Update README.md

README.md

@@ -3,32 +3,41 @@ license: mit
 tags:
 - aerospace
 - physics-informed-neural-network
-- pytorch
 - pinn
+- pytorch
 - surrogate-model
+- alrithm
 library_name: pytorch
 ---
 
 # 🚀 ALRITHM-Astro
 
-**ALRITHM-Astro** is an AI-powered Aerospace Engineering Copilot designed to simulate rocket flight dynamics and predict Maximum Dynamic Pressure (Max Q). 
+**ALRITHM-Astro** is an AI-powered Aerospace Engineering Copilot designed to simulate rocket flight dynamics and predict Maximum Dynamic Pressure (Max Q). It combines a custom-trained Physics-Informed Neural Network (PINN) with a natural language interface.
 
-🔗 **[Try the Live Demo Here](https://godela-web.vercel.app)** *(Hãy thay bằng link Vercel thực tế của bạn)*
+🔗 **[Try the Live Demo Here](https://godela-web.vercel.app)** *(Hãy nhớ thay bằng link Vercel thực tế của bạn)*
 
 ## 🏛️ Powered by Alrithm
 This project is fundamentally built upon and inspired by the vision of **[Alrithm](https://alrithmapi.vercel.app)**. 
 
-- **Data Source:** The machine learning models in this project were fine-tuned exclusively on the **`alr-aerospace-platform-0.2`** dataset, fetched via the official Alrithm API.
+- **Data Source:** The machine learning models in this project were fine-tuned exclusively on the **`alr-aerospace-platform-0.2`** dataset, fetched via the official Alrithm API (`https://alrithmapi.vercel.app/api/v1/models`).
 - **Philosophy:** Alrithm provides rigorously verified, high-fidelity aerospace data. ALRITHM-Astro serves as a demonstration of how developers can leverage Alrithm's verified data streams to build real-time, physics-informed AI applications.
 
+## ✨ Features
+- **Natural Language Processing:** Understands complex aerospace queries using Groq's LLMs (`openai/gpt-oss-120b`).
+- **Physics-Informed Prediction:** Calculates Max Q with high precision using a custom PyTorch Neural Network trained on Alrithm's verified aerospace data.
+- **Interactive 3D Visualization:** Renders the rocket's 3D trajectory in real-time using Plotly.js.
+- **Full-Stack Architecture:** FastAPI backend (Render) + Vanilla JS frontend (Vercel).
+
 ## 🧠 Model Details
-- **Architecture:** Multi-Layer Perceptron (MLP) with 256 hidden units.
+- **Architecture:** Multi-Layer Perceptron (MLP) with 256 hidden units and SiLU activation.
 - **Input Features:** 13 engineered physical features (e.g., Burnout Velocity, Thrust-to-Weight Ratio, Mass Ratio).
 - **Output:** Maximum Dynamic Pressure (`max_q_kpa`).
 - **Training:** 5-Fold Cross-Validation (Fine-tuned on Alrithm data).
 
 ## 🛠️ How to use this model
 
+You can load this model directly using PyTorch:
+
 ```python
 import torch
 import torch.nn as nn
@@ -45,8 +54,16 @@ class PureMLPModel_Deep(nn.Module):
     def forward(self, x):
         return self.net(x).squeeze(-1)
 
-# Load model
-checkpoint = torch.load("godela_surrogate.pth", map_location="cpu")
+# 1. Load the ALRITHM-Astro model weights
+checkpoint = torch.load("alrithm_astro_model.pth", map_location="cpu")
+
+# 2. Initialize the architecture and load weights
 model = PureMLPModel_Deep()
 model.load_state_dict(checkpoint['model_state_dict'])
-model.eval()
+model.eval()
+
+# 3. Retrieve the scalers for data normalization
+scaler_x = checkpoint['scaler_x']
+scaler_y = checkpoint['scaler_y']
+
+print("✅ ALRITHM-Astro model loaded successfully!")