feat: add logging for prediction pipeline output type and content

app.py

@@ -199,6 +199,8 @@ def run_inference(img_path, task_type="ocr"):
     try:
         logger.info(f"--- Inference Start: {task_type} ---")
         output = pipeline.predict(img_path)
+        logger.info(f"Output object type: {type(output)}")
+        logger.info(f"Output object: {output}")
         
         md_content = ""
         json_content = ""
@@ -207,7 +209,7 @@ def run_inference(img_path, task_type="ocr"):
         run_id = f"run_{int(time.time())}"
         run_output_dir = os.path.join(OUTPUT_DIR, run_id)
         os.makedirs(run_output_dir, exist_ok=True)
-
+    
         for i, res in enumerate(output):
             res.save_to_json(save_path=run_output_dir)
             res.save_to_markdown(save_path=run_output_dir)

requirements.txt

@@ -5,7 +5,7 @@ paddleocr[doc-parser]
 gradio
 pillow
 requests
-numpy
+numpy<2.0.0
 psutil
 librosa
 pandas