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Text_to_SVG_GRPO
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#!/usr/bin/env python3
# svg_compare_gradio.py
# ------------------------------------------------------------
import re, os, torch, cairosvg, lpips, clip, gradio as gr
from io import BytesIO
from pathlib import Path
from PIL import Image
from unsloth import FastLanguageModel
from transformers import BitsAndBytesConfig, AutoTokenizer
import gradio as gr
import spaces

# ---------- paths YOU may want to edit ----------------------
ADAPTER_DIR = "unsloth_trained_weights/checkpoint-1700"  # LoRA ckpt
BASE_MODEL  = "Qwen/Qwen2.5-Coder-7B-Instruct"
MAX_NEW     = 512
DEVICE      = "cuda" # if torch.cuda.is_available() else "cpu"

# ---------- utils -------------------------------------------
SVG_PAT = re.compile(r"<svg[^>]*>.*?</svg>", re.S | re.I)
def extract_svg(txt:str):
    m = list(SVG_PAT.finditer(txt))
    return m[-1].group(0) if m else None                       # last match βœ”

def svg2pil(svg:str):
    try:
        png = cairosvg.svg2png(bytestring=svg.encode())
        return Image.open(BytesIO(png)).convert("RGB")
    except Exception:
        return None

# ---------- backbone loaders (CLIP + LPIPS) -----------------
_CLIP,_PREP,_LP=None,None,None
@spaces.GPU
def _load_backbones():
    global _CLIP,_PREP,_LP
    if _CLIP is None:
        _CLIP,_PREP = clip.load("ViT-L/14", device=DEVICE); _CLIP.eval()
    if _LP is None:
        _LP = lpips.LPIPS(net="vgg").to(DEVICE).eval()

@spaces.GPU
@torch.no_grad()
def fused_sim(a:Image.Image,b:Image.Image,Ξ±=.5):
    _load_backbones()
    ta,tb = _PREP(a).unsqueeze(0).to(DEVICE), _PREP(b).unsqueeze(0).to(DEVICE)
    fa = _CLIP.encode_image(ta); fa/=fa.norm(dim=-1,keepdim=True)
    fb = _CLIP.encode_image(tb); fb/=fb.norm(dim=-1,keepdim=True)
    clip_sim=(([email protected]).item()+1)/2
    lp_sim = 1 - _LP(ta,tb,normalize=True).item()
    return Ξ±*clip_sim + (1-Ξ±)*lp_sim

# ---------- load models once at startup ---------------------
bnb_cfg = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_use_double_quant=True)
print("Loading BASE …")
base, tok = FastLanguageModel.from_pretrained(
    BASE_MODEL, max_seq_length=2048,
    load_in_4bit=True, quantization_config=bnb_cfg, device_map="auto")
tok.pad_token = tok.eos_token

print("Loading LoRA …")
lora, _ = FastLanguageModel.from_pretrained(
    ADAPTER_DIR, max_seq_length=2048,
    load_in_4bit=True, quantization_config=bnb_cfg, device_map="auto")

def build_prompt(desc:str):
    msgs=[{"role":"system","content":"You are an SVG illustrator."},
          {"role":"user",
           "content":f"ONLY reply with a valid, complete <svg>…</svg> file that depicts: {desc}"}]
    return tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)

@spaces.GPU
@torch.no_grad()
def draw(model, desc:str):
    prompt = build_prompt(desc)
    ids = tok(prompt, return_tensors="pt").to(DEVICE)
    out = model.generate(**ids, max_new_tokens=MAX_NEW,
                         do_sample=True, temperature=.7, top_p=.8)
    txt = tok.decode(out[0], skip_special_tokens=True)
    svg = extract_svg(txt)
    img = svg2pil(svg) if svg else None
    return img, svg or "(no SVG found)"

# ---------- gradio interface --------------------------------
def compare(desc):
    img_base, svg_base = draw(base, desc)
    img_lora, svg_lora = draw(lora, desc)
    # sim = (fused_sim(img_lora, img_base) if img_base and img_lora else float("nan"))
    caption = "Thanks for trying our model 😊"
    return img_base, img_lora, caption, svg_base, svg_lora

with gr.Blocks(css="body{background:#111;color:#eee}") as demo:
    gr.Markdown("## πŸ–ŒοΈ Qwen-2.5 SVG Generator β€” base vs GRPO-LoRA")
    gr.Markdown(
        "Type an image **description** (e.g. *a purple forest at dusk*). "
        "Click **Generate** to see what the base model and your fine-tuned LoRA produce."
    )
    inp = gr.Textbox(label="Description", placeholder="a purple forest at dusk")
    btn = gr.Button("Generate")
    with gr.Row():
        out_base = gr.Image(label="Base model", type="pil")
        out_lora = gr.Image(label="LoRA-tuned model", type="pil")
    sim_lbl = gr.Markdown()
    with gr.Accordion("βš™οΈ  Raw SVG code", open=False):
        svg_base_box = gr.Textbox(label="Base SVG", lines=6)
        svg_lora_box = gr.Textbox(label="LoRA SVG", lines=6)
    btn.click(compare, inp, [out_base, out_lora, sim_lbl, svg_base_box, svg_lora_box])

demo.launch()