R Notebook

Step 3: Upload the Training Dataset

Now, we will upload the 3D training dataset (3D_GPT_Training_Data.jsonl) to OpenAI for fine-tuning.

---

3.1 Download the JSONL Training Dataset

If you haven’t downloaded it yet, get it here: 📂 Download 3D_GPT_Training_Data.jsonl

Make sure the file is in your current working directory (where you will run the commands).

---

3.2 Validate the Dataset Format

Before uploading, ensure the JSONL file is correctly formatted. Run:

openai tools fine_tunes.prepare_data -f 3D_GPT_Training_Data.jsonl

If it returns:

[INFO] Validation successful. File is ready for fine-tuning.

You’re good to go!

---

3.3 Upload the Dataset to OpenAI

Now, upload the dataset to OpenAI:

openai api files.create -p fine-tune -f "3D_GPT_Training_Data.jsonl"

This will return a file ID like:

{
  "id": "file-XXXXXXXXXXXXXX",
  "object": "file",
  "bytes": 123456,
  "purpose": "fine-tune"
}

Copy the file ID, as you’ll need it for the next step.

---

3.4 Check Uploaded Files

To verify the upload:

openai api files.list

If your dataset appears in the list, the upload was successful.

---

Step 4: Fine-Tune GPT-4 on the 3D Dataset

Now that the dataset has been uploaded, we will fine-tune GPT-4 to learn from the 3D spatial structure data.

---

4.1 Start the Fine-Tuning Process

Run the following command, replacing file-XXXXXXXXXXXXXX with your uploaded dataset’s file ID:

openai api fine_tunes.create -t "file-XXXXXXXXXXXXXX" -m "gpt-4"

This will: - Start fine-tuning a GPT-4 model using your dataset. - Assign a unique fine-tune ID to track progress.

---

4.2 Monitor Training Progress

You can check the status of your fine-tuning job with:

openai api fine_tunes.list

or track a specific fine-tune job:

openai api fine_tunes.get -i "ft-XXXXXXXXXXXXXX"

(Replace ft-XXXXXXXXXXXXXX with your fine-tune ID.)

The training process may take several minutes to hours, depending on OpenAI’s queue and dataset size.

---

4.3 Retrieve the Fine-Tuned Model

Once training is complete, you’ll receive a fine-tuned model ID (e.g., "ft:gpt-4-XXXXXXXXXXXXXX").

To confirm that your model is ready:

openai api fine_tunes.results -i "ft-XXXXXXXXXXXXXX"

---

4.4 Use Your Fine-Tuned Model

After training, you can query your fine-tuned GPT model with:

openai api completions.create -m "ft:gpt-4-XXXXXXXXXXXXXX" -p "Describe this 3D quantum structure..."

Or, in Python:

import openai

openai.api_key = "your-api-key-here"

response = openai.ChatCompletion.create(
    model="ft:gpt-4-XXXXXXXXXXXXXX",
    messages=[{"role": "user", "content": "Describe this 3D spatial structure"}]
)

print(response["choices"][0]["message"]["content"])

---

Next Step

1. Run the fine-tuning command.
2. Wait for training to complete.

Step 5: Integrate Fine-Tuned GPT into Your Quantum GIS Framework

Now that your fine-tuned 3D-aware GPT is ready, let’s integrate it into your Quantum GIS system so it can: ✅ Analyze 3D spatial data
✅ Generate descriptions of quantum structures
✅ Enhance GIS workflows with AI-driven insights

---

5.1 Set Up Your Python Environment

If you haven’t already, install the required libraries:

pip install openai numpy pandas trimesh

This ensures you have everything needed to process 3D structures and interact with OpenAI.

---

5.2 Load Your Fine-Tuned GPT Model

Now, replace "ft:gpt-4-XXXXXXXXXXXXXX" with your actual fine-tuned model ID, and run:

import openai

openai.api_key = "your-api-key-here"

# Function to query the fine-tuned GPT model
def describe_3d_structure():
    response = openai.ChatCompletion.create(
        model="ft:gpt-4-XXXXXXXXXXXXXX",  # Replace with your fine-tuned model ID
        messages=[{"role": "user", "content": "Describe this 3D spatial structure"}]
    )
    return response["choices"][0]["message"]["content"]

# Get a GPT-generated description of a 3D structure
description = describe_3d_structure()
print("GPT-Generated Description of the 3D Structure:")
print(description)

This will query the model to generate a natural language description of your 3D quantum structure.

