app.py

# app.py — TRUST OCR DEMO (Streamlit) — works even if batch_text_detection is missing

import os
import io
import tempfile
from typing import List

import numpy as np
import cv2
from PIL import Image
import pypdfium2
import pytesseract
# --- set safe dirs before importing streamlit ---
safe_home = os.environ.get("HOME") or "/app"
os.environ["HOME"] = safe_home
cfg_dir = os.path.join(safe_home, ".streamlit")
os.makedirs(cfg_dir, exist_ok=True)

# اطمینان از اینکه Streamlit همه فایل‌ها را اینجا می‌نویسد
os.environ["STREAMLIT_CONFIG_DIR"] = cfg_dir

# اگر دوست داری همین‌جا config.toml بسازی و usage stats را خاموش کنی:
conf_path = os.path.join(cfg_dir, "config.toml")
if not os.path.exists(conf_path):
    with open(conf_path, "w", encoding="utf-8") as f:
        f.write("browser.gatherUsageStats = false\n")

# runtime dir امن
runtime_dir = os.path.join(tempfile.gettempdir(), ".streamlit")
os.environ["STREAMLIT_RUNTIME_DIR"] = runtime_dir
os.makedirs(runtime_dir, exist_ok=True)

import streamlit as st


# ===== Safe runtime dir for Streamlit/HF cache =====
# runtime_dir = os.path.join(tempfile.gettempdir(), ".streamlit")
# os.environ["STREAMLIT_RUNTIME_DIR"] = runtime_dir
# os.makedirs(runtime_dir, exist_ok=True)

# ===== Try to import Surya APIs =====
DET_AVAILABLE = True
try:
    from surya.detection import batch_text_detection
except Exception:
    DET_AVAILABLE = False

from surya.layout import batch_layout_detection  # may still import; we’ll gate usage by DET_AVAILABLE

# Detection model loaders: segformer (newer) vs model (older)
try:
    from surya.model.detection.segformer import load_model as load_det_model, load_processor as load_det_processor
except Exception:
    from surya.model.detection.model import load_model as load_det_model, load_processor as load_det_processor

from surya.model.recognition.model import load_model as load_rec_model
from surya.model.recognition.processor import load_processor as load_rec_processor

from surya.model.ordering.model import load_model as load_order_model
from surya.model.ordering.processor import load_processor as load_order_processor
from surya.ordering import batch_ordering

from surya.ocr import run_ocr
from surya.postprocessing.heatmap import draw_polys_on_image
from surya.postprocessing.text import draw_text_on_image
from surya.languages import CODE_TO_LANGUAGE
from surya.input.langs import replace_lang_with_code
from surya.schema import OCRResult, TextDetectionResult, LayoutResult, OrderResult


# ===================== Helper Functions =====================

def remove_border(image_path: str, output_path: str) -> np.ndarray:
    """Remove outer border & deskew (perspective) if a rectangular contour is found."""
    image = cv2.imread(image_path)
    if image is None:
        raise ValueError(f"Cannot read image: {image_path}")
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    if not contours:
        cv2.imwrite(output_path, image)
        return image
    max_contour = max(contours, key=cv2.contourArea)
    epsilon = 0.02 * cv2.arcLength(max_contour, True)
    approx = cv2.approxPolyDP(max_contour, epsilon, True)
    if len(approx) == 4:
        pts = approx.reshape(4, 2).astype("float32")
        rect = np.zeros((4, 2), dtype="float32")
        s = pts.sum(axis=1)
        rect[0] = pts[np.argmin(s)]   # tl
        rect[2] = pts[np.argmax(s)]   # br
        diff = np.diff(pts, axis=1)
        rect[1] = pts[np.argmin(diff)]  # tr
        rect[3] = pts[np.argmax(diff)]  # bl
        (tl, tr, br, bl) = rect
        widthA = np.linalg.norm(br - bl)
        widthB = np.linalg.norm(tr - tl)
        maxWidth = max(int(widthA), int(widthB))
        heightA = np.linalg.norm(tr - br)
        heightB = np.linalg.norm(tl - bl)
        maxHeight = max(int(heightA), int(heightB))
        dst = np.array([[0, 0], [maxWidth - 1, 0],
                        [maxWidth - 1, maxHeight - 1],
                        [0, maxHeight - 1]], dtype="float32")
        M = cv2.getPerspectiveTransform(rect, dst)
        cropped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
        cv2.imwrite(output_path, cropped)
        return cropped
    else:
        cv2.imwrite(output_path, image)
        return image


def open_pdf(pdf_file) -> pypdfium2.PdfDocument:
    stream = io.BytesIO(pdf_file.getvalue())
    return pypdfium2.PdfDocument(stream)


