Update README.md

README.md

@@ -1,14 +1,49 @@
 ---
-title: Depression Detection Mcp Agent
-emoji: 🐨
-colorFrom: indigo
-colorTo: blue
+title: Early Depression Detection MCP Agent
+emoji: 🧠
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 6.0.1
+sdk_version: 5.0.0
 app_file: app.py
-pinned: false
+tags:
+- mcp-in-action-track-consumer
+- depression-detection
+- mental-health
+- longformer
+- mcp
+- agents
 license: mit
-short_description: MCP-enabled depression detection agent using Mental-Longform
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🧠 Early Depression Detection MCP Agent
+
+**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]
+
+## 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.
+
+**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
+
+**Model:** [avtak/erisk-longformer-depression-v1](https://huggingface.co/avtak/erisk-longformer-depression-v1)
+
+## 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
+
+## Team
+- Hassan Hassanzadeh Aliabadi (@avtak)