Update README.md

README.md

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-Interactive app that selects a transport wing from a candidate set, renders PNG + interactive 3D + STL,
-validates from an optional polar, and adds an LLM explanation.
+# ✈️ Transport Wing Selector — AI-Assisted Wing Design
+
+An **AI-driven design tool** that automates 3D wing generation from a 2D airfoil and optional polar data.  
+The system evaluates 160 candidate wings using a trained **multilayer perceptron (MLP)** selector model and returns the best geometry based on your chosen aerodynamic objective:
+- **min_cd** – minimize drag  
+- **max_cl** – maximize lift  
+- **max_ld** – maximize lift-to-drag ratio  
+
+It converts a traditionally slow, intuition-based process into a transparent, interpretable, and reproducible workflow—delivering optimal wing geometries in seconds.
+
+---
+
+## 🚀 What This App Does
+- **Generates** candidate 3D wing geometries from a user-provided 2D airfoil (.dat/.txt)  
+- **Scores and ranks** each design across lift, drag, and efficiency objectives  
+- **Selects the top candidate**, visualizing it as:
+  - A **static PNG** rendering  
+  - An **interactive 3D model** (Plotly viewer)  
+  - A downloadable **CAD-ready STL file**  
+  - A structured **JSON summary** of aerodynamic and geometric parameters  
+- **Validates** the best wing using strip-theory sweep plots (lift, drag, efficiency)  
+- **Explains** results through a grounded **LLM summary** comparing the top design with close alternatives
+
+---
+
+## 🧠 How It Works
+1. **Input:** Upload your airfoil geometry (and optional polar data).  
+2. **Objective:** Choose your design target (min_cd, max_cl, or max_ld).  
+3. **Candidate Generation:**  
+   The MLP model evaluates 160 deterministic wing geometries parameterized by span, taper, twist, and chord vectors.  
+4. **Selection & Visualization:**  
+   The top-scoring wing is rendered as an interactive 3D mesh and validated with strip-theory sweeps.  
+5. **Explanation:**  
+   A lightweight language model summarizes why this candidate performs best, grounding the response in numerical features like span, taper, aspect ratio, and score.  
+   Example:  
+   > “The chosen wing achieves high efficiency by combining a long span, moderate taper, and strong negative washout, lowering induced drag while maintaining lift.”
+
+---
+
+## 💡 Why It Matters
+Traditional wing design can take **hours per concept**, requiring manual setup, XFOIL/QBlade runs, and iterative CFD.  
+This app reduces that to **under one minute**, enabling:
+- Faster concept iteration  
+- Reproducible, data-driven decisions  
+- Transparent model reasoning through structured explanations  
+- Easy export for further CFD or CAD analysis  
+
+The workflow empowers engineers, students, and researchers to rapidly explore the early design space while maintaining aerodynamic interpretability.
+
+---
+
+## 🧩 Features
+| Feature | Description |
+|----------|-------------|
+| **Objective Selection** | min_cd / max_cl / max_ld |
+| **Deterministic Mode** | Reproducible results with identical geometry and scoring |
+| **Top-k Comparison** | View multiple top candidates ranked by score |
+| **Validation Sweep** | Strip-theory verification for lift/drag trends |
+| **Exports** | `.png`, `.stl`, `.json` artifacts |
+| **Grounded LLM Explanation** | Physics-based summary of the chosen design |
+
+---
+
+## 🧭 How to Use
+1. Upload your **airfoil** file (`.dat` or `.txt`) and optionally a **polar** file.  
+2. Choose an optimization **objective**.  
+3. Adjust parameters (Top-k slider, deterministic mode, AoA sweep).  
+4. Click **“Find Best Wing.”**  
+5. View results, export files, and read the **AI-generated design explanation.**
+
+---
+
+## ⚙️ System Overview
+- **Selector Model:** MLP trained on 500+ generated wings, each labeled with aerodynamic metrics (CL, CD, L/D)  
+- **Validation:** Deterministic scoring and strip-theory consistency checks  
+- **LLM Wrapper:** Qwen2.5-1.5B-Instruct, constrained via structured JSON prompt to prevent hallucination  
+- **Deployment:** Gradio app on Hugging Face Spaces with STL/JSON export pipeline  
+
+---
+
+## 📚 References & Credits
+Developed by **Emily Copus** and **Kevin Kyi**  
+*Carnegie Mellon University — 24-679 Designing and Deploying AI/ML Systems*  
+Instructor: **Dr. Chris McComb**
+
+- [Transport Wing Selector Space](https://huggingface.co/spaces/kevinkyi/Project1_Airfoil_Interface)  
+- [ViewSTL](https://www.viewstl.com/) – for external STL visualization  
+- [3D Viewer.net](https://3dviewer.net/) – quick in-browser CAD preview  
+- [Introduction to Aerospace Flight Vehicles — ERAU](https://eaglepubs.erau.edu/introductiontoaerospaceflightvehicles/chapter/wing-shapes-and-nomenclature/)
+
+---
+
+## 🧾 License & Notes
+Educational demonstration.  
+Not validated for production or certification use.  
+Outputs and explanations are deterministic, data-grounded, and intended to accelerate conceptual design only.
+