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README.md

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----
-title: ForgeSight
-emoji: 🔍
-colorFrom: red
-colorTo: gray
-sdk: docker
-pinned: true
-license: mit
-short_description: "Multimodal QC Copilot · AMD MI300X · Qwen2-VL"
-tags:
-  - amd
-  - rocm
-  - mi300x
-  - qwen
-  - qwen2-vl
-  - vllm
-  - quality-control
-  - agents
-  - multimodal
-  - industrial-ai
-  - vision
----
-
-# 🔍 ForgeSight — Multimodal Quality-Control Copilot
-
-### ⚡ Live Status (Hackathon Mode)
-- **Primary Inference**: AMD Instinct MI300X (192GB VRAM)
-- **Backend**: FastAPI + vLLM on ROCm
-- **Status**: ✅ **ONLINE** (Live Inference Active)
-- **Current Server**: `165.245.137.80` (vLLM via Token Auth)
-
-> **AMD + lablab.ai Hackathon** — Track 2 (AMD Developer Cloud) · Track 1 (AI Agents) · Track 3 (Vision & Multimodal AI)
-
-ForgeSight is a production-ready AI system that performs automated visual quality control on the **AMD Instinct MI300X** GPU. Upload a product image and a 4-agent agentic pipeline delivers a structured defect report in seconds.
-
----
-
-## 🤖 Qwen2-VL — The Brain of ForgeSight
-
-ForgeSight is powered entirely by **[Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)**, Alibaba Cloud's state-of-the-art multimodal vision-language model.
-
-### Why Qwen2-VL?
-
-| Capability | How ForgeSight uses it |
-| --- | --- |
-| **Image understanding** | Reads raw product images — scratches, cracks, misalignments |
-| **Structured JSON output** | Each agent returns typed JSON: verdicts, defect lists, action codes |
-| **Long-context reasoning** | Diagnostician agent cross-references inspector findings over 8K tokens |
-| **Multilingual** | Operator notes can be submitted in any language |
-| **192 GB VRAM on MI300X** | Entire 7B model fits in GPU memory with headroom for 88× concurrent sessions |
-
-### How Qwen2-VL is used across the 4-agent pipeline
-
-```text
-Image Input (JPEG/PNG/WEBP)
-        │
-        ▼
-┌─────────────────────────────────────────────────────────┐
-│ Agent 1 · INSPECTOR  (Qwen2-VL)                         │
-│  → Detects defects, produces verdict: pass / warn / fail│
-└──────────────────────┬──────────────────────────────────┘
-                       │ inspector_report
-                       ▼
-┌─────────────────────────────────────────────────────────┐
-│ Agent 2 · DIAGNOSTICIAN  (Qwen2-VL)                     │
-│  → Classifies root cause, estimates severity            │
-└──────────────────────┬──────────────────────────────────┘
-                       │ diagnostic_report
-                       ▼
-┌─────────────────────────────────────────────────────────┐
-│ Agent 3 · ACTION  (Qwen2-VL)                            │
-│  → Maps defects to priority codes (P0–P3) + actions     │
-└──────────────────────┬──────────────────────────────────┘
-                       │ action_plan
-                       ▼
-┌─────────────────────────────────────────────────────────┐
-│ Agent 4 · REPORTER  (Qwen2-VL)                          │
-│  → Writes a human-readable QC report + social post      │
-└─────────────────────────────────────────────────────────┘
-        │
-        ▼
-Structured JSON → React Dashboard
-```
-
----
-
-## 🏗️ Architecture
-
-| Layer | Technology |
-| --- | --- |
-| **Hardware** | AMD Instinct MI300X · 192 GB HBM3 |
-| **Runtime** | ROCm 7.2.1 · PyTorch 2.10 (ROCm build) |
-| **Inference** | vLLM 0.20.1 (ROCm wheels) · OpenAI-compatible API |
-| **Model** | Qwen/Qwen2-VL-7B-Instruct |
-| **Backend** | FastAPI + Gradio · Python 3.12 |
-| **Persistence** | MongoDB Atlas (motor async driver) |
-| **Frontend** | React 18 · Recharts · Lucide |
-| **Deployment** | Hugging Face Spaces (Docker) |
-
----
-
-## 🚀 Running Locally
-
-```bash
-# 1. Start vLLM on your AMD GPU
-python -m vllm.entrypoints.openai.api_server \
-  --model Qwen/Qwen2-VL-7B-Instruct \
-  --host 0.0.0.0 --port 8000 \
-  --allowed-origins '["*"]'
-
-# 2. Set environment variables
-export AMD_INFERENCE_URL=http://localhost:8000
-export AMD_MODEL_NAME=Qwen/Qwen2-VL-7B-Instruct
-export MONGO_URL=mongodb+srv://...   # optional
-
-# 3. Start the backend
-pip install -r requirements.txt
-python app.py
-```
-
----
-
-## 🎯 Hackathon Track Alignment
-
-- **Track 2 · AMD Developer Cloud** *(primary)*: Real MI300X inference via ROCm/vLLM
-- **Track 1 · AI Agents**: 4-agent agentic workflow (Inspector → Diagnostician → Action → Reporter)
-- **Track 3 · Vision & Multimodal AI**: Qwen2-VL processing product images for industrial QC
-- **Qwen Challenge**: Qwen2-VL-7B-Instruct is the sole model powering all four agents end-to-end
+---
+title: ForgeSight
+emoji: 🔍
+colorFrom: red
+colorTo: gray
+sdk: docker
+pinned: true
+license: mit
+short_description: "Multimodal QC Copilot · AMD MI300X · Qwen2-VL"
+tags:
+  - amd
+  - rocm
+  - mi300x
+  - qwen
+  - qwen2-vl
+  - vllm
+  - quality-control
+  - agents
+  - multimodal
+  - industrial-ai
+  - vision
+---
+
+# 🔍 ForgeSight — Multimodal Quality-Control Copilot
+
+### ⚡ Live Status (Hackathon Mode)
+- **Primary Inference**: AMD Instinct MI300X (192GB VRAM)
+- **Backend**: FastAPI + vLLM on ROCm
+- **Status**: ✅ **ONLINE** (Live Inference Active)
+- **Current Server**: `165.245.137.80` (vLLM via Token Auth)
+
+> **AMD + lablab.ai Hackathon** — Track 2 (AMD Developer Cloud) · Track 1 (AI Agents) · Track 3 (Vision & Multimodal AI)
+
+ForgeSight is a production-ready AI system that performs automated visual quality control on the **AMD Instinct MI300X** GPU. Upload a product image and a 4-agent agentic pipeline delivers a structured defect report in seconds.
