---
title: Intelligent Style Transfer System
emoji: 🎨
colorFrom: blue
colorTo: purple
sdk: docker
app_file: app.py
pinned: false
license: mit
---

# 🎨 Intelligent Multi-Attribute Style Transfer

This project intelligently analyzes image attributes to recommend and apply relevant style transfers. Instead of simply applying any filter, the system first classifies the image to ensure the suggested transformation makes contextual sense.

## Key Features

- **Multi-Attribute Analysis**: Classifies images across four key attributes: content (human vs. landscape), style (photo vs. art), time of day, and weather.
- **Intelligent Recommendations**: Style transfers are only suggested when the AI's confidence in its analysis exceeds 60%, preventing irrelevant suggestions.
- **High-Accuracy Models**: Core classification models achieve 90%+ accuracy, ensuring reliable analysis.
- **Combined Effects**: Users can apply a single suggested effect or chain multiple transformations together.

## How to Use

1.  **Upload** an image.
2.  **Review** the AI's analysis and smart style suggestions.
3.  **Apply** your chosen effect(s).

## Technical Details

The system is powered by a series of fine-tuned CNN models built on ResNet50 and MobileNetV2 for high performance and accuracy.

- **Content Classification (Human/Landscape)**: 97% accuracy
- **Style Classification (Photo/Art)**: 92% accuracy
- **Time of Day Classification (Day/Night)**: 90% accuracy
- **Weather Classification (Foggy/Clear)**: 85% accuracy

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_Built with TensorFlow and ❤️_