Add logger

Add more debug messages
Add more information
Add logo and header

app.py

@@ -1,20 +1,23 @@
 from logging import PlaceHolder
 import gradio as gr
-import os
+import os, sys
 from npc_bert_models.gradio_demo import *
 from npc_bert_models.mlm_module import NpcBertMLM
 from npc_bert_models.cls_module import NpcBertCLS
 from npc_bert_models.summary_module import NpcBertGPT2
+from npc_bert_models.app_logger import get_logger
 import json
 
-
 class main_window():
+    logger = get_logger('main')
     def __init__(self):
         self.interface = None
         self.examples = json.load(open("examples.json", 'r'))
+        self.logger.info(f"Created {__class__.__name__} instance.")
         
     def initialize(self):
         #! Initialize MLM
+        self.logger.info("Loading MLM interface...")
         self.npc_mlm = NpcBertMLM()
         self.npc_mlm.load()
        
@@ -40,6 +43,7 @@ class main_window():
             inp.submit(fn=self.npc_mlm.__call__, inputs=inp, outputs=out)
 
         #! Initialize report classification
+        self.logger.info("Loading BERTCLS interface...")
         self.npc_cls = NpcBertCLS()
         self.npc_cls.load()
         
@@ -75,6 +79,7 @@ class main_window():
             inp.submit(fn=self.npc_cls.__call__, inputs=inp, outputs=out)
         
         #! Initialize report conclusion generation
+        self.logger.info("Loading Bert-GPT2 interface...")
         self.npc_summary = NpcBertGPT2()
         self.npc_summary.load()
         
@@ -85,6 +90,8 @@ class main_window():
                         In this example we explored how the fine-tunned BERT can aid summarizing the reported items and 
                         generates a conclusion, which includes providing stages of the written reports. 
                         
+                        > On this cloud node withonly 2 cpu, it takes ~60 second for this task.
+                        
                         # Disclaimer
                         
                         Again, similar to the last experiement, the examples we list here are mock reports that are created
@@ -112,6 +119,9 @@ class main_window():
             
         #! Create tab interface
         with gr.Blocks() as self.interface:
+            # Logo
+            gr.HTML(open("./assets/header.html", 'r').read())
+            
             gr.Markdown("""
                         # Introduction 
                         
@@ -137,7 +147,7 @@ class main_window():
                                       tab_names=["Masked Language Model", "Report classification", "Report conclusion generation"])     
 
     def lauch(self):
-        self.interface.launch()
+        self.interface.launch(allowed_paths=['assets'])
         pass
             
     def _set_report_file_helper(self, file_in):

assets/header.html

@@ -0,0 +1 @@
+<h1 style="border-bottom: 0.1em solid;background-image: url('/file=assets/logo.png');background-position: center right;background-size: auto 100%;background-repeat: no-repeat;padding-top:0.5em;font-size:32pt"> CUHK NPC-NLP Pilot Demo </h1>

npc_bert_models/app_logger.py

@@ -0,0 +1,34 @@
+import logging
+import inspect
+import sys
+from pathlib import Path
+
+log_format = "[%(asctime)-12s-%(levelname)s] (%(name)s) %(message)s"
+
+def get_logger(logger_name: str = None, log_level: str = 'info') -> logging.Logger:
+    # Set up levels
+    log_levels = logging._nameToLevel
+    log_level = log_levels[log_level.upper()]
+    
+    # Set up logger format
+    logger_name = logger_name or Path(inspect.getmodule(inspect.stack()[1][0]).__file__).name
+    logger = logging.getLogger(logger_name.rstrip('.py'))
+    logger.setLevel(log_level)
+    formatter = logging.Formatter(fmt = log_format)
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setFormatter(formatter)
+    
+    
+    # Set up exception hook
+    if logger_name == 'main':
+        def exception_hook(*args):
+            gettrace = getattr(sys, 'gettrace', None)
+            if not gettrace():
+                logger.error('Uncaught exception:')
+                logger.exception(args[-1], exc_info=args)
+        sys.excepthook = exception_hook
+            
+    # Add handler to logger
+    logger.addHandler(handler)
+    logger.info(f"Created {logger = }")
+    return logger

npc_bert_models/cls_module.py

@@ -2,6 +2,7 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from transformers import pipeline as hf_pipeline
 from pathlib import Path
 from typing import Any, Dict
+from .app_logger import get_logger
 
 class NpcBertCLS():
     r"""A class for performing report classification with BERT.
@@ -21,12 +22,14 @@ class NpcBertCLS():
         pretrained_model (str): 
             The path to the directory containing the fine-tuned model.
     """
+    logger = get_logger()
     def __init__(self):
         self.model = None
         self.tokenizer = None
         self.pipeline = None
         # relative to app.py
         self.pretrained_model = "./models/npc-bert-cls"
+        self.logger.info(f"Created {__class__.__name__} instance.")
     
     def load(self) -> None:
         """Loads the fine-tuned BERT model and related components.
@@ -61,6 +64,7 @@ class NpcBertCLS():
         Raises:
             BrokenPipeError: