Update README.md

README.md

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----
-title: ClarityOps Augmented Decision AI
-emoji: 🩺
-colorFrom: blue
-colorTo: gray
-sdk: docker
-app_port: 7860
-pinned: false
-license: other
----
-
-# Medical Decision Support AI
-
-A Hugging Face Space providing medical decision support through an AI-powered chatbot interface.
-
-## System Information
-- Current User: Raj-VedAI
-- Timestamps: All times displayed in UTC (YYYY-MM-DD HH:MM:SS format)
-
-## Features
-- Real-time medical decision support
-- UTC timestamp display
-- User identification
-- Medical knowledge base access
-- Interactive chat interface
-
-## Usage Guidelines
-1. Enter your medical query in the chat input
-2. Review the AI-generated response along with timestamp
-3. Use example queries for guidance
-4. Note: This is a support tool and should not replace professional medical advice
-
-## Technical Details
-- Built with Gradio
-- Real-time UTC timestamp display
-- Secure user identification
-- Comprehensive medical knowledge base integration
-
-## Disclaimer
-This AI assistant is for informational purposes only and should not be used as a substitute for professional medical advice, diagnosis, or treatment.
+# Clarity Ops — Two-Phase Medical Analytics Engine
+
+This repo implements a universal, scenario-agnostic medical analytics workflow:
+
+1) **Phase 1: Clarification Questions** (<=5, grouped by category)
+2) **Phase 2: Structured Analysis** (schema-validated, unit-checked, math-verified, policy-aligned)
+
+The engine never invents data. If inputs are missing/ambiguous, it asks for clarifications first.
+
+## Quick start
+
+1. Populate a scenario in `/packs/<scenario>/` (see `/packs/mdsi` as an example).
+2. (Optional) Put known answers to clarifications in `clarifications.json`.
+3. Run the pipeline (pseudo-call in your orchestration):
+   - `run_two_phase.run_clarityops("packs/mdsi")`
+4. Review the final JSON. It passes:
+   - JSON Schema validation
+   - Unit/range checks
+   - Math consistency
+   - Policy/constraints adherence
+5. (Optional) Compare against `/packs/<scenario>/expected.json` with the rule-based grader.
+
+## Folder overview
+- `/core`: Global medical rules (units, ranges, privacy)
+- `/prompts`: System + two-phase user template
+- `/schemas`: Output schema for Phase 2
+- `/validators`: Hard guardrails (schema/units/math/policy)
+- `/graders`: Rule-based grader for gold answers
+- `/pipeline`: Two-phase orchestration
+- `/packs/<scenario>`: Scenario Pack (inputs, constraints, schema selection, rubric, expected)
+
+## Notes
+- This repo shows reference Python code for validators and pipeline. Hook the prompts into your LLM runner of choice.
+- All numbers are examples unless your scenario pack provides them.
+