Update README.md

README.md

@@ -4,8 +4,9 @@ emoji: 🧠
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 5.0.0
+sdk_version: 6.0.1
 app_file: app.py
+pinned: true
 tags:
 - mcp-in-action-track-consumer
 - depression-detection
@@ -14,6 +15,7 @@ tags:
 - mcp
 - agents
 license: mit
+short_description: MCP-enabled depression detection agent
 ---
 
 # 🧠 Early Depression Detection MCP Agent
@@ -21,29 +23,45 @@ license: mit
 **Hackathon:** MCP 1st Birthday - Track 2: MCP in Action (Consumer)  
 **Author:** Hassan Hassanzadeh Aliabadi | [LinkedIn](https://www.linkedin.com/in/hassanzh/)
 
-## Demo Video
-[INSERT YOUTUBE LINK HERE]
+## 📹 Demo Video
+[INSERT YOUTUBE/LOOM LINK HERE]
 
-## Social Media Post
+## 📱 Social Media Post
 [INSERT LINKEDIN/X POST LINK HERE]
 
-## Project Description
-This MCP-enabled agent detects depression risk from social media text using Mental-Longformer, achieving F1-score of 0.7668 on eRisk 2025 test data.
+## 🎯 Project Description
+This MCP-enabled agent detects depression risk from social media text using Mental-Longformer, achieving **F1-score of 0.7668** on eRisk 2025 test data.
 
 **Key Features:**
-- 4,096-token context window (8x BERT's capacity)
-- Trained on eRisk 2017-2022 datasets
-- Data augmentation with Gemini 2.5 Flash Lite
-- Real-time linguistic pattern analysis
+- 🔍 4,096-token context window (8x BERT's capacity)
+- 📊 Trained on eRisk 2017-2022 datasets
+- 🤖 Data augmentation with Gemini 2.5 Flash Lite
+- ⚡ Real-time linguistic pattern analysis
+- 🔌 MCP-enabled for agent integration
 
 **Model:** [avtak/erisk-longformer-depression-v1](https://huggingface.co/avtak/erisk-longformer-depression-v1)
 
-## How It Works
+## 🧪 How It Works
 The agent analyzes long-form text for linguistic markers including:
 - Anhedonia (loss of interest)
 - Self-focused negative language
 - Social withdrawal indicators
 - Hopelessness themes
+- Disrupted sleep/energy patterns
 
-## Team
+## 🏆 Research Background
+Built on Master's thesis research at **University of Malaya**, this model addresses critical challenges in early depression detection:
+- Handles imbalanced datasets through LLM-powered augmentation
+- Captures long-context dependencies (4096 vs 512 tokens)
+- Rigorous 5-fold cross-validation (mean F1: 0.862, std: 0.009)
+- Validated on held-out eRisk 2025 test set
+
+## 👥 Team
 - Hassan Hassanzadeh Aliabadi (@avtak)
+
+## ⚠️ Ethical Considerations
+This is a research tool, **not a medical diagnostic instrument**. Always consult qualified healthcare professionals for mental health concerns.
+
+**Crisis Resources:**
+- 🆘 Crisis Text Line: Text HOME to 741741 (US)
+- 🌍 International: [befrienders.org](https://befrienders.org)