Update app.py

app.py

@@ -54,12 +54,55 @@ footer { display: none !important; }
 """
 
 ABOUT_MARKDOWN = """
-### Model: Vision Transformer (ViT)
-This application uses a state-of-the-art Vision Transformer model to perform real-time facial emotion recognition.
-### Dataset
-The model was pre-trained on the **AffectNet** dataset, the largest database of "in the wild" facial expressions. This ensures robust performance on real-world, spontaneous emotions.
-### MLOps Pipeline
-This application is the deployment artifact of a complete MLOps pipeline, demonstrating skills in data management (DVC), model training (TensorFlow), and application development (Gradio).
+## 🚀 About This Project
+
+This application is the culmination of a complete, end-to-end MLOps project, demonstrating the full lifecycle from research and experimentation to a final, deployed, state-of-the-art solution.
+
+**💻 [View Project on GitHub](https://github.com/AlyyanAhmed21/Emotion-Recognition-MLOps)** 
+
+---
+
+
+### ✨ Key Technical Features
+
+*   **State-of-the-Art AI Model:** The core of this app is a **Swin Transformer**, a powerful Vision Transformer (ViT) architecture. It was pre-trained on the massive **AffectNet** dataset, ensuring high accuracy and robust generalization to real-world, "in the wild" facial expressions.
+
+*   **Full MLOps Lifecycle Demonstration:** This project wasn't a straight line. It involved a reproducible pipeline built with **DVC** that progressed through:
+    1.  **Initial Model (MobileNetV2):** Achieved high accuracy (~96%) on a clean, posed dataset (CK+) but failed to generalize to real-world faces, demonstrating a key data science challenge.
+    2.  **Data-Centric Iteration:** Experimented with combining and balancing multiple datasets (FER+, CK+) to improve robustness, highlighting the importance of data quality.
+    3.  **Final SOTA Integration:** Strategically pivoted to a powerful, pre-trained model from the Hugging Face Hub to achieve superior real-world performance.
+
+*   **Full-Stack & Deployment:** The application architecture evolved from a Python-only script to a decoupled **FastAPI backend** and a **React frontend**, and was ultimately deployed as this streamlined and robust **Gradio** application.
+
+*   **Containerized & Automated:** The entire application is packaged with **Docker** and is set up for **CI/CD with GitHub Actions**, enabling automated testing and deployment to cloud platforms like Hugging Face Spaces.
+
+---
+
+### 🛠️ Architecture & Tech Stack
+
+*   **Machine Learning & CV:**
+    *   Python, PyTorch, Hugging Face `transformers`
+    *   `MTCNN` for robust face detection
+    *   `OpenCV` for image processing
+
+*   **MLOps & DevOps:**
+    *   **DVC:** For data versioning and building reproducible pipelines.
+    *   **GitHub Actions:** For CI/CD and automated deployment.
+    *   **Docker:** For containerizing the application for consistent environments.
+    *   *(MLflow was used for experiment tracking during the training phase)*
+
+*   **Application & UI:**
+    *   **Gradio:** For building and deploying this interactive UI.
+    *   *(FastAPI and React were used in an alternate full-stack version of the application)*
+
+### 💡 Skills Demonstrated
+
+This project showcases a comprehensive skillset in building modern AI systems:
+
+*   **Data Science & Analysis:** Deeply analyzing dataset quality, identifying limitations (e.g., posed vs. "in the wild"), and making strategic, data-driven decisions to improve model performance.
+*   **Deep Learning & Computer Vision:** Implementing and fine-tuning multiple advanced architectures (CNNs, Vision Transformers) for a complex computer vision task.
+*   **Full-Stack Application Development:** Building both decoupled (FastAPI/React) and unified (Gradio) web applications to serve a live ML model.
+*   **MLOps & CI/CD Automation:** Engineering a complete, end-to-end pipeline that is version-controlled, reproducible, and automatically deployed, reflecting best practices in production machine learning.
 """
 
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