fix: suppress ML library stdout noise in ocr.py and upscale.py

PaddleOCR prints download/init messages to stdout which corrupts the
JSON result that the bridge expects. Same risk with basicsr/realesrgan.
Applied the same fd-level stdout redirect pattern already used in
remove_bg.py: redirect fd 1 to stderr during ML work, restore for
the JSON result. Also added show_log=False to PaddleOCR constructor.

packages/ai/python/ocr.py

@@ -33,6 +33,13 @@ def run_tesseract(input_path, language):
 def run_paddleocr(input_path, language):
     """Run PaddleOCR."""
     os.environ["PADDLE_PDX_DISABLE_MODEL_SOURCE_CHECK"] = "True"
+
+    # Redirect stdout to stderr so PaddleOCR download/init messages
+    # cannot contaminate our JSON result on stdout.
+    stdout_fd = os.dup(1)
+    os.dup2(2, 1)
+
+    try:
         from paddleocr import PaddleOCR
         from gpu import gpu_available
 
@@ -41,7 +48,7 @@ def run_paddleocr(input_path, language):
         paddle_lang = paddle_lang_map.get(language, "en")
 
         emit_progress(20, "Loading")
-    ocr = PaddleOCR(lang=paddle_lang, use_gpu=gpu_available())
+        ocr = PaddleOCR(lang=paddle_lang, use_gpu=gpu_available(), show_log=False)
         emit_progress(30, "Scanning")
         result = ocr.ocr(input_path)
         emit_progress(70, "Extracting text")
@@ -54,6 +61,11 @@ def run_paddleocr(input_path, language):
                 if line and line[1]
             ]
         )
+    finally:
+        # Restore stdout
+        os.dup2(stdout_fd, 1)
+        os.close(stdout_fd)
+
     return text, "paddleocr"
 
 

packages/ai/python/upscale.py

@@ -30,12 +30,22 @@ def main():
         new_size = (img.width * scale, img.height * scale)
 
         # Try Real-ESRGAN first
+        try:
+            # Redirect stdout to stderr so basicsr/realesrgan init messages
+            # cannot contaminate our JSON result on stdout.
+            stdout_fd = os.dup(1)
+            os.dup2(2, 1)
+
             try:
                 from basicsr.archs.rrdbnet_arch import RRDBNet
                 from realesrgan import RealESRGANer
                 from gpu import gpu_available
                 import numpy as np
                 import torch
+            finally:
+                # Restore stdout after imports
+                os.dup2(stdout_fd, 1)
+                os.close(stdout_fd)
 
             if not os.path.exists(REALESRGAN_MODEL_PATH):
                 raise FileNotFoundError(f"RealESRGAN model not found: {REALESRGAN_MODEL_PATH}")