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#!/usr/bin/env python3
"""
Integration script for advanced training interface
Shows how to add comprehensive parameter controls to the main Gradio app
"""
import gradio as gr
import sys
import os
# Add parent directory to path to find ui module
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from ui.advanced_training import create_advanced_training_interface, start_advanced_training, start_simple_training
def create_enhanced_app():
"""Create the main app with advanced training controls integrated."""
with gr.Blocks(title="Dressify - Enhanced Outfit Recommendation", fill_height=True) as app:
gr.Markdown("## 🏆 Dressify – Advanced Outfit Recommendation System\n*Research-grade, self-contained outfit recommendation with comprehensive training controls*")
with gr.Tabs():
# Main recommendation tab
with gr.Tab("🎨 Recommend"):
gr.Markdown("### Upload wardrobe images and generate outfit recommendations")
# ... your existing recommendation interface
pass
# Advanced training tab
with gr.Tab("🔬 Advanced Training"):
# Create the advanced training interface
training_interface, components = create_advanced_training_interface()
# Set up event handlers for the training interface
components['start_btn'].click(
fn=start_simple_training,
inputs=[components['resnet_epochs'], components['vit_epochs']],
outputs=components['train_log']
)
components['start_advanced_btn'].click(
fn=start_advanced_training,
inputs=[
# ResNet parameters
components['resnet_epochs'], components['resnet_batch_size'], components['resnet_lr'],
components['resnet_optimizer'], components['resnet_weight_decay'], components['resnet_triplet_margin'],
components['resnet_embedding_dim'], components['resnet_backbone'], components['resnet_use_pretrained'],
components['resnet_dropout'],
# ViT parameters
components['vit_epochs'], components['vit_batch_size'], components['vit_lr'],
components['vit_optimizer'], components['vit_weight_decay'], components['vit_triplet_margin'],
components['vit_embedding_dim'], components['vit_num_layers'], components['vit_num_heads'],
components['vit_ff_multiplier'], components['vit_dropout'],
# Advanced parameters
components['use_mixed_precision'], components['channels_last'], components['gradient_clip'],
components['warmup_epochs'], components['scheduler_type'], components['early_stopping_patience'],
components['mining_strategy'], components['augmentation_level'], components['seed']
],
outputs=components['train_log']
)
# Simple training tab
with gr.Tab("🚀 Simple Training"):
gr.Markdown("### Quick training with default parameters")
epochs_res = gr.Slider(1, 50, value=10, step=1, label="ResNet epochs")
epochs_vit = gr.Slider(1, 100, value=20, step=1, label="ViT epochs")
train_log = gr.Textbox(label="Training Log", lines=10)
start_btn = gr.Button("Start Training")
start_btn.click(fn=start_simple_training, inputs=[epochs_res, epochs_vit], outputs=train_log)
# Other tabs...
with gr.Tab("📊 Embed (Debug)"):
gr.Markdown("### Debug image embeddings")
# ... your existing embed interface
pass
with gr.Tab("📥 Downloads"):
gr.Markdown("### Download trained models and artifacts")
# ... your existing downloads interface
pass
with gr.Tab("📈 Status"):
gr.Markdown("### System status and monitoring")
# ... your existing status interface
pass
return app
def create_minimal_integration():
"""Minimal integration example - just add the advanced training tab to existing app."""
# This shows how to add just the advanced training interface to your existing app.py
# 1. Import the advanced training functions
from advanced_training_ui import create_advanced_training_interface, start_advanced_training
# 2. In your existing app.py, add this tab:
"""
with gr.Tab("🔬 Advanced Training"):
# Create the advanced training interface
training_interface, components = create_advanced_training_interface()
# Set up event handlers
components['start_advanced_btn'].click(
fn=start_advanced_training,
inputs=[
components['resnet_epochs'], components['resnet_batch_size'], components['resnet_lr'],
components['resnet_optimizer'], components['resnet_weight_decay'], components['resnet_triplet_margin'],
components['resnet_embedding_dim'], components['resnet_backbone'], components['resnet_use_pretrained'],
components['resnet_dropout'], components['vit_epochs'], components['vit_batch_size'], components['vit_lr'],
components['vit_optimizer'], components['vit_weight_decay'], components['vit_triplet_margin'],
components['vit_embedding_dim'], components['vit_num_layers'], components['vit_num_heads'],
components['vit_ff_multiplier'], components['vit_dropout'], components['use_mixed_precision'],
components['channels_last'], components['gradient_clip'], components['warmup_epochs'],
components['scheduler_type'], components['early_stopping_patience'], components['mining_strategy'],
components['augmentation_level'], components['seed']
],
outputs=components['train_log']
)
"""
print("✅ Advanced training interface ready for integration!")
print("📝 Copy the code above into your existing app.py")
def show_parameter_examples():
"""Show examples of different parameter combinations."""
examples = {
"Quick Experiment": {
"resnet_epochs": 5,
"vit_epochs": 10,
"batch_size": 16,
"learning_rate": 1e-3,
"description": "Fast training for parameter testing"
},
"Balanced Training": {
"resnet_epochs": 20,
"vit_epochs": 30,
"batch_size": 64,
"learning_rate": 1e-3,
"description": "Standard quality training (default)"
},
"High Quality": {
"resnet_epochs": 50,
"vit_epochs": 100,
"batch_size": 32,
"learning_rate": 5e-4,
"description": "Production-quality models"
},
"Research Mode": {
"resnet_backbone": "resnet101",
"embedding_dim": 768,
"transformer_layers": 8,
"attention_heads": 12,
"mining_strategy": "hardest",
"description": "Maximum model capacity"
}
}
print("🎯 Parameter Combination Examples:")
print("=" * 50)
for name, params in examples.items():
print(f"\n📋 {name}:")
for key, value in params.items():
if key != "description":
print(f" {key}: {value}")
print(f" 💡 {params['description']}")
if __name__ == "__main__":
print("🚀 Dressify Advanced Training Integration")
print("=" * 50)
print("\n1️⃣ Create enhanced app with all features:")
print(" enhanced_app = create_enhanced_app()")
print("\n2️⃣ Minimal integration into existing app:")
create_minimal_integration()
print("\n3️⃣ Parameter combination examples:")
show_parameter_examples()
print("\n✅ Integration complete! Your app now has comprehensive training controls.")
print("\n📚 See TRAINING_PARAMETERS.md for detailed parameter explanations.")
print("🔧 Use the advanced training interface to experiment with different configurations.")
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