@st.cache_data(show_spinner=False)
def get_page_image(pdf_file, page_num: int, dpi: int = 96) -> Image.Image:
    doc = open_pdf(pdf_file)
    renderer = doc.render(pypdfium2.PdfBitmap.to_pil, page_indices=[page_num - 1], scale=dpi / 72)
    png = list(renderer)[0]
    return png.convert("RGB")


@st.cache_data(show_spinner=False)
def page_count(pdf_file) -> int:
    doc = open_pdf(pdf_file)
    return len(doc)


# ===================== Streamlit UI =====================

st.set_page_config(page_title="TRUST OCR DEMO", layout="wide")
st.markdown("# TRUST OCR DEMO")

if not DET_AVAILABLE:
    st.warning("⚠️ ماژول تشخیص متن Surya در این محیط در دسترس نیست. OCR کامل کار می‌کند، اما دکمه‌های Detection/Layout/Order غیرفعال شده‌اند. برای فعال‌سازی آن‌ها، Surya را به نسخهٔ سازگار پین کنید (راهنما پایین صفحه).")

# Sidebar controls
in_file = st.sidebar.file_uploader("فایل PDF یا عکس :", type=["pdf", "png", "jpg", "jpeg", "gif", "webp"])
languages = st.sidebar.multiselect(
    "زبان‌ها (Languages)",
    sorted(list(CODE_TO_LANGUAGE.values())),
    default=["Persian"],
    max_selections=4
)
auto_rotate = st.sidebar.toggle("چرخش خودکار (Tesseract OSD)", value=True)
auto_border = st.sidebar.toggle("حذف قاب/کادر تصویر ورودی", value=True)

# Buttons (disable some if detection missing)
text_det_btn = st.sidebar.button("تشخیص متن (Detection)", disabled=not DET_AVAILABLE)
layout_det_btn = st.sidebar.button("آنالیز صفحه (Layout)", disabled=not DET_AVAILABLE)
order_det_btn = st.sidebar.button("ترتیب خوانش (Reading Order)", disabled=not DET_AVAILABLE)
text_rec_btn = st.sidebar.button("تبدیل به متن (Recognition)")

if in_file is None:
    st.info("یک فایل PDF/عکس از سایدبار انتخاب کنید. | Please upload a file to begin.")
    st.stop()

filetype = in_file.type

# Two-column layout (left: outputs / right: input image)
col2, col1 = st.columns([.5, .5])

# ===================== Load Models (cached) =====================

@st.cache_resource(show_spinner=True)
def load_det_cached():
    return load_det_model(checkpoint="vikp/surya_det2"), load_det_processor(checkpoint="vikp/surya_det2")

@st.cache_resource(show_spinner=True)
def load_rec_cached():
    return load_rec_model(checkpoint="MohammadReza-Halakoo/TrustOCR"), \
           load_rec_processor(checkpoint="MohammadReza-Halakoo/TrustOCR")

@st.cache_resource(show_spinner=True)
def load_layout_cached():
    return load_det_model(checkpoint="vikp/surya_layout2"), load_det_processor(checkpoint="vikp/surya_layout2")

@st.cache_resource(show_spinner=True)
def load_order_cached():
    return load_order_model(checkpoint="vikp/surya_order"), load_order_processor(checkpoint="vikp/surya_order")


# recognition models are enough for run_ocr; detection/layout/order models used only if DET_AVAILABLE
rec_model, rec_processor = load_rec_cached()
if DET_AVAILABLE:
    det_model, det_processor = load_det_cached()
    layout_model, layout_processor = load_layout_cached()
    order_model, order_processor = load_order_cached()
else:
    det_model = det_processor = layout_model = layout_processor = order_model = order_processor = None


# ===================== High-level Ops =====================

def _apply_auto_rotate(pil_img: Image.Image) -> Image.Image:
    """Auto-rotate using Tesseract OSD if enabled."""
    if not auto_rotate:
        return pil_img
    try:
        osd = pytesseract.image_to_osd(pil_img, output_type=pytesseract.Output.DICT)
        angle = int(osd.get("rotate", 0))  # 0/90/180/270
        if angle and angle % 360 != 0:
            return pil_img.rotate(-angle, expand=True)
        return pil_img
    except Exception as e:
        st.warning(f"OSD rotation failed, continuing without rotation. Error: {e}")
        return pil_img


def text_detection(pil_img: Image.Image):
    pred: TextDetectionResult = batch_text_detection([pil_img], det_model, det_processor)[0]
    polygons = [p.polygon for p in pred.bboxes]
    det_img = draw_polys_on_image(polygons, pil_img.copy())
    return det_img, pred


