fhf

readme.md

@@ -3,9 +3,7 @@ title: Intelligent Style Transfer System
 emoji: 🎨
 colorFrom: blue
 colorTo: purple
-sdk: gradio
-sdk_version: "4.44.0"
-app_file: app.py
+sdk: docker
 pinned: false
 license: mit
 ---
@@ -16,10 +14,10 @@ This project intelligently analyzes image attributes to recommend and apply rele
 
 ## 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.
+- **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
 
@@ -31,11 +29,11 @@ This project intelligently analyzes image attributes to recommend and apply rele
 
 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
+- **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
 
 ---
 
-*Built with Gradio, TensorFlow, and ❤️*
+_Built with TensorFlow and ❤️_