pushing the changes..

Dockerfile

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+# Hugging Face Spaces: Recommended Secure Dockerfile for FastAPI
+
+FROM python:3.10-slim
+
+# Add a non-root user (important for HF Spaces security)
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+# Copy requirements and install as user (not root)
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy the rest of the app as user
+COPY --chown=user . /app
+
+# Expose port for FastAPI (7860 is default for Spaces)
+EXPOSE 7860
+
+# Start FastAPI app with uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import os
+import json
+from io import BytesIO
+from PIL import Image
+import torch
+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.responses import JSONResponse
+from huggingface_hub import hf_hub_download
+from transformers import (
+    AutoProcessor,
+    LayoutLMv3Model,
+    T5ForConditionalGeneration,
+    AutoTokenizer
+)
+
+app = FastAPI()
+
+# ── 1) CONFIG & CHECKPOINT ────────────────────────────────────────────────
+HF_REPO     = "shouvik27/LayoutLMv3_T5"
+CKPT_NAME   = "pytorch_model.bin"
+
+ckpt_path = hf_hub_download(repo_id=HF_REPO, filename=CKPT_NAME)
+ckpt = torch.load(ckpt_path, map_location="cpu")
+
+# ── 2) BUILD MODELS ───────────────────────────────────────────────────────
+processor = AutoProcessor.from_pretrained(
+    "microsoft/layoutlmv3-base", apply_ocr=False
+)
+layout_model = LayoutLMv3Model.from_pretrained("microsoft/layoutlmv3-base")
+layout_model.load_state_dict(ckpt["layout_model"], strict=False)
+layout_model.eval().to("cpu")
+
+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")
+
+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")
+
+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_from_files(image_file: UploadFile, json_file: UploadFile):
+    # Read image
+    image_bytes = image_file.file.read()
+    img_name = os.path.basename(image_file.filename)
+
+    # Parse the NDJSON file, find entry
+    entry = None
+    for line in json_file.file:
+        if not line.strip():
+            continue
+        obj = json.loads(line.decode('utf-8').strip())
+        if obj.get("img_name") == img_name:
+            entry = obj
+            break
+
+    if entry is None:
+        return {"error": f"No JSON entry for: {img_name}"}
+
+    words = entry["src_word_list"]
+    boxes = entry["src_wordbox_list"]
+
+    img = Image.open(BytesIO(image_bytes)).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")
+
+    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
+        )
+
+    result = tokenizer.decode(gen_ids[0], skip_special_tokens=True)
+    return {"result": result}
+
+# ── 4) FASTAPI ENDPOINT ──────────────────────────────────────────────────
+@app.post("/infer")
+async def infer_api(
+    image_file: UploadFile = File(..., description="The image file"),
+    json_file: UploadFile = File(..., description="The NDJSON file"),
+):
+    output = infer_from_files(image_file, json_file)
+    return JSONResponse(content=output)
+
+@app.get("/")
+def healthcheck():
+    return {"message": "OCR FastAPI server is running."}

requirements.txt

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+fastapi
+uvicorn
+torch
+transformers
+huggingface_hub
+Pillow
+python-multipart