Update README.md

README.md

@@ -1,421 +1,85 @@
-<div align="center">
-
-<img src="https://img.shields.io/badge/AMD_Instinct-MI300X-ED1C24?style=for-the-badge&logo=amd&logoColor=white" />
-<img src="https://img.shields.io/badge/ROCm-Stack-ED1C24?style=for-the-badge&logo=amd&logoColor=white" />
-<img src="https://img.shields.io/badge/vLLM-Inference-6D28D9?style=for-the-badge" />
-<img src="https://img.shields.io/badge/Qwen-Multimodal-0EA5E9?style=for-the-badge" />
-<img src="https://img.shields.io/badge/FastAPI-0.115-009688?style=for-the-badge&logo=fastapi&logoColor=white" />
-<img src="https://img.shields.io/badge/Python-3.12+-3776AB?style=for-the-badge&logo=python&logoColor=white" />
-
-<br /><br />
-
-# 🏥 MediAgent
-
-### Autonomous Multi-Agent Medical Imaging Analysis System
-
-**Five specialized AI agents. One radiological verdict. Running entirely on AMD.**
-
-*AMD Developer Hackathon 2026 · Track: Vision & Multimodal AI*
-
-<br />
-
-> Built by **Ramyar** — Security researcher & full-stack developer, Sulaymaniyah, Iraq
-
-</div>
-
 ---
-
-## What Is MediAgent?
-
-MediAgent is a production-grade autonomous AI system that analyzes medical images — X-rays, MRI scans, CT scans — through a five-agent pipeline and generates structured, peer-reviewed clinical radiology reports in real time.
-
-Upload an image. Watch five AI agents execute live. Get a formal radiology report with differential diagnoses, ICD-10 codes, a quality score, and a FHIR R4 export ready for any EMR system.
-
-**No cloud APIs. No OpenAI. No Nvidia.**
-Pure AMD MI300X inference. Local. Private. Fast.
-
+license: mit
+language:
+- en
+base_model:
+- Qwen/Qwen3.6-35B-A3B
+- Qwen/Qwen3.6-27B
+pipeline_tag: image-to-text
+tags:
+- medical
 ---
 
