Create README.md

You are building the Quantum Environmental Intelligence System (QEIS).

Purpose:
Develop an AI-driven system that fuses quantum-inspired simulation, environmental data sensing, and psychophysiological feedback to map and optimize the unseen patterns of human–environment coherence. The system combines real-world sensor input, AR visualization, and quantum-mechanical analogs for predictive modeling and environmental intelligence.

Core Objectives:
- Capture environmental and biometric data in real time.
- Simulate probability and coherence fields using quantum-inspired algorithms.
- Visualize AR overlays showing energetic or environmental flow.
- Generate actionable insights for sustainability, design, and wellness.

System Architecture:
1. Mobile Layer:
- React Native with ARCore/ARKit for AR environment rendering.
- TensorFlow Lite models for local predictions and biofeedback analysis.
- Sensor API integration (camera, GPS, EMF, accelerometer, microphone, EEG).

2. Backend:
- Node.js + Express + WebSocket for live data streaming.
- REST + MQTT endpoints for IoT and bio-sensor input.
- PostgreSQL (user + metadata) + InfluxDB (time-series environment data).
- Redis or Kafka for event streaming and message queues.

3. Cloud AI Pipeline:
- Vertex AI for large-scale model training and deployment.
- TensorFlow and PyTorch pipelines for pattern recognition.
- AutoML for coherence mapping and anomaly detection.
- Synthetic Data Generator (Python + NumPy + Faker) to simulate diverse sensor environments.

4. Visualization Layer:
- Next.js frontend deployed on Vercel.
- Three.js + WebGL quantum-field rendering.
- Real-time “coherence map” overlay using probabilistic color gradients.
- Optional neural interface visualization (EEG, HRV, or GSR streams).

API Schema:
- POST /data/sensor
→ { device_id, timestamp, type, value, geo }
- GET /map/coherence
→ returns AR field JSON grid + vector overlays
- GET /forecast/anomaly
→ predictive anomaly report
- WS /stream
→ live stream for visualization

Database Structure:
- users(id, name, auth_id, permissions)
- devices(id, user_id, type, last_seen)
- sensor_data(id, device_id, sensor_type, value, timestamp, geo)
- coherence_map(id, timestamp, grid_data, model_version)

MQTT Topics:
- qeis/sensors/[device_id]
- qeis/predict/anomaly
- qeis/visual/field

WebSocket Message Example:
{
"type": "coherence_update",
"payload": {
"region": "local",
"coherence_index": 0.87,
"timestamp": "2025-11-05T12:00:00Z"
}
}

Machine Learning Tasks:
- Train environmental coherence models via Vertex AI.
- Convert TensorFlow models to TFLite for mobile inference.
- Use synthetic data generator to augment training dataset.
- Apply unsupervised clustering for quantum-like entanglement mapping.

TF Lite Conversion Script:
python
import tensorflow as tf
model = tf.keras.models.load_model('model.h5')
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("model.tflite", "wb").write(tflite_model)

Terraform Template (GCP):
terraform
provider "google" {
project = "qeis-project"
region = "us-central1"
}
resource "google_storage_bucket" "qeis_data" {
name = "qeis-data-bucket"
location = "US"
}
resource "google_vertex_ai_endpoint" "qeis" {
display_name = "qeis-endpoint"
}

GitHub Actions (CI/CD):
yaml
name: Deploy QEIS
on: [push]
jobs:
build-deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- run: npm install && npm run build
- run: vercel deploy --prod

Sprint Plan (8 Weeks):
Week 1–2: Backend scaffolding, API routes, DB setup
Week 3–4: Frontend UI, AR rendering, WebSocket integration
Week 5–6: ML pipeline, Vertex AI model training
Week 7: TFLite integration + mobile tests
Week 8: Cloud deployment + system test

Immediate Setup Commands:
# Clone and initialize repo
git clone https://github.com/yourname/QEIS.git
cd QEIS
npm install
vercel link
firebase init
gcloud init
terraform init && terraform apply

Output:
Fully operational, AI-powered, AR-integrated quantum environmental intelligence platform ready for GitHub deployment, Google Vertex AI integration, and open interoperability with other edge and cloud services.

README.md

@@ -0,0 +1,218 @@
+---
+license: apache-2.0
+datasets:
+- nvidia/PhysicalAI-Autonomous-Vehicles
+- HuggingFaceFW/finewiki
+- fka/awesome-chatgpt-prompts
+- Agent-Ark/Toucan-1.5M
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- Phr00t/Qwen-Image-Edit-Rapid-AIO
+- deepseek-ai/DeepSeek-OCR
+- deepseek-ai/DeepSeek-V3.2-Exp
+- PaddlePaddle/PaddleOCR-VL
+- MiniMaxAI/MiniMax-M2
+new_version: deepseek-ai/DeepSeek-OCR
+library_name: adapter-transformers
+tags:
+- agent
+- code
+- not-for-all-audiences
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]