app.py

import os, json, base64
from io import BytesIO
from PIL import Image
import gradio as gr
import torch
from huggingface_hub import hf_hub_download
from transformers import (
    AutoProcessor,
    LayoutLMv3Model,
    T5ForConditionalGeneration,
    AutoTokenizer
)

# ── 1) CONFIG & CHECKPOINT ────────────────────────────────────────────────
HF_REPO     = "Uddipan107/ocr-layoutlmv3-base-t5-small"
CKPT_NAME   = "pytorch_model.bin"

# 1a) Download the checkpoint dict from your Hub
ckpt_path   = hf_hub_download(repo_id=HF_REPO, filename=CKPT_NAME)
ckpt        = torch.load(ckpt_path, map_location="cpu")

# ── 2) BUILD MODELS ───────────────────────────────────────────────────────
# 2a) Processor for LayoutLMv3
processor   = AutoProcessor.from_pretrained(
    "microsoft/layoutlmv3-base", apply_ocr=False
)

# 2b) LayoutLMv3 encoder
layout_model = LayoutLMv3Model.from_pretrained("microsoft/layoutlmv3-base")
layout_model.load_state_dict(ckpt["layout_model"], strict=False)
layout_model.eval().to("cpu")

# 2c) T5 decoder + tokenizer
t5_model    = T5ForConditionalGeneration.from_pretrained("t5-small")
t5_model.load_state_dict(ckpt["t5_model"], strict=False)
t5_model.eval().to("cpu")

tokenizer   = AutoTokenizer.from_pretrained("t5-small")

# 2d) Projection head
proj_state  = ckpt["projection"]
projection  = torch.nn.Sequential(
    torch.nn.Linear(768, t5_model.config.d_model),
    torch.nn.LayerNorm(t5_model.config.d_model),
    torch.nn.GELU()
)
projection.load_state_dict(proj_state)
projection.eval().to("cpu")

# 2e) Ensure we have a valid start token for generation
if t5_model.config.decoder_start_token_id is None:
    t5_model.config.decoder_start_token_id = tokenizer.bos_token_id or tokenizer.pad_token_id
if t5_model.config.bos_token_id is None:
    t5_model.config.bos_token_id = t5_model.config.decoder_start_token_id

# ── 3) INFERENCE ─────────────────────────────────────────────────────────
def infer(image_path, json_file):
    img_name = os.path.basename(image_path)

    # 3a) Read the uploaded NDJSON & find the matching record
    entry = None
    with open(json_file.name, "r", encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if not line: 
                continue
            obj = json.loads(line)
            if obj.get("img_name") == img_name:
                entry = obj
                break

    if entry is None:
        return f"❌ No JSON entry for: {img_name}"

    words = entry["src_word_list"]
    boxes = entry["src_wordbox_list"]

    # 3b) Preprocess: image + OCR tokens + boxes
    img = Image.open(image_path).convert("RGB")
    enc = processor([img], [words], boxes=[boxes],
                    return_tensors="pt", padding=True, truncation=True)
    pixel_values   = enc.pixel_values.to("cpu")
    input_ids      = enc.input_ids.to("cpu")
    attention_mask = enc.attention_mask.to("cpu")
    bbox           = enc.bbox.to("cpu")

    # 3c) Forward pass
    with torch.no_grad():
        out = layout_model(
            pixel_values=pixel_values,
            input_ids=input_ids,
            attention_mask=attention_mask,
            bbox=bbox
        )
        seq_len    = input_ids.size(1)
        text_feats = out.last_hidden_state[:, :seq_len, :]
        proj_feats = projection(text_feats)

        gen_ids = t5_model.generate(
            inputs_embeds=proj_feats,
            attention_mask=attention_mask,
            max_length=512,
            decoder_start_token_id=t5_model.config.decoder_start_token_id
        )

    # 3d) Decode & return
    return tokenizer.decode(gen_ids[0], skip_special_tokens=True)

# ── 4) GRADIO APP ────────────────────────────────────────────────────────
demo = gr.Interface(
    fn=infer,
    inputs=[
        gr.Image(type="filepath", label="Upload Image"),
        gr.File(label="Upload JSON (NDJSON)")
    ],
    outputs="text",
    title="OCR Reorder Pipeline"
)

if __name__ == "__main__":
    demo.launch(share=True)