app.py

import gradio as gr
import yaml
import re
import os
import torch

# Paths from installed glmocr package (required on HF Space)
import glmocr
GLMOCR_BASE = os.path.dirname(glmocr.__file__)
config_path = os.path.join(GLMOCR_BASE, "config.yaml")
formatter_path = os.path.join(GLMOCR_BASE, "postprocess", "result_formatter.py")

# ── STEP 1: Fix config — keep header & footer (do NOT add them to abandon) ──
with open(config_path, "r") as f:
    config = yaml.safe_load(f)
config["pipeline"]["result_formatter"]["abandon"] = [
    "number", "footnote", "aside_text", "reference",
    "footer_image", "header_image",
]
config["pipeline"]["enable_layout"] = True
with open(config_path, "w") as f:
    yaml.dump(config, f, default_flow_style=False, sort_keys=False)
print("✅ config.yaml fixed (header & footer kept in output)")

# ── STEP 2: Fix result_formatter.py (remove hardcoded header/footer) ───────
with open(formatter_path, "r") as f:
    source = f.read()
labels_to_remove = [
    '"header"', "'header'", '"footer"', "'footer'",
    '"doc_header"', "'doc_header'", '"doc_footer"', "'doc_footer'"
]
for label in labels_to_remove:
    source = re.sub(r',\s*' + re.escape(label), '', source)
    source = re.sub(re.escape(label) + r'\s*,', '', source)
    source = re.sub(re.escape(label), '', source)
with open(formatter_path, "w") as f:
    f.write(source)
print("✅ result_formatter.py fixed")

# ── STEP 3: Load model ────────────────────────────────────────────────────
from transformers import AutoProcessor, GlmOcrForConditionalGeneration
print("Loading model... (~2GB first run)")
processor = AutoProcessor.from_pretrained("zai-org/GLM-OCR")
model = GlmOcrForConditionalGeneration.from_pretrained(
    "zai-org/GLM-OCR",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
print("✅ Model ready on", next(model.parameters()).device)
ABANDON = set(config["pipeline"]["result_formatter"]["abandon"])

# ── STEP 4: OCR (PDF → image then run model) ───────────────────────────────
def run_ocr(uploaded_file):
    if uploaded_file is None:
        return "Please upload a file.", "No regions detected."
    try:
        path = uploaded_file.name if hasattr(uploaded_file, "name") else str(uploaded_file)
        if path.lower().endswith(".pdf"):
            try:
                import fitz
                doc = fitz.open(path)
                page = doc[0]
                pix = page.get_pixmap(matrix=fitz.Matrix(1, 1), alpha=False)
                img_path = path[:-4] + "_page0.png"
                pix.save(img_path)
                doc.close()
                path = img_path
            except Exception as e:
                return "PDF conversion failed: " + str(e), "Failed."

        messages = [
            {"role": "user", "content": [
                {"type": "image", "url": path},
                {"type": "text", "text": "Document Parsing:"}
            ]}
        ]
        inputs = processor.apply_chat_template(
            messages, tokenize=True, add_generation_prompt=True,
            return_dict=True, return_tensors="pt"
        ).to(model.device)
        inputs.pop("token_type_ids", None)
        with torch.no_grad():
            output_ids = model.generate(**inputs, max_new_tokens=2048)
        raw = processor.decode(
            output_ids[0][inputs["input_ids"].shape[1]:],
            skip_special_tokens=False
        )
        raw = raw.replace("<|user|>", "").strip()
        json_match = re.search(r'\[.*\]', raw, re.DOTALL)
        regions = json.loads(json_match.group()) if json_match else []
        header_count = footer_count = 0
        region_lines = []
        markdown_parts = []
        for region in regions:
            label = region.get("label", "text")
            content = str(region.get("content", ""))
            if label in ABANDON:
                continue
            if label == "header":
                header_count += 1
                region_lines.append("🔵 HEADER:\n" + content + "\n")
                markdown_parts.append("<!-- HEADER -->\n" + content)
            elif label == "footer":
                footer_count += 1
                region_lines.append("🟢 FOOTER:\n" + content + "\n")
                markdown_parts.append("<!-- FOOTER -->\n" + content)
            else:
                region_lines.append("[" + label + "]: " + content[:150])
                markdown_parts.append(content)
        summary = (
            "Headers found : " + str(header_count) + "\n"
            "Footers found : " + str(footer_count) + "\n"
            "Total regions : " + str(len(regions)) + "\n" + "─"*40 + "\n"
            + "\n".join(region_lines)
        )
        markdown = "\n\n".join(markdown_parts) if markdown_parts else raw
        return markdown, summary
    except Exception as e:
        import traceback
        return "Error: " + str(e) + "\n\n" + traceback.format_exc(), "Failed."

# ── STEP 5: Gradio UI ──────────────────────────────────────────────────────
with gr.Blocks(title="GLM-OCR — Header & Footer Kept") as demo:
    gr.Markdown("# 🔍 GLM-OCR — Header & Footer Kept\nUpload PDF or image. Headers 🔵 and Footers 🟢 are kept in output.")
    file_input = gr.File(label="Upload PDF or Image", file_types=[".pdf", ".png", ".jpg", ".jpeg", ".tiff", ".bmp"])
    run_btn = gr.Button("▶  Run OCR", variant="primary", size="lg")
    with gr.Row():
        with gr.Column():
            gr.Markdown("### 📄 Markdown Output")
            markdown_out = gr.Textbox(lines=25, label="")
        with gr.Column():
            gr.Markdown("### 🗂️ Detected Regions")
            regions_out = gr.Textbox(lines=25, label="")
    run_btn.click(fn=run_ocr, inputs=file_input, outputs=[markdown_out, regions_out])
demo.launch()