| --- |
| title: ForgeSight |
| emoji: 🔍 |
| colorFrom: red |
| colorTo: gray |
| sdk: gradio |
| sdk_version: 5.29.1 |
| app_file: app.py |
| pinned: true |
| license: mit |
| short_description: "Multimodal QC Copilot on AMD MI300X + ROCm" |
| tags: |
| - amd |
| - rocm |
| - mi300x |
| - qwen |
| - vllm |
| - quality-control |
| - agents |
| --- |
| |
| # 🔍 ForgeSight — Multimodal QC Copilot on AMD Instinct™ MI300X |
|
|
| ForgeSight is a production-ready **Agentic Quality Control (QC) Pipeline** designed for high-throughput manufacturing environments. Built exclusively for the **AMD + lablab.ai Developer Hackathon**, it leverages the massive 192GB VRAM of the **AMD Instinct MI300X** to run a state-of-the-art multimodal multi-agent workflow. |
|
|
| ## 🚀 Key Features |
|
|
| * **Multimodal Reasoning**: Uses **Qwen2-VL-7B** to "see" and understand complex assembly line defects in a single forward pass. |
| * **4-Agent Pipeline**: Chained reasoning workflow: |
| 1. **Inspector** — Identifies surface defects, anomalies, and violations. |
| 2. **Diagnostician** — Performs industry-literate root-cause analysis. |
| 3. **Action** — Generates prioritized work orders and tool checklists. |
| 4. **Reporter** — Summarizes findings into human-readable executive reports. |
| * **MI300X Optimized**: Served via **vLLM on ROCm**, utilizing continuous batching and paged attention for near-instant inference. |
| * **Audit-Ready**: Generates downloadable **PDF QC Audit Reports** for every inspection. |
| * **Persistent Data**: Integrated with **MongoDB Atlas** for long-term defect tracking and telemetry history. |
|
|
| ## 🏗️ Technical Architecture |
|
|
| ```mermaid |
| graph TD |
| A[React Dashboard] --> B[FastAPI Gateway] |
| B --> C[Gradio Admin Console] |
| B --> D[4-Agent Pipeline] |
| D --> E[AMD MI300X Inference Server] |
| E --> F[vLLM / ROCm] |
| F --> G[Qwen2-VL-7B-Instruct] |
| B --> H[MongoDB Atlas] |
| B --> I[PDF Generator] |
| ``` |
|
|
| ### Stack |
| - **Hardware**: AMD Instinct MI300X (192GB HBM3) |
| - **Software**: ROCm 6.2, PyTorch 2.4, vLLM |
| - **Frontend**: React 18, Tailwind CSS, Recharts |
| - **Backend**: FastAPI, Gradio, Python 3.10 |
|
|
| ## 🛠️ Installation & Setup |
|
|
| 1. **Clone the Repo**: `git clone https://github.com/rasali535/hans.git` |
| 2. **Install Deps**: `pip install -r requirements.txt` |
| 3. **Configure Environment**: Set `AMD_INFERENCE_URL` and `AMD_INFERENCE_TOKEN` in your `.env`. |
| 4. **Launch**: `python hf_space/app.py` |
|
|
| ## 📊 Performance on AMD |
| The MI300X's 5.3 TB/s bandwidth allows ForgeSight to maintain **>2500 tokens/sec** throughput, enabling real-time visual inspection of high-speed manufacturing lines without the latency typical of cloud-based VLM APIs. |
|
|
| --- |
| Built by **Hans** for the **AMD Developer Hackathon**. |
|
|