def layout_detection(pil_img: Image.Image):
    _, det_pred = text_detection(pil_img)
    pred: LayoutResult = batch_layout_detection([pil_img], layout_model, layout_processor, [det_pred])[0]
    polygons = [p.polygon for p in pred.bboxes]
    labels = [p.label for p in pred.bboxes]
    layout_img = draw_polys_on_image(polygons, pil_img.copy(), labels=labels, label_font_size=40)
    return layout_img, pred


def order_detection(pil_img: Image.Image):
    _, layout_pred = layout_detection(pil_img)
    bboxes = [l.bbox for l in layout_pred.bboxes]
    pred: OrderResult = batch_ordering([pil_img], [bboxes], order_model, order_processor)[0]
    polys = [l.polygon for l in pred.bboxes]
    positions = [str(l.position) for l in pred.bboxes]
    order_img = draw_polys_on_image(polys, pil_img.copy(), labels=positions, label_font_size=40)
    return order_img, pred


def ocr_page(pil_img: Image.Image, langs: List[str]):
    """Full-page OCR using Surya run_ocr — works without detection import."""
    langs = list(langs) if langs else ["Persian"]
    replace_lang_with_code(langs)  # in-place
    # If detection models are loaded, pass them; else, let run_ocr use its internal defaults
    args = [pil_img], [langs]
    if det_model and det_processor and rec_model and rec_processor:
        img_pred: OCRResult = run_ocr([pil_img], [langs], det_model, det_processor, rec_model, rec_processor)[0]
    else:
        img_pred: OCRResult = run_ocr([pil_img], [langs])[0]
    bboxes = [l.bbox for l in img_pred.text_lines]
    text = [l.text for l in img_pred.text_lines]
    rec_img = draw_text_on_image(bboxes, text, pil_img.size, langs, has_math="_math" in langs)
    return rec_img, img_pred


# ===================== Input Handling =====================

if "pdf" in filetype:
    try:
        pg_cnt = page_count(in_file)
    except Exception as e:
        st.error(f"خواندن PDF ناموفق بود: {e}")
        st.stop()
    page_number = st.sidebar.number_input("صفحه:", min_value=1, value=1, max_value=pg_cnt)
    pil_image = get_page_image(in_file, page_number)
else:
    bytes_data = in_file.getvalue()
    temp_dir = "temp_files"
    os.makedirs(temp_dir, exist_ok=True)
    file_path = os.path.join(temp_dir, in_file.name)
    with open(file_path, "wb") as f:
        f.write(bytes_data)
    out_file = os.path.splitext(file_path)[0] + "-1.JPG"
    try:
        if auto_border:
            _ = remove_border(file_path, out_file)
            pil_image = Image.open(out_file).convert("RGB")
        else:
            pil_image = Image.open(file_path).convert("RGB")
    except Exception as e:
        st.warning(f"حذف قاب/بازخوانی تصویر با خطا مواجه شد؛ تصویر اصلی استفاده می‌شود. Error: {e}")
        pil_image = Image.open(file_path).convert("RGB")

# Auto-rotate if enabled
pil_image = _apply_auto_rotate(pil_image)

# ===================== Buttons Logic =====================

with col1:
    if text_det_btn and DET_AVAILABLE:
        try:
            det_img, det_pred = text_detection(pil_image)
            st.image(det_img, caption="تشخیص متن (Detection)", use_column_width=True)
        except Exception as e:
            st.error(f"خطا در تشخیص متن: {e}")

    if layout_det_btn and DET_AVAILABLE:
        try:
            layout_img, layout_pred = layout_detection(pil_image)
            st.image(layout_img, caption="آنالیز صفحه (Layout)", use_column_width=True)
        except Exception as e:
            st.error(f"خطا در آنالیز صفحه: {e}")

    if order_det_btn and DET_AVAILABLE:
        try:
            order_img, order_pred = order_detection(pil_image)
            st.image(order_img, caption="ترتیب خوانش (Reading Order)", use_column_width=True)
        except Exception as e:
            st.error(f"خطا در ترتیب خوانش: {e}")

    if text_rec_btn:
        try:
            rec_img, ocr_pred = ocr_page(pil_image, languages)
            text_tab, json_tab = st.tabs(["متن صفحه | Page Text", "JSON"])
            with text_tab:
                st.text("\n".join([p.text for p in ocr_pred.text_lines]))
            with json_tab:
                st.json(ocr_pred.model_dump(), expanded=False)
        except Exception as e:
            st.error(f"خطا در بازشناسی متن (Recognition): {e}")

with col2:
    st.image(pil_image, caption="تصویر ورودی | Input Preview", use_column_width=True)