Update app.py

app.py

@@ -1,4 +1,368 @@
-# app.py — TRUST OCR DEMO (Streamlit) — works even if batch_text_detection is missing
+# # 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)
+
+
+# # --- قبل از import streamlit، احیاناً مسیر کش قابل‌نوشتن:
+# import os, tempfile
+# os.environ.setdefault("HF_HOME", "/tmp/hf_home")
+# os.makedirs(os.environ["HF_HOME"], exist_ok=True)
+# import tempfile, os
+# temp_dir = os.path.join(tempfile.gettempdir(), "trustocr_temp")
+# os.makedirs(temp_dir, exist_ok=True)
+# # جای "temp_files" استفاده کن
+
+
+# # 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")
+
+# # from huggingface_hub import HfFolder
+# # HF_TOKEN = os.environ.get("HF_TOKEN")
+
+# # @st.cache_resource(show_spinner=True)
+# # def load_rec_cached():
+# #     return load_rec_model(checkpoint="MohammadReza-Halakoo/TrustOCR", token=HF_TOKEN), \
+# #            load_rec_processor(checkpoint="MohammadReza-Halakoo/TrustOCR", token=HF_TOKEN)
+
+# @st.cache_resource(show_spinner=True)
+# def load_rec_cached():
+#     checkpoints = [
+#         "MohammadReza-Halakoo/TrustOCR",  # خصوصی
+#         "vikp/surya_rec2",                # عمومی (fallback)
+#     ]
+#     last_err = None
+#     for ckpt in checkpoints:
+#         try:
+#             m = load_rec_model(checkpoint=ckpt)
+#             p = load_rec_processor(checkpoint=ckpt)
+#             return m, p
+#         except Exception as e:
+#             last_err = e
+#     st.error(f"Loading recognition checkpoint failed: {last_err}")
+#     raise last_err
+# # @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)
+
+
+# app.py — TRUST OCR DEMO (Streamlit)
+# Works on Hugging Face Spaces (no permission/XSRF issues)
 
 import os
 import io
@@ -10,57 +374,42 @@ 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)
-
 
-# --- قبل از import streamlit، احیاناً مسیر کش قابل‌نوشتن:
-import os, tempfile
+# -------------------- Safe, writable dirs & config (BEFORE importing streamlit) --------------------
+# Put everything under /tmp (world-writable on Spaces)
+os.environ.setdefault("HOME", "/tmp")
+os.environ.setdefault("STREAMLIT_CONFIG_DIR", "/tmp/.streamlit")
+os.environ.setdefault("STREAMLIT_RUNTIME_DIR", "/tmp/.streamlit")
 os.environ.setdefault("HF_HOME", "/tmp/hf_home")
-os.makedirs(os.environ["HF_HOME"], exist_ok=True)
-import tempfile, os
-temp_dir = os.path.join(tempfile.gettempdir(), "trustocr_temp")
-os.makedirs(temp_dir, exist_ok=True)
-# جای "temp_files" استفاده کن
 
+for d in (os.environ["STREAMLIT_CONFIG_DIR"], os.environ["STREAMLIT_RUNTIME_DIR"], os.environ["HF_HOME"]):
+    os.makedirs(d, 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")
+# Create a minimal config.toml to avoid 403 on uploads and reduce telemetry writes
+conf_path = os.path.join(os.environ["STREAMLIT_CONFIG_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)
+        f.write(
+            "[server]\n"
+            "enableXsrfProtection = false\n"
+            "enableCORS = false\n"
+            "maxUploadSize = 200\n"
+            "\n[browser]\n"
+            "gatherUsageStats = false\n"
+        )
 
 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 =====
+# -------------------- Surya imports (gated) --------------------
 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
+from surya.layout import batch_layout_detection  # we'll gate usage using DET_AVAILABLE
 
-# Detection model loaders: segformer (newer) vs model (older)
+# Detection model loaders: try newer segformer, fall back to older
 try:
     from surya.model.detection.segformer import load_model as load_det_model, load_processor as load_det_processor
 except Exception:
@@ -150,7 +499,7 @@ 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).")
+    st.warning("⚠️ ماژول تشخیص متن Surya در این محیط در دسترس نیست. OCR کامل کار می‌کند، اما دکمه‌های Detection/Layout/Order غیرفعال شده‌اند.")
 
 # Sidebar controls
 in_file = st.sidebar.file_uploader("فایل PDF یا عکس :", type=["pdf", "png", "jpg", "jpeg", "gif", "webp"])
@@ -184,19 +533,12 @@ col2, col1 = st.columns([.5, .5])
 def load_det_cached():
     return load_det_model(checkpoint="vikp/surya_det2"), load_det_processor(checkpoint="vikp/surya_det2")
 
-# from huggingface_hub import HfFolder
-# HF_TOKEN = os.environ.get("HF_TOKEN")
-
-# @st.cache_resource(show_spinner=True)
-# def load_rec_cached():
-#     return load_rec_model(checkpoint="MohammadReza-Halakoo/TrustOCR", token=HF_TOKEN), \
-#            load_rec_processor(checkpoint="MohammadReza-Halakoo/TrustOCR", token=HF_TOKEN)
-
 @st.cache_resource(show_spinner=True)
 def load_rec_cached():
+    """Try private checkpoint first, then fall back to public."""
     checkpoints = [
-        "MohammadReza-Halakoo/TrustOCR",  # خصوصی
-        "vikp/surya_rec2",                # عمومی (fallback)
+        "MohammadReza-Halakoo/TrustOCR",  # private (requires HUGGINGFACE_HUB_TOKEN if private)
+        "vikp/surya_rec2",                # public fallback
     ]
     last_err = None
     for ckpt in checkpoints:
@@ -208,10 +550,6 @@ def load_rec_cached():
             last_err = e
     st.error(f"Loading recognition checkpoint failed: {last_err}")
     raise last_err
-# @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():
@@ -279,8 +617,7 @@ 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 detection/recognition models are loaded, pass them; else rely on Surya defaults
     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:
@@ -303,7 +640,8 @@ if "pdf" in filetype:
     pil_image = get_page_image(in_file, page_number)
 else:
     bytes_data = in_file.getvalue()
-    temp_dir = "temp_files"
+    # use /tmp for writes
+    temp_dir = os.path.join(tempfile.gettempdir(), "trustocr_temp")
     os.makedirs(temp_dir, exist_ok=True)
     file_path = os.path.join(temp_dir, in_file.name)
     with open(file_path, "wb") as f:
@@ -359,3 +697,4 @@ with col1:
 
 with col2:
     st.image(pil_image, caption="تصویر ورودی | Input Preview", use_column_width=True)
+