app.py

# app.py – encoder-only demo for bert-beatrix-2048
# -----------------------------------------------
# launch:  python app.py
# (gradio UI appears at http://localhost:7860)

import json
import re
import sys
from pathlib import Path, PurePosixPath   # ← PurePosixPath import added

import gradio as gr
import spaces
import torch
from huggingface_hub import snapshot_download

from bert_handler import create_handler_from_checkpoint


# ------------------------------------------------------------------
# 0.  Download & patch config.json  --------------------------------
# ------------------------------------------------------------------
REPO_ID = "AbstractPhil/bert-beatrix-2048"
LOCAL_CKPT = "bert-beatrix-2048"          # cached dir name

snapshot_download(
    repo_id=REPO_ID,
    revision="main",
    local_dir=LOCAL_CKPT,
    local_dir_use_symlinks=False,
)

# ── one-time patch: strip the “repo--” prefix that confuses AutoModel ──
cfg_path = Path(LOCAL_CKPT) / "config.json"
with cfg_path.open() as f:
    cfg = json.load(f)

auto_map = cfg.get("auto_map", {})
changed = False
for k, v in auto_map.items():
    if "--" in v:                         # v looks like  "repo--module.Class"
        auto_map[k] = PurePosixPath(v.split("--", 1)[1]).as_posix()
        changed = True

if changed:
    cfg["auto_map"] = auto_map
    with cfg_path.open("w") as f:
        json.dump(cfg, f, indent=2)
    print("🛠️  Patched config.json → auto_map now points at local modules")


# ------------------------------------------------------------------
# 1.  Model / tokenizer  -------------------------------------------
# ------------------------------------------------------------------
handler, full_model, tokenizer = create_handler_from_checkpoint(LOCAL_CKPT)
full_model = full_model.eval().cuda()

# Grab encoder + embedding stack only
encoder     = full_model.bert.encoder
embeddings  = full_model.bert.embeddings
emb_ln      = full_model.bert.emb_ln
emb_drop    = full_model.bert.emb_drop


# ------------------------------------------------------------------
# 2.  Symbolic token set  ------------------------------------------
# ------------------------------------------------------------------
SYMBOLIC_ROLES = [
    "<subject>", "<subject1>", "<subject2>", "<pose>", "<emotion>",
    "<surface>", "<lighting>", "<material>", "<accessory>", "<footwear>",
    "<upper_body_clothing>", "<hair_style>", "<hair_length>", "<headwear>",
    "<texture>", "<pattern>", "<grid>", "<zone>", "<offset>",
    "<object_left>", "<object_right>", "<relation>", "<intent>", "<style>",
    "<fabric>", "<jewelry>",
]

# quick sanity check
missing = [tok for tok in SYMBOLIC_ROLES
           if tokenizer.convert_tokens_to_ids(tok) == tokenizer.unk_token_id]
if missing:
    sys.exit(f"❌ Tokenizer is missing {missing}")


# ------------------------------------------------------------------
# 3.  Encoder-only inference util  ---------------------------------
# ------------------------------------------------------------------
@spaces.GPU
def encode_and_trace(text: str, selected_roles: list[str]):
    with torch.no_grad():
        batch = tokenizer(text, return_tensors="pt").to("cuda")
        ids, mask = batch.input_ids, batch.attention_mask

        x = emb_drop(emb_ln(embeddings(ids)))

        ext_mask = full_model.bert.get_extended_attention_mask(mask, x.shape[:-1])
        enc = encoder(x, attention_mask=ext_mask)              # (1, S, H)

        sel_ids = {tokenizer.convert_tokens_to_ids(t) for t in selected_roles}
        flags   = torch.tensor([tid in sel_ids for tid in ids[0].tolist()],
                               device=enc.device)

        found = [tokenizer.convert_ids_to_tokens([tid])[0]
                 for tid in ids[0].tolist() if tid in sel_ids]

        if flags.any():
            vec = enc[0][flags].mean(0)
            norm = f"{vec.norm().item():.4f}"
        else:
            norm = "0.0000"

        return {
            "Symbolic Tokens": ", ".join(found) or "(none)",
            "Embedding Norm":  norm,
            "Symbolic Token Count": int(flags.sum().item()),
        }


# ------------------------------------------------------------------
# 4.  Gradio UI  ----------------------------------------------------
# ------------------------------------------------------------------
def build_interface():
    with gr.Blocks(title="🧠 Symbolic Encoder Inspector") as demo:
        gr.Markdown(
            "## 🧠 Symbolic Encoder Inspector\n"
            "Paste some text containing the special `<role>` tokens and "
            "inspect their encoder representations."
        )

        with gr.Row():
            with gr.Column():
                txt = gr.Textbox(
                    label="Input with Symbolic Tokens",
                    placeholder="Example: A <subject> wearing <upper_body_clothing> …",
                    lines=3,
                )
                roles = gr.CheckboxGroup(
                    choices=SYMBOLIC_ROLES,
                    label="Trace these symbolic roles",
                )
                btn = gr.Button("Encode & Trace")
            with gr.Column():
                out_tok  = gr.Textbox(label="Symbolic Tokens Found")
                out_norm = gr.Textbox(label="Mean Norm")
                out_cnt  = gr.Textbox(label="Token Count")

        btn.click(encode_and_trace, [txt, roles], [out_tok, out_norm, out_cnt])

    return demo


if __name__ == "__main__":
    build_interface().launch()