fix HF readme metadata

README.md

@@ -1,3 +1,12 @@
+---
+title: DriveCore
+emoji: 🚗
+colorFrom: indigo
+colorTo: purple
+sdk: docker
+pinned: false
+---
+
 # DriveCore 🚗⚡
 
 **An AI-powered incident response, forensic analysis, and branch debugging platform for autonomous vehicle fleets.**
@@ -11,7 +20,6 @@ Quick Start Instructions to Run on AMD GPU Droplet:
 ollama serve &
 
 2) Start Qwen backend
-->Go into Docker container first
 cd /app/Autopulse
 uvicorn backend:app --host 0.0.0.0 --port 8006 &
 
@@ -21,144 +29,28 @@ bun run dev --host 0.0.0.0 --port 30000 &
 4) Go to Website
 http://165.245.137.74:30000
 
-
 ## What is DriveCore?
 
 DriveCore routes every autonomous vehicle incident, near miss, and sensor log through a multi-step AI agent pipeline — surfacing root causes, compliance concerns, and operator coaching plans in seconds.
 
-When an AV has an incident (GNSS failure, unexpected stop, software regression), engineers spend hours manually triaging logs and writing reports. DriveCore automates that entire workflow using a 5-step LangChain agent pipeline powered by Qwen3 on AMD GPU.
-
----
-
-## ✨ Features
-
-| Module | What it does |
-|---|---|
-| **Incident Analysis** | Submit text or file-based incident reports. Five AI agents intake, enrich, analyze root causes, generate response plans, and draft formal safety reports. |
-| **Branch Debug** | Paste a git diff or code snippet + failure description → get ranked root-cause suspects with file path, line range, confidence, and mechanism. IP Shield strips secrets before anything reaches the AI. |
-| **Forensic Debrief** | Three-stage post-deployment forensic pipeline: code-only hypotheses → log correlation → full ROS bag / sensor data trace. |
-| **Compliance Concerns** | Aggregated view of all compliance flags across incidents, referenced to AV standards (NHTSA AV Policy, ISO 26262, SAE J3016, FMVSS, UN R157). |
-| **Coaching Recommendations** | Operator and engineer action items surfaced from all completed analyses. |
-| **Safety Reports** | Browse and export completed incident analyses as Markdown reports. |
-
----
-
 ## 🤖 AI Agent Pipeline
 
-DriveCore uses a **5-step LangChain agentic workflow** powered by **Qwen3 on AMD Instinct GPU**:
-
-```
-Step 1: Intake Agent        → Classifies incident, extracts vehicle ID, timestamp, severity (P1/P2/P3)
-Step 2: Enrichment Agent    → Identifies affected AV subsystems (GNSS, LiDAR, perception, planning, control)
-Step 3: Risk Agent          → Root cause analysis ranked by confidence (ISO 26262, SAE J3016, NHTSA)
-Step 4: Response Agent      → Immediate actions, short-term fixes, long-term prevention measures
+Step 1: Intake Agent → Classifies incident, extracts vehicle ID, timestamp, severity
+Step 2: Enrichment Agent → Identifies affected AV subsystems
+Step 3: Risk Agent → Root cause analysis ranked by confidence
+Step 4: Response Agent → Immediate actions, short-term fixes, long-term prevention
 Step 5: Documentation Agent → Formal Markdown safety report + Qwen's Personal Recommendation
-```
-
-Each step feeds into the next, forming a genuine multi-agent reasoning chain — not just a single LLM call.
-
----
 
 ## 🏗 Tech Stack
 
-| Layer | Technology |
-|---|---|
-| **AI Model** | Qwen3 (open-source) via Ollama |
-| **Agent Framework** | LangChain (multi-step agentic pipeline) |
-| **GPU Compute** | AMD Instinct MI300X via AMD Developer Cloud |
-| **Backend** | FastAPI (Python), OpenAI-compatible API |
-| **Frontend** | React 19 + TanStack Start + Vite + Tailwind CSS v4 |
-| **Database** | Supabase (Postgres + Auth) |
-| **Deployment** | AMD Developer Cloud (GPU backend) |
-
----
-
-## 🚀 Quick Start
-
-### Prerequisites
-- AMD Developer Cloud account with GPU droplet
-- Docker
-- Ollama
-- Bun ≥ 1.0
-
-### 1. Clone the repo
-```bash
-git clone https://github.com/nextgenframes/DriveCore.git
-cd DriveCore
-```
-
-### 2. Install dependencies
-```bash
-bun install
-```
-
-### 3. Set up environment variables
-```bash
-cp .env.example .env
-```
-
-Edit `.env`:
-```
-VITE_SUPABASE_URL=your_supabase_url
-VITE_SUPABASE_PUBLISHABLE_KEY=your_anon_key
-SUPABASE_SERVICE_ROLE_KEY=your_service_role_key
-AI_BASE_URL=http://your_amd_gpu_ip:8006
-AI_API_KEY=dummy
-AI_MODEL=qwen3
-```
-
-### 4. Start the Qwen3 backend on AMD GPU
-```bash
-# Inside your AMD GPU container
-ollama serve &
-ollama pull qwen3
-pip install langchain langchain-ollama fastapi uvicorn
-uvicorn backend:app --host 0.0.0.0 --port 8006 &
-```
-
-### 5. Run the frontend
-```bash
-bun run dev --host 0.0.0.0 --port 30000
-```
-
----
-
-## 🔌 API Endpoints
-
-| Endpoint | Method | Description |
-|---|---|---|
-| `/health` | GET | Health check — confirms Qwen3 is running |
-| `/triage` | POST | Full 5-step agentic incident triage pipeline |
-| `/branch-debug` | POST | Git diff root cause analysis |
-| `/forensic` | POST | Forensic content analysis |
-| `/v1/chat/completions` | POST | OpenAI-compatible endpoint |
-
----
-
-## 🔐 Security
-
-- **IP Shield** — Branch Debug strips API keys, JWTs, and secrets before sending to AI
-- **SSRF Guard** — Forensic pipeline blocks loopback, private, and cloud metadata IPs
-- **Auth** — Supabase authentication with row-level security
-
----
+- AI Model: Qwen3 via Ollama
+- Agent Framework: LangChain
+- GPU Compute: AMD Instinct MI300X via AMD Developer Cloud
+- Backend: FastAPI (Python)
+- Frontend: React 19 + TanStack Start + Vite + Tailwind CSS v4
 
 ## 🏆 Hackathon
 
-Built for **AMD Developer Cloud Hackathon — Track 1: AI Agents & Agentic Workflows**
-
-- ✅ LangChain multi-step agentic pipeline
-- ✅ Qwen3 open-source model
-- ✅ Running on AMD Instinct GPU via AMD Developer Cloud
-- ✅ Real AV safety use case (incident triage, branch debug, forensic analysis)
-- ✅ OpenAI-compatible API
-
----
-
-## 📜 License
-
-Proprietary — internal use only unless otherwise specified.
-
----
+Built for AMD Developer Cloud Hackathon — Track 1: AI Agents & Agentic Workflows
 
 *Powered by AMD Instinct GPU ⚡*