README_MODAL.md

# Running on Modal + Image Rendering Guide

## 1. Running Training on Modal

### Setup

```bash
pip install modal
modal setup  # Authenticate with your Modal token
```

### Create a Modal Secret for HuggingFace

```bash
modal secret create huggingface-token HF_TOKEN=your_hf_token_here
```

### Deploy & Run

The repo includes `modal_train.py`. Simply:

```bash
cd parametric-floorplan-generator
modal run modal_train.py
```

This will:
1. Spin up a CPU container to generate the 5,000-example synthetic dataset (saved to a Modal Volume)
2. Spin up an **A10G GPU** container to fine-tune Qwen2.5-1.5B-Instruct with LoRA
3. Push the trained model to HuggingFace Hub

### Customize GPU

Edit `modal_train.py` and change the GPU:

```python
@app.function(gpu="A100-40GB", ...)  # or "T4", "H100"
```

### Check Progress

```bash
modal app logs floorplan-trainer
```

---

## 2. Generating Floorplan Images

The model outputs **JSON** with room polygons. To convert to visual plans:

### Option A: SVG (Vector, best for CAD/printing)

```bash
# Generate a floorplan first
python generate.py \
  --plot_length 15 --plot_width 12 \
  --setback_front 1.5 --setback_rear 1.0 \
  --setback_left 1.0 --setback_right 1.0 \
  --road_side N --num_bedrooms 3 --toilets 3 \
  --parking --has_pooja --has_balcony \
  --num_floors 2 --city Delhi > myhouse.json

# Render to SVG
python render_floorplan.py --input myhouse.json --output myhouse.svg
```

The SVG includes:
- **Plot boundary** (thick black line)
- **Buildable boundary** (dashed gray)
- **Rooms** color-coded by type (living=blue, bedroom=orange, kitchen=purple, etc.)
- **Door openings** (green lines)
- **Windows** (blue dashed lines)
- **Room labels** with names and areas
- **Dimension annotations**
- **North arrow**
- **Legend**

### Option B: PNG (Raster, best for web/presentations)

```bash
pip install cairosvg
python render_floorplan.py --input myhouse.json --output myhouse.png
```

### Option C: Interactive Web Viewer (Gradio)

Deploy as a HuggingFace Space:

```python
import gradio as gr
import json
from render_floorplan import render_floorplan_svg

def generate_and_render(params_json):
    floorplan = json.loads(model_output)
    svg = render_floorplan_svg(floorplan, width=1200)
    return svg

gr.Interface(
    fn=generate_and_render,
    inputs=gr.JSON(label="Project Parameters"),
    outputs=gr.HTML(label="Floorplan SVG"),
    title="Parametric Floorplan Generator"
).launch()
```

### Option D: CAD Export (DXF)

For professional CAD output, extend `render_floorplan.py` to write DXF using `ezdxf`:

```bash
pip install ezdxf
```

Then iterate over the JSON polygons and write them as DXF polylines.

---

## 3. Complete Pipeline on Modal

You can also run inference + rendering as a Modal endpoint:

```python
import modal

app = modal.App("floorplan-api")

@app.function(gpu="T4", image=modal.Image.debian_slim().pip_install("transformers", "torch", "accelerate", "cairosvg"))
@modal.web_endpoint(method="POST")
def generate(params: dict):
    # 1. Run model inference
    # 2. Render SVG
    # 3. Convert to PNG
    # 4. Return image
    pass
```

This gives you an HTTP API that takes your ProjectCreate JSON and returns a PNG floorplan.

---

## 4. Summary of Commands

```bash
# Clone repo
git clone https://huggingface.co/Karthik8nitt/parametric-floorplan-generator
cd parametric-floorplan-generator

# Install locally
pip install transformers trl torch datasets peft accelerate trackio
pip install cairosvg  # for PNG rendering

# Train on Modal
modal run modal_train.py

# Generate floorplan
python generate.py --plot_length 15 --plot_width 12 ... > plan.json

# Render
python render_floorplan.py --input plan.json --output plan.svg
python render_floorplan.py --input plan.json --output plan.png
```