-## The Pipeline
-
-```
-┌─────────────────────────────────────────────────────────────────────┐
-│                        IMAGE UPLOAD                                 │
-│              PNG / JPG / DICOM (.dcm) — up to 20 MB                │
-└──────────────────────────┬──────────────────────────────────────────┘
-                           │
-          ┌────────────────┴────────────────┐
-          │         PARALLEL STAGE          │
-          ▼                                 ▼
-┌─────────────────┐               ┌─────────────────┐
-│  INTAKE AGENT   │               │  VISION AGENT   │
-│                 │               │                 │
-│ • Validates     │               │ • Multimodal    │
-│   image payload │               │   Qwen analysis │
-│ • Normalizes    │               │ • Anatomical    │
-│   clinical text │               │   findings      │
-│ • Extracts      │               │ • Severity per  │
-│   demographics  │               │   region        │
-│ • Safety triage │               │ • Confidence    │
-│   (16 keywords) │               │   scoring       │
-│ • Modality hint │               │ • Anomaly flags │
-└────────┬────────┘               └────────┬────────┘
-         └──────────────┬──────────────────┘
-                        │
-                        ▼
-            ┌───────────────────────┐
-            │    RESEARCH AGENT     │
-            │                       │
-            │ • KB cross-reference  │
-            │   (15 conditions)     │
-            │ • Demographic weight  │
-            │ • Ranked differentials│
-            │ • ICD-10 codes        │
-            │ • Match probabilities │
-            └───────────┬───────────┘
-                        │
-                        ▼
-            ┌───────────────────────┐
-            │     REPORT AGENT      │
-            │                       │
-            │ • ACR/NICE format     │
-            │ • Clinical history    │
-            │ • Technique section   │
-            │ • Findings narrative  │
-            │ • Impression + top Dx │
-            │ • Recommendations     │
-            └───────────┬───────────┘
-                        │
-                        ▼
-            ┌───────────────────────┐
-            │     CRITIC AGENT      │
-            │                       │
-            │ • Cross-validates     │
-            │   report vs findings  │
-            │ • Quality score 0-100 │
-            │ • Uncertainty flags   │
-            │ • Disclaimer enforce  │
-            └───────────┬───────────┘
-                        │
-                        ▼
-┌─────────────────────────────────────────────────────────────────────┐
-│                      FINAL REPORT                                   │
-│         Structured JSON · PDF Export · FHIR R4 DiagnosticReport    │
-└─────────────────────────────────────────────────────────────────────┘
-```
-
-INTAKE and VISION execute **concurrently** — cutting wall-clock latency by running the two most expensive operations in parallel. Everything downstream sequences after both complete.
-
----
-
-## AMD Hardware Stack
-
-| Component | Technology |
-|---|---|
-| **GPU** | AMD Instinct MI300X |
-| **GPU Software** | ROCm — AMD's open-source GPU compute platform |
-| **Inference Server** | vLLM (ROCm build) at `localhost:8000/v1` |
-| **Model** | Qwen multimodal — native vision + text |
-| **Backend** | FastAPI 0.115 + Uvicorn |
-| **Frontend** | Vanilla JS + Tailwind CSS + SSE streaming |
-
-This project is a direct proof of concept that AMD's ROCm stack is **production-viable for real-world medical AI**. Every inference call — vision analysis, clinical normalization, report synthesis, peer review, post-report chat — runs on AMD MI300X. Zero CUDA dependency. Zero cloud API calls.
-
----
-
-## Key Features
-
-### 🔴 Real-Time SSE Streaming
-Watch the pipeline execute live, agent by agent. Every status transition — WAITING → RUNNING → DONE — streams to the dashboard as it happens via Server-Sent Events. Per-agent runtime counters track exactly how long each step takes.
-
-### 👁️ Multimodal Vision Analysis
-Qwen processes the raw medical image natively. It returns structured JSON: detected modality, technical quality assessment, per-region findings with anatomical names, radiological descriptions, severity levels (NORMAL / INCIDENTAL / SIGNIFICANT / CRITICAL), confidence scores (0–100), and anomaly flags.
-
-### 🔬 Medical Knowledge Base + ICD-10 Mapping
-The Research Agent cross-references vision findings against 15 curated clinical conditions spanning pulmonary, neurological, abdominal, musculoskeletal, and vascular pathology. Every differential diagnosis comes with an ICD-10 code, match probability, and a sentence explaining exactly why the condition matches the findings.
-
-### 🛡️ Critic Agent QA
-Every report goes through a peer-review pass before delivery. The Critic checks that all anomalies from the Vision Agent appear in the report, flags low-confidence findings, assigns a quality score (completeness 30% + accuracy 40% + safety 20% + compliance 10%), and hard-caps the score at 40/100 if a core agent failed.
-
-### 🏥 DICOM Support
-Upload real `.dcm` files. MediAgent extracts 20+ metadata fields — patient name, study date, institution, modality, body part, KVP, slice thickness, pixel spacing, image dimensions — and pre-populates the intake form automatically. MONOCHROME1 inversion and multi-frame handling included.
-
-### 📋 FHIR R4 Export
-Every report can be exported as a fully conformant HL7 FHIR R4 DiagnosticReport resource. Includes an inline Patient resource, Observation resources, LOINC and SNOMED CT codes, severity mapping, full report text in `presentedForm`, and custom extensions for AI quality score and pipeline status. Ready to import into Epic, Cerner, or any FHIR-capable EMR.
-
-### 💬 Post-Report Clinical Chat
-After the report is delivered, a ClinicalAdvisorAgent is available for follow-up questions. It answers in 2–4 sentences with direct reference to the report findings. Qwen's thinking/reasoning mode is explicitly disabled — answers are fast, direct, and clinical.
-
-### 🔒 Hard Safety Enforcement
-- **16 deterministic safety keywords** — chest pain, stroke symptoms, acute trauma, hemoptysis, sepsis, spinal trauma, and more — trigger urgent flags regardless of LLM output.
-- **Age-based alerts** — pediatric (<18) and geriatric (>75) cases are automatically flagged for expert review.
-- **Mandatory AI disclaimer** — enforced at two independent layers (Report Agent + Critic Agent) and cannot be bypassed or modified by the LLM.
-- **Graceful degradation** — the pipeline produces a report even if individual agents fail, always marking what succeeded and what didn't.
-
-### 📄 Client-Side PDF Export
-Full radiology report exported as a formatted PDF directly in the browser using jsPDF — severity color banner, all six report sections, DICOM metadata, QA score. No server round-trip needed.
-
----
-
-## Agent Architecture
-
-### IntakeAgent
-Validates the image payload (minimum size, valid base64), applies deterministic safety triage, and normalizes clinical language. For simple inputs under 120 characters it skips the LLM entirely and uses a built-in layman-to-medical term map (22 entries: "can't breathe" → "dyspnea", "lump" → "mass/nodule", "dizzy" → "dizziness/vertigo", etc.). Only calls the LLM for complex clinical narratives with comorbidities or medical history. Falls back cleanly to raw input preservation if the LLM is unavailable.
-
-### VisionAgent
-Sends the base64 image and clinical context to Qwen at temperature 0.0 with a strict JSON schema enforced via system prompt. Handles malformed enum values from the LLM with safe conversion fallbacks — a single bad field never drops a finding. Tracks token usage and anomaly counts in the output metadata.
-
-### ResearchAgent
-Pre-filters the knowledge base to only conditions compatible with the detected modality before sending to the LLM — reducing prompt size and improving accuracy. Enforces strict output rules: only conditions from the KB, 2–4 differentials maximum, 5% minimum probability, exact ICD-10 codes, and evidence sentences that actually explain the match.
-
-### ReportAgent
-Builds a structured prompt with clearly labeled sections — clinical history, imaging technique, findings block, differentials block — and asks the LLM to synthesize them into a formal ACR/NICE radiology report. The disclaimer is overwritten to the exact regulatory string after LLM generation, unconditionally.
-
-### CriticAgent
-Operates at temperature 0.0 for fully deterministic QA. Receives the draft report and the full pipeline state including raw vision findings. Checks every anomaly is accounted for, flags low-confidence observations, and appends a `[QUALITY ASSESSMENT]` block to the recommendations section with score, issues, and uncertainty warnings.
+https://cdn-uploads.huggingface.co/production/uploads/69e8826eb1347b4a2120bea7/-WekpB77IqmwChejTUzxP.mp4
 