---

5.3 Connect GPT to GIS Data Processing

Now, let’s integrate it with Quantum GIS (QGIS) or ArcGIS. If you’re working with GIS software, modify the script to: - Load STL/Point Cloud/Voxel data. - Send the data to GPT for analysis. - Return AI-generated insights in your GIS environment.

Example: GPT-Powered GIS Pipeline

import openai
import geopandas as gpd

openai.api_key = "your-api-key-here"

# Load spatial data (example using GeoJSON or Shapefiles)
gis_data = gpd.read_file("your_3d_structure.geojson")

# Convert GIS data into GPT-friendly format (e.g., centroid, bounding box)
gis_features = {
    "centroid": gis_data.geometry.centroid[0],
    "bounding_box": gis_data.total_bounds
}

# Send GIS data to GPT for analysis
response = openai.ChatCompletion.create(
    model="ft:gpt-4-XXXXXXXXXXXXXX",
    messages=[{"role": "user", "content": f"Analyze this GIS structure: {gis_features}"}]
)

print("AI-GIS Analysis:")
print(response["choices"][0]["message"]["content"])

---

5.4 Deploy GPT Inside Your GIS Workflow

1. For ArcGIS Users → Use the Python script inside ArcPy Notebooks.
2. For QGIS Users → Integrate into QGIS Python Console.
3. For Web Apps → Connect to a web-based GIS tool via OpenAI’s API.

---

Final Steps

✅ Your GPT is now fine-tuned and integrated!
✅ It can analyze and describe 3D GIS and quantum structures.
✅ You can now automate 3D analysis inside your GIS pipeline.

---

next

1. Automate this process further? (e.g., batch processing 3D structures)
2. Integrate GPT into a Web App? (for live GIS analysis)
3. Optimize your model further? (improve responses)

Step 6: Automate 3D Structure Processing with GPT

Now that your Quantum GIS GPT is fine-tuned and integrated, let’s automate the batch processing of multiple 3D structures.

This automation will: ✅ Process multiple STL or point cloud files automatically
✅ Extract and convert features for GPT analysis
✅ Generate AI-driven insights for each structure in bulk

---

6.1 Organizing 3D Structure Files

Before we automate, place all your STL or 3D point cloud files inside a folder.
For example:
📂 D:/Capstone research/3D_Files/
- structure1.stl
- structure2.stl
- structure3.stl
- …

Step 8: Optimize Your Fine-Tuned GPT Model for Better 3D Analysis

Now, we will improve your GPT’s accuracy by:
✅ Refining GPT’s understanding of 3D structures
✅ Enhancing training data for better responses
✅ Fine-tuning again with additional datasets

---

8.1 Identify Weaknesses in the Model

Before optimizing, test your current fine-tuned GPT model by running:

import openai

openai.api_key = "your-api-key-here"

response = openai.ChatCompletion.create(
    model="ft:gpt-4-XXXXXXXXXXXXXX",  # Replace with your fine-tuned model ID
    messages=[{"role": "user", "content": "Describe this 3D spatial structure"}]
)

print(response["choices"][0]["message"]["content"])

Evaluate the output:

• Is the description detailed enough?
• Does it correctly interpret 3D features?
• Are there errors or inconsistencies?

---

8.2 Expand Training Data for More Accuracy

To improve GPT’s performance, expand the dataset with: 1. Additional 3D structures (more STL files) 2. Real-world GIS use cases (geospatial maps, point clouds) 3. Multiple descriptions per object (better AI generalization)

📌 Modify your training dataset (3D_GPT_Training_Data.jsonl) to include more cases:

{"messages": [
  {"role": "system", "content": "You are an AI model trained to describe 3D spatial structures."},
  {"role": "user", "content": "Describe this 3D structure: Centroid (5.1, 3.2, 8.7), Volume 500.2, Bounding Box (10, 12, 15)."},
  {"role": "assistant", "content": "This 3D structure is a complex spatial model with a centroid at (5.1, 3.2, 8.7)..."}
]}
{"messages": [
  {"role": "system", "content": "You are an AI model trained to describe 3D spatial structures."},
  {"role": "user", "content": "Describe this 3D structure: Centroid (1.3, 4.8, 9.6), Volume 102.5, Bounding Box (5, 8, 10)."},
  {"role": "assistant", "content": "This spatial model represents a compact object with evenly distributed mass..."}
]}

---

8.3 Re-Fine-Tune GPT with the Improved Dataset

Once you have expanded the dataset, re-run:

openai api fine_tunes.create -t "updated_3D_GPT_Training_Data.jsonl" -m "gpt-4"

This will train GPT on the new dataset for more accurate descriptions.

