Add Modal + image rendering guide

README_MODAL.md

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+# 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
+```