-### ClinicalAdvisorAgent
-Activated only after report delivery, scoped to the specific report's findings. Strips all Qwen thinking output via multi-layer regex before returning the answer — handles `<think>` XML blocks, markdown think fences, and plain-text reasoning preambles.
-
----
-
-## LLM Client
-
-The `LLMClient` wraps the OpenAI Python SDK pointed at the local vLLM endpoint. It handles:
-
-- Text completions with optional JSON mode enforcement
-- Multimodal completions with base64 image injection
-- Token-level streaming with an `on_token` callback
-- 3-attempt retry loop with 1-second flat backoff
-- 90-second timeout per call
-- Dual-strategy JSON extraction: direct parse first, then character-by-character brace-matching fallback for responses where the LLM adds conversational padding
-
----
-
-## Medical Knowledge Base
-
-15 conditions covering the most common radiological findings across all supported modalities:
-
-| Condition | ICD-10 | Modalities | Severity |
-|---|---|---|---|
-| Community-Acquired Pneumonia | J18.9 | X-RAY, CT | SIGNIFICANT |
-| Cardiogenic Pulmonary Edema | J81.0 | X-RAY, CT | CRITICAL |
-| Pleural Effusion | J90 | X-RAY, CT, MRI | SIGNIFICANT |
-| Spontaneous Pneumothorax | J93.9 | X-RAY, CT | CRITICAL |
-| Intracerebral Hemorrhage | I61.9 | CT, MRI | CRITICAL |
-| Ischemic Stroke | I63.9 | CT, MRI | CRITICAL |
-| Intracranial Neoplasm | C71.9 | MRI, CT | SIGNIFICANT |
-| Abdominal Aortic Aneurysm | I71.4 | CT, MRI | CRITICAL |
-| Nephrolithiasis | N20.0 | CT, X-RAY | SIGNIFICANT |
-| Small Bowel Obstruction | K56.6 | X-RAY, CT | SIGNIFICANT |
-| Long Bone Fracture | S82.902 | X-RAY, CT | SIGNIFICANT |
-| Degenerative Joint Disease | M19.90 | X-RAY, MRI | INCIDENTAL |
-| Hepatic Steatosis | K76.0 | CT, MRI | INCIDENTAL |
-| Herniated Disc | M51.16 | MRI, CT | SIGNIFICANT |
-| Pulmonary Nodule | R91.1 | X-RAY, CT | SIGNIFICANT |
-
----
-
-## API Reference
-
-| Method | Endpoint | Description |
-|---|---|---|
-| `GET` | `/` | Clinical dashboard UI |
-| `GET` | `/health` | System health, version, active sessions |
-| `GET` | `/metrics/gpu` | Live AMD GPU metrics (util, VRAM, temp, power) |
-| `POST` | `/analyze` | Synchronous pipeline → full JSON report |
-| `POST` | `/analyze/stream` | Real-time SSE streaming pipeline |
-| `GET` | `/status/{report_id}` | Poll live pipeline state |
-| `POST` | `/chat/{report_id}` | Post-report clinical Q&A |
-| `GET` | `/api/docs` | Swagger UI |
-| `GET` | `/api/redoc` | ReDoc UI |
-
-### `/analyze/stream` — SSE Event Types
-
-```json
-// Agent status update (emitted on every state transition)
-{"agent": "VISION", "status": "RUNNING"}
-{"agent": "VISION", "status": "DONE"}
-
-// Final report (emitted when pipeline completes)
-{"type": "report", "data": {...}, "report_id": "REP-A3F9C2D1B4E7"}
-
-// Error
-{"type": "error", "message": "Pipeline produced no report"}
-```
-
-### Form Fields (`/analyze`, `/analyze/stream`)
+# 🏥 MediAgent
+### Autonomous Multi-Agent Medical Imaging Analysis — AMD Instinct MI300X
 
