create_dummy_model.py

#!/usr/bin/env python3
"""
Create a dummy TensorFlow model for testing the system
This is useful if you don't have a trained model yet
"""

import os
import json
import numpy as np
import tensorflow as tf
from tensorflow import keras

def create_dummy_model():
    """Create a simple dummy model for testing"""
    print("Creating dummy TensorFlow model...")
    
    # Create a simple CNN model
    model = keras.Sequential([
        keras.layers.Input(shape=(224, 224, 3)),
        keras.layers.Conv2D(32, (3, 3), activation='relu'),
        keras.layers.MaxPooling2D((2, 2)),
        keras.layers.Conv2D(64, (3, 3), activation='relu'),
        keras.layers.MaxPooling2D((2, 2)),
        keras.layers.Conv2D(64, (3, 3), activation='relu'),
        keras.layers.Flatten(),
        keras.layers.Dense(64, activation='relu'),
        keras.layers.Dropout(0.5),
        keras.layers.Dense(3, activation='softmax')  # 3 classes
    ])
    
    model.compile(
        optimizer='adam',
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )
    
    return model

def save_model(model, output_dir='my_model'):
    """Save the model in the required format"""
    print(f"Saving model to {output_dir}/...")
    
    # Create directory if it doesn't exist
    os.makedirs(output_dir, exist_ok=True)
    
    # Save the model
    model.save(output_dir)
    
    # Create metadata.json
    metadata = {
        "tfjsVersion": "1.3.1",
        "tmVersion": "2.4.0",
        "packageVersion": "0.8.4",
        "packageName": "@teachablemachine/image",
        "timeStamp": "2025-10-17T14:00:00.000Z",
        "userMetadata": {},
        "modelName": "tm-my-image-model",
        "labels": [
            "Person_1",
            "Person_2",
            "Unknown"
        ],
        "imageSize": 224
    }
    
    metadata_path = os.path.join(output_dir, 'metadata.json')
    with open(metadata_path, 'w') as f:
        json.dump(metadata, f, indent=2)
    
    print("✓ Model saved successfully!")
    print(f"✓ Metadata saved to {metadata_path}")
    print("\nModel classes:")
    for i, label in enumerate(metadata['labels']):
        print(f"  {i+1}. {label}")

def main():
    print("=" * 60)
    print("Dummy Model Creator")
    print("=" * 60)
    print("\nThis script creates a simple TensorFlow model for testing.")
    print("⚠️  WARNING: This is NOT a trained model!")
    print("It will return random predictions.\n")
    
    response = input("Create dummy model? (y/n): ").strip().lower()
    
    if response != 'y':
        print("Cancelled.")
        return
    
    try:
        model = create_dummy_model()
        save_model(model)
        
        print("\n" + "=" * 60)
        print("Dummy model created successfully!")
        print("=" * 60)
        print("\nYou can now test the system with this model.")
        print("However, it will return random predictions.")
        print("\nTo get real face recognition, you need to:")
        print("1. Go to https://teachablemachine.withgoogle.com/")
        print("2. Train a model with real face images")
        print("3. Export and replace the my_model/ directory")
        print("\n")
        
    except Exception as e:
        print(f"\n❌ Error creating model: {e}")
        print("\nMake sure TensorFlow is installed:")
        print("  pip install tensorflow")

if __name__ == '__main__':
    main()