app_functionality():
"""Test that the app components work correctly"""
print("🧪 Testing Aircraft Classifier App Components")
print("=" * 50)
try:
# Import app components
from app import AircraftClassifier, classify_aircraft, get_top_predictions, CLASS_NAMES
from config import MODEL_METRICS
print("✅ Successfully imported app components")
# Test model creation
model = AircraftClassifier(num_classes=len(CLASS_NAMES))
print(f"✅ Model created: {model.__class__.__name__}")
print(f" Classes: {len(CLASS_NAMES)}")
# Create a dummy test image (random noise)
test_image = Image.fromarray(np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8))
print("✅ Created test image")
# Test classification function
results = classify_aircraft(test_image)
print("✅ Classification function works")
print(f" Got {len(results)} class predictions")
# Test top predictions function
top_preds = get_top_predictions(test_image)
print("✅ Top predictions function works")
print(" Sample output:")
print(f" {top_preds[:100]}...")
# Display model metrics
print(f"\n📊 Model Performance (from config):")
for metric, value in MODEL_METRICS.items():
print(f" {metric}: {value}")
print(f"\n🛩️ Aircraft Classes:")
for i, class_name in enumerate(CLASS_NAMES):
print(f" {i+1:2d}. {class_name}")
print(f"\n🎉 All tests passed! The Gradio app is ready to deploy.")
print(f"💡 To launch the interface, run: python app.py")
return True
except Exception as e:
print(f"❌ Test failed: {e}")
import traceback
traceback.print_exc()
return False
if __name__ == "__main__":
success = test_app_functionality()
sys.exit(0 if success else 1) |