+
+---
+
+## 🤖 Qwen2-VL — The Brain of ForgeSight
+
+ForgeSight is powered entirely by **[Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)**, Alibaba Cloud's state-of-the-art multimodal vision-language model.
+
+### Why Qwen2-VL?
+
+| Capability | How ForgeSight uses it |
+| --- | --- |
+| **Image understanding** | Reads raw product images — scratches, cracks, misalignments |
+| **Structured JSON output** | Each agent returns typed JSON: verdicts, defect lists, action codes |
+| **Long-context reasoning** | Diagnostician agent cross-references inspector findings over 8K tokens |
+| **Multilingual** | Operator notes can be submitted in any language |
+| **192 GB VRAM on MI300X** | Entire 7B model fits in GPU memory with headroom for 88× concurrent sessions |
+
+### How Qwen2-VL is used across the 4-agent pipeline
+
+```text
+Image Input (JPEG/PNG/WEBP)
+        │
+        ▼
+┌─────────────────────────────────────────────────────────┐
+│ Agent 1 · INSPECTOR  (Qwen2-VL)                         │
+│  → Detects defects, produces verdict: pass / warn / fail│
+└──────────────────────┬──────────────────────────────────┘
+                       │ inspector_report
+                       ▼
+┌─────────────────────────────────────────────────────────┐
+│ Agent 2 · DIAGNOSTICIAN  (Qwen2-VL)                     │
+│  → Classifies root cause, estimates severity            │
+└──────────────────────┬──────────────────────────────────┘
+                       │ diagnostic_report
+                       ▼
+┌─────────────────────────────────────────────────────────┐
+│ Agent 3 · ACTION  (Qwen2-VL)                            │
+│  → Maps defects to priority codes (P0–P3) + actions     │
+└──────────────────────┬──────────────────────────────────┘
+                       │ action_plan
+                       ▼
+┌─────────────────────────────────────────────────────────┐
+│ Agent 4 · REPORTER  (Qwen2-VL)                          │
+│  → Writes a human-readable QC report + social post      │
+└─────────────────────────────────────────────────────────┘
+        │
+        ▼
+Structured JSON → React Dashboard
+```
+
+---
+
+## 🏗️ Architecture
+
+| Layer | Technology |
+| --- | --- |
+| **Hardware** | AMD Instinct MI300X · 192 GB HBM3 |
+| **Runtime** | ROCm 7.2.1 · PyTorch 2.10 (ROCm build) |
+| **Inference** | vLLM 0.20.1 (ROCm wheels) · OpenAI-compatible API |
+| **Model** | Qwen/Qwen2-VL-7B-Instruct |
+| **Backend** | FastAPI + Gradio · Python 3.12 |
+| **Persistence** | MongoDB Atlas (motor async driver) |
+| **Frontend** | React 18 · Recharts · Lucide |
+| **Deployment** | Hugging Face Spaces (Docker) |
+
+---
+
+## 🚀 Running Locally
+
+```bash
+# 1. Start vLLM on your AMD GPU
+python -m vllm.entrypoints.openai.api_server \
+  --model Qwen/Qwen2-VL-7B-Instruct \
+  --host 0.0.0.0 --port 8000 \
+  --allowed-origins '["*"]'
+
+# 2. Set environment variables
+export AMD_INFERENCE_URL=http://localhost:8000
+export AMD_MODEL_NAME=Qwen/Qwen2-VL-7B-Instruct
+export MONGO_URL=mongodb+srv://...   # optional
+
+# 3. Start the backend
+pip install -r requirements.txt
+python app.py
+```
+
+---
+
+## 🎯 Hackathon Track Alignment
+
+- **Track 2 · AMD Developer Cloud** *(primary)*: Real MI300X inference via ROCm/vLLM
+- **Track 1 · AI Agents**: 4-agent agentic workflow (Inspector → Diagnostician → Action → Reporter)
+- **Track 3 · Vision & Multimodal AI**: Qwen2-VL processing product images for industrial QC
+- **Qwen Challenge**: Qwen2-VL-7B-Instruct is the sole model powering all four agents end-to-end