feat(app): scan-mode sync, confirmation-gated documents, single-pass product classification

Fixes reported from APK field testing: DO/Product scan mode was inconsistent
between the camera drawer and documents screen (now one shared provider,
with an orange/green color cue); unconfirmed scans leaked into history with
placeholder data before the user tapped confirm (backend now gates
GET /documents on a new `confirmed` column, flipped only by PUT); and
Product Scan ran the GPU classifier twice, once at upload and again on
review (now a single pass at upload, persisted and read directly by the
editor). Also removes the unused "Hubungkan ke PO" field and fabricated
PO/SO/DO placeholder values from the Product Scan flow, closes out the
per-document-polling and save-recovery tasks (6.1/6.3), and splits several
touched files to stay under the repo's 256-line guideline.

Full detail in docs/iteration-log.md and backend/docs/iteration-log.md.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

backend/config/classify_ocr_server.py

@@ -1,5 +1,6 @@
 import base64
 import io
+import math
 import os
 import re
 import traceback
@@ -471,6 +472,45 @@ async def classify_ocr(payload: ScanRequest):
         raw_image = Image.open(io.BytesIO(img_data))
         image = ImageOps.exif_transpose(raw_image).convert("RGB")
         
+        # First-pass PaddleOCR to check orientation based on Expiry Date
+        rotated_image_used = False
+        res_list = []
+        text_lines = []
+        text_polys = []
+        expired_date = None
+        expired_idx = None
+        expired_source_line = None
+        
+        if ocr:
+            try:
+                img_arr = np.array(image)
+                res_list = list(ocr.predict(img_arr))
+                if res_list and len(res_list) > 0:
+                    res_entry = res_list[0]
+                    text_lines = res_entry.get("rec_texts", [])
+                    text_polys = ocr_text_polys(res_entry)
+                    expired_date, expired_idx, expired_source_line = extract_expired_date(text_lines)
+                    
+                    if expired_idx is not None and expired_idx < len(text_polys):
+                        poly = text_polys[expired_idx]
+                        if len(poly) >= 2:
+                            p0 = poly[0]
+                            p1 = poly[1]
+                            dx = float(p1[0]) - float(p0[0])
+                            dy = float(p1[1]) - float(p0[1])
+                            
+                            angle_rad = math.atan2(dy, dx)
+                            angle_deg = math.degrees(angle_rad)
+                            
+                            # Standardize tilt rotation
+                            if abs(angle_deg) > 3.0:
+                                print(f"[Auto-Rotate] Detected Expiry Date text line angle: {angle_deg:.2f} degrees. Rotating image...")
+                                image = image.rotate(angle_deg, resample=Image.BICUBIC, expand=True)
+                                rotated_image_used = True
+            except Exception as pre_ocr_err:
+                print(f"Error in pre-pass OCR: {pre_ocr_err}")
+                traceback.print_exc()
+
         # 1. Run DINOv2 Similarity Search or YOLO Classification
         classification_result = {}
         top1_name = None
@@ -561,24 +601,33 @@ async def classify_ocr(payload: ScanRequest):
         # 2. Run PaddleOCR
         ocr_result = {}
         if ocr:
-            img_arr = np.array(image)
-            # Use predict method and convert generator to list
-            res_list = list(ocr.predict(img_arr))
-            
-            text_lines = []
-            if res_list and len(res_list) > 0:
-                text_lines = res_list[0].get("rec_texts", [])
-            
-            sku = extract_sku(text_lines)
-            expired_date, expired_idx, expired_source_line = extract_expired_date(text_lines)
-            product_name = extract_product_name(text_lines, top1_name)
-            
-            res_entry = res_list[0] if res_list else {}
-            coord_image = ocr_coordinate_image(res_entry, image)
-            text_polys = ocr_text_polys(res_entry)
-            crop_idx = find_expired_crop_index(
-                text_lines, expired_idx, expired_date, len(text_polys)
-            )
+            if rotated_image_used:
+                img_arr = np.array(image)
+                # Use predict method and convert generator to list
+                res_list = list(ocr.predict(img_arr))
+                
+                text_lines = []
+                if res_list and len(res_list) > 0:
+                    text_lines = res_list[0].get("rec_texts", [])
+                
+                sku = extract_sku(text_lines)
+                expired_date, expired_idx, expired_source_line = extract_expired_date(text_lines)
+                product_name = extract_product_name(text_lines, top1_name)
+                
+                res_entry = res_list[0] if res_list else {}
+                coord_image = ocr_coordinate_image(res_entry, image)
+                text_polys = ocr_text_polys(res_entry)
+                crop_idx = find_expired_crop_index(
+                    text_lines, expired_idx, expired_date, len(text_polys)
+                )
+            else:
+                sku = extract_sku(text_lines)
+                product_name = extract_product_name(text_lines, top1_name)
+                res_entry = res_list[0] if res_list else {}
+                coord_image = ocr_coordinate_image(res_entry, image)
+                crop_idx = find_expired_crop_index(
+                    text_lines, expired_idx, expired_date, len(text_polys)
+                )
             
             # Create visual OCR image with bounding boxes
             vis_image_b64 = None
@@ -633,7 +682,13 @@ async def classify_ocr(payload: ScanRequest):
             # 3. Call Spotting API
             spotting_image_b64 = None
             try:
-                img_b64_only = payload.image_base64.split(",")[-1]
+                if rotated_image_used:
+                    buffered = io.BytesIO()
+                    image.save(buffered, format="JPEG")
+                    img_b64_only = base64.b64encode(buffered.getvalue()).decode("utf-8")
+                else:
+                    img_b64_only = payload.image_base64.split(",")[-1]
+                
                 spotting_payload = {
                     "file": img_b64_only,
                     "matchHistoryJob": False,