-| Field | Type | Required | Notes |
-|---|---|---|---|
-| `image` | File | ✅ | PNG, JPG, or DICOM (.dcm), max 20 MB |
-| `symptoms` | string | — | Free-text chief complaint |
-| `age` | integer | — | 0–120 |
-| `sex` | string | — | `M`, `F`, or `O` |
-| `clinical_context` | string | — | Medical history, referral details |
+> **AMD Developer Hackathon 2026 · Vision & Multimodal AI Track**
+> Built by Ramyar — Sulaymaniyah, Iraq
 
 ---
 
-## Data Models
-
-```
-PatientInput
-    └── image_base64, symptoms, age, sex, clinical_context
-
-PipelineState
-    ├── agent_statuses: {INTAKE, VISION, RESEARCH, REPORT, CRITIC}
-    ├── intake_output: IntakeOutput
-    ├── vision_output: VisionOutput
-    │       └── findings: [VisionFinding, ...]
-    │               └── anatomical_region, description, severity,
-    │                   confidence, confidence_score, is_anomaly
-    ├── research_output: ResearchOutput
-    │       └── differential_diagnoses: [KnowledgeMatch, ...]
-    │               └── condition_name, match_probability,
-    │                   supporting_evidence, differential_rank, icd10_code
-    ├── report_draft: ReportSection
-    │       └── clinical_history, technique, findings, impression,
-    │           recommendations, disclaimer
-    └── final_report: FinalReport
-            └── report_id, patient_metadata, sections, vision_summary,
-                research_summary, overall_severity, agent_pipeline_status,
-                generation_timestamp
-```
+## What It Does
 
----
-
-## Project Structure
+MediAgent runs a 5-agent AI pipeline that analyzes medical images (X-ray, MRI, CT, DICOM) and generates formal radiology reports with differential diagnoses, ICD-10 codes, and FHIR R4 export — entirely on AMD hardware.
 
-```
-mediagent/
-├── main.py                  ← FastAPI server, all routes, SSE orchestration
-├── core/
-│   ├── llm.py               ← LLM client (retry, vision, streaming, JSON extraction)
-│   ├── models.py            ← All Pydantic v2 data models
-│   ├── pipeline.py          ← Parallel pipeline orchestrator
-│   ├── dicom.py             ← DICOM parser (pydicom + numpy + Pillow)
-│   └── fhir.py              ← FHIR R4 DiagnosticReport builder
-├── agents/
-│   ├── intake.py            ← Input validation, normalization, safety triage
-│   ├── vision.py            ← Multimodal image analysis
-│   ├── research.py          ← KB matching, ICD-10, differential diagnosis
-│   ├── report.py            ← ACR/NICE radiology report synthesis
-│   ├── critic.py            ← QA validation, quality scoring
-│   └── advisor.py           ← Post-report clinical Q&A
-├── static/
-│   └── index.html           ← Full dashboard (Tailwind + Chart.js + SSE)
-├── requirements.txt
-└── .env.example
-```
+**No cloud APIs. No OpenAI. No Nvidia. Pure AMD MI300X + ROCm + vLLM.**
 
 ---
 
-## Getting Started
-
-### Prerequisites
-
-- Python 3.12+
-- vLLM running a Qwen multimodal model on ROCm, accessible at `http://localhost:8000/v1`
-- ROCm-compatible AMD GPU (MI300X recommended)
-
-### Installation
-
-```bash
-# Clone the repository
-git clone https://github.com/Ramyar2007/mediagent
-cd mediagent
-
-# Install Python dependencies
-pip install -r requirements.txt
-
-# Configure environment
-cp .env.example .env
-# Edit .env and set LLM_BASE_URL to your vLLM endpoint
-```
-
-### Environment Variables
-
-```env
-LLM_BASE_URL=http://localhost:8000/v1   # vLLM OpenAI-compatible endpoint
-LLM_MODEL=/model                         # Model path served by vLLM
-APP_PORT=8090                            # Server port
-```
-
-### Run
+## ⚠️ Demo Mode
 
-```bash
-python main.py
-```
+This Space runs in **demo mode** — the full pipeline UI works and all 5 agents animate live, but no real inference is performed since the AMD Instinct MI300X backend is not available on HuggingFace's free hardware.
 
-Dashboard available at **http://localhost:8090**
+**See the video demo for live inference on real AMD hardware.**
 
-Swagger docs at **http://localhost:8090/api/docs**
+Live inference requires: AMD Instinct MI300X · ROCm · vLLM · Qwen multimodal
 
 ---
 
-## Dependencies
+## The 5-Agent Pipeline
 
-| Package | Version | Purpose |
-|---|---|---|
-| `fastapi` | 0.115.6 | Web framework |
-| `uvicorn[standard]` | 0.34.0 | ASGI server |
-| `openai` | 1.58.1 | SDK for vLLM OpenAI-compatible API |
-| `python-multipart` | 0.0.20 | Multipart form / file upload |
-| `pydantic` | 2.10.5 | Data validation and serialization |
-| `Pillow` | 11.1.0 | Image processing for DICOM conversion |
-| `pydicom` | 2.4.4 | DICOM file parsing and metadata extraction |
-| `numpy` | 1.26.4 | Pixel array normalization for DICOM |
+| Agent | Role |
+|---|---|
+| **INTAKE** | Validates input, normalizes clinical language, safety triage |
+| **VISION** | Multimodal image analysis via Qwen on AMD MI300X |
+| **RESEARCH** | KB cross-reference, differential diagnoses, ICD-10 codes |
+| **REPORT** | ACR/NICE format radiology report synthesis |
+| **CRITIC** | QA peer-review, quality scoring, disclaimer enforcement |
 
