Update README.md

README.md

@@ -1,40 +1,51 @@
-# Aesthetix AI: Facial Symmetry & Aesthetic Rater 🗿
-
-An AI-powered computer vision system that analyzes facial aesthetics and predicts a rating on a 1.0-5.0 scale. Built with PyTorch, utilizing a fine-tuned ResNet18 architecture and Grad-CAM for visual explainability.
-
-**Try the App:** [Link to your Hugging Face Space]
-
-## Overview
-
-Unlike standard face classifiers that just detect identity, Aesthetix AI is a **regression model** trained to quantify subjective facial attractiveness based on the SCUT-FBP5500 Dataset.
-
-It features a complete inference pipeline:
-
-1. **Face Isolation**: Uses Haar Cascades to detect and tightly crop the face.
-2. **Semantic Segmentation**: Uses DeepLabV3 to remove background noise (hair/neck masking) to force the model to evaluate facial geometry only.
-3. **Scoring Engine**: A ResNet18 CNN fine-tuned to predict a continuous float score.
-4. **Explainability**: Generates Grad-CAM heatmaps to visualize exactly which features (eyes, jawline, symmetry) the model focused on.
-
-## Performance
-
-- **Architecture**: ResNet18 (Pre-trained on ImageNet → Fine-tuned)
-- **Loss Function**: MSELoss (Mean Squared Error)
-- **Optimizer**: Adam (lr=1e-4)
-- **Validation Loss**: 0.0858 (MSE)
-  - **Interpretation**: The model's predictions are on average within +/- 0.29 points of the human ground truth.
-
-## The Stack
-
-- **PyTorch**: Core deep learning framework.
-- **Torchvision**: Pre-trained models (ResNet18, DeepLabV3).
-- **OpenCV**: Face detection and image processing.
-- **Streamlit**: Interactive web interface.
-- **Grad-CAM**: Visual attention mapping.
-
-## Installation & Usage
-
-1. Clone the repo:
-
-   ```bash
-   git clone https://github.com/AKMessi/facial-rating-using-cnn.git
+---
+title: Aesthetix AI
+emoji: 🗿
+colorFrom: gray
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.29.0
+app_file: app.py
+pinned: false
+---
+
+# Aesthetix AI: Facial Symmetry & Aesthetic Rater 🗿
+
+An AI-powered computer vision system that analyzes facial aesthetics and predicts a rating on a 1.0-5.0 scale. Built with PyTorch, utilizing a fine-tuned ResNet18 architecture and Grad-CAM for visual explainability.
+
+**Try the App:** [Link to your Hugging Face Space]
+
+## Overview
+
+Unlike standard face classifiers that just detect identity, Aesthetix AI is a **regression model** trained to quantify subjective facial attractiveness based on the SCUT-FBP5500 Dataset.
+
+It features a complete inference pipeline:
+
+1. **Face Isolation**: Uses Haar Cascades to detect and tightly crop the face.
+2. **Semantic Segmentation**: Uses DeepLabV3 to remove background noise (hair/neck masking) to force the model to evaluate facial geometry only.
+3. **Scoring Engine**: A ResNet18 CNN fine-tuned to predict a continuous float score.
+4. **Explainability**: Generates Grad-CAM heatmaps to visualize exactly which features (eyes, jawline, symmetry) the model focused on.
+
+## Performance
+
+- **Architecture**: ResNet18 (Pre-trained on ImageNet → Fine-tuned)
+- **Loss Function**: MSELoss (Mean Squared Error)
+- **Optimizer**: Adam (lr=1e-4)
+- **Validation Loss**: 0.0858 (MSE)
+  - **Interpretation**: The model's predictions are on average within +/- 0.29 points of the human ground truth.
+
+## The Stack
+
+- **PyTorch**: Core deep learning framework.
+- **Torchvision**: Pre-trained models (ResNet18, DeepLabV3).
+- **OpenCV**: Face detection and image processing.
+- **Streamlit**: Interactive web interface.
+- **Grad-CAM**: Visual attention mapping.
+
+## Installation & Usage
+
+1. Clone the repo:
+
+   ```bash
+   git clone https://github.com/AKMessi/facial-rating-using-cnn.git
    cd facial-beauty-rating-cnns# facial-rating-using-cnn