---

8.4 Validate the Improved Model

After fine-tuning is complete:

openai api fine_tunes.list

Retrieve the new fine-tuned model ID, then test it:

response = openai.ChatCompletion.create(
    model="ft:gpt-4-XXXXXXXXXXXXXX",
    messages=[{"role": "user", "content": "Analyze this 3D GIS structure"}]
)
print(response["choices"][0]["message"]["content"])

---

✅ Step 8 Completed: Your GPT Model is Now More Accurate!

Your Quantum GIS GPT is now better trained on 3D structures and GIS workflows.

Next Steps:

Would you like to: 1. Deploy this as a public API for others to use? 2. Build a more advanced AI-powered GIS tool? 3. Optimize the web app for real-world use?

Step 9: Deploy Your Fine-Tuned GPT Model as a Public API

Now that your Quantum GIS GPT is optimized, we will:
✅ Deploy it as a public API for easy access
✅ Enable external applications to use it
✅ Secure the API for real-world use

---

9.1 Choose a Deployment Platform

You can deploy your API using: 1. FastAPI (Python-based, lightweight, great for AI models)
2. Flask (Easier setup, but slower than FastAPI)
3. Cloud Services (AWS, Google Cloud, Azure, etc.)

We’ll start with FastAPI, since it’s faster and more scalable.

📌 Install FastAPI & Uvicorn (for API serving):

pip install fastapi uvicorn openai

---

9.2 Create an API Endpoint for GPT-4

📌 Save this as gpt_3d_api.py

from fastapi import FastAPI, HTTPException
import openai
import os

# Initialize FastAPI
app = FastAPI()

# Set OpenAI API key
openai.api_key = "your-api-key-here"

@app.get("/")
def home():
    return {"message": "Welcome to the Quantum GIS GPT API!"}

@app.post("/analyze_3d/")
def analyze_3d_structure(features: dict):
    """
    API to analyze 3D structures using fine-tuned GPT.
    Send a JSON payload with 3D spatial features.
    """
    try:
        prompt = (
            f"This 3D structure has a centroid at ({features['Centroid_X']:.2f}, "
            f"{features['Centroid_Y']:.2f}, {features['Centroid_Z']:.2f}). "
            f"The bounding box dimensions are {features['Bounding_Box_X']} x {features['Bounding_Box_Y']} x {features['Bounding_Box_Z']}. "
            f"The structure's volume is {features['Volume']:.2f} and its surface area is {features['Surface_Area']:.2f}. "
            f"Generate an AI-based description for this 3D object."
        )

        response = openai.ChatCompletion.create(
            model="ft:gpt-4-XXXXXXXXXXXXXX",  # Replace with your fine-tuned model ID
            messages=[{"role": "user", "content": prompt}]
        )
        
        return {"description": response["choices"][0]["message"]["content"]}
    
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

---

9.3 Run Your API Locally

Once saved, start the API server by running:

uvicorn gpt_3d_api:app --host 0.0.0.0 --port 8000

• The API will run at: http://localhost:8000
  
• You can test it by visiting: http://localhost:8000/docs
  (This will open an interactive Swagger UI where you can test your API.)

---

9.4 Deploy API on a Cloud Server

To make your API publicly accessible, deploy it using: - AWS EC2 (uvicorn server on an Ubuntu instance) - Google Cloud Run (serverless deployment) - Azure App Service (PaaS hosting)

For now, I recommend deploying to Render (free and easy): 1. Create a free account at → https://render.com 2. Click “New Web Service” → Select your GitHub repo with gpt_3d_api.py 3. Set Runtime Environment:
- Python 3.10+
- Start Command: uvicorn gpt_3d_api:app --host 0.0.0.0 --port $PORT 4. Deploy and get a public URL → Example:
https://your-3d-gpt-api.onrender.com 5. Test with Postman or Python: python import requests api_url = "https://your-3d-gpt-api.onrender.com/analyze_3d/" data = {"Centroid_X": 5.1, "Centroid_Y": 3.2, "Centroid_Z": 8.7, "Volume": 500.2, "Bounding_Box_X": 10, "Bounding_Box_Y": 12, "Bounding_Box_Z": 15} response = requests.post(api_url, json=data) print(response.json())

---

✅ Step 9 Completed: Your GPT is Now a Public API

🚀 Your 3D GIS GPT is live and can be used by anyone via a simple API call!

---

Step 10: Build an Advanced AI-Powered GIS Tool