-Optional: `amdsmi` Python library — used automatically when available for more accurate GPU metrics than the `rocm-smi` CLI fallback.
+INTAKE + VISION run in **parallel** to minimize latency.
 
 ---
 
-## Clinical Safety
-
-MediAgent is built with clinical safety as a first-class concern, not an afterthought.
-
-**Mandatory disclaimer** — enforced at two independent code layers and cannot be overridden by any LLM output:
-
-> *"This analysis is AI-generated and must be reviewed by a licensed radiologist before any clinical decisions are made."*
-
-**Hard safety rules that run deterministically, without LLM involvement:**
-- 16 urgent clinical keywords trigger immediate flags before any AI processing
-- Pediatric and geriatric age thresholds auto-flag for specialist review
-- Quality score is hard-capped at 40/100 if core agents (Vision, Report) fail
-- Low-confidence findings are always flagged with confirmatory imaging recommendations
-- The disclaimer is re-enforced after every LLM call, unconditionally
+## Key Features
 
-**This system is a decision support tool, not a clinical decision maker.** Every output is intended to assist, not replace, a licensed radiologist.
+- Real-time SSE streaming pipeline with per-agent timers
+- DICOM (.dcm) file support with metadata extraction
+- 15-condition medical knowledge base with ICD-10 mapping
+- FHIR R4 DiagnosticReport export
+- Client-side PDF export
+- Post-report clinical Q&A (ClinicalAdvisorAgent)
+- Live AMD GPU metrics (util, VRAM, temp, power)
+- Hard-enforced clinical safety rules
 
 ---
 
-## Dashboard Preview
+## Tech Stack
 
-The single-page clinical dashboard provides:
-
-- **Live pipeline panel** — real-time agent status cards with per-step runtime counters
-- **Analytics tab** — severity distribution donut chart, differential diagnosis confidence bar chart, agent timing bar chart — all populated from structured model output
-- **Report panel** — severity banner, safety flags, all six report sections, finding cards color-coded by severity
-- **DICOM metadata card** — study date, institution, modality, body part, technical parameters
-- **PDF export** — full formatted report generated client-side
-- **Clinical chat** — slide-up Q&A panel backed by the ClinicalAdvisorAgent
-- **AMD GPU panel** — live util %, VRAM used/total, temperature, power draw — polling every 3 seconds
+- **GPU:** AMD Instinct MI300X
+- **GPU Software:** ROCm
+- **Inference:** vLLM (ROCm build) + Qwen multimodal
+- **Backend:** FastAPI + Uvicorn
+- **Frontend:** Vanilla JS + Tailwind CSS + Chart.js + SSE
 
 ---
 
-## Built For
-
-**AMD Developer Hackathon 2026**
-Track: Vision & Multimodal AI
+## GitHub
 
-This project demonstrates that AMD's ROCm ecosystem is a complete, production-viable alternative for serious AI workloads. Medical imaging analysis — with real multimodal vision, structured clinical reasoning, and standards-compliant output — running fully on AMD MI300X without a single NVIDIA or cloud dependency.
+Full source code, architecture docs, and README:
+**https://github.com/Ramyar2007/mediagent**
 
 ---
 
-<div align="center">
-
-**Built by Ramyar · Sulaymaniyah, Iraq**
-
-*#AMDDevChallenge · AMD Instinct MI300X · ROCm · vLLM · Qwen*
-
-</div>
+*This system is a decision support tool. All outputs must be reviewed by a licensed radiologist before any clinical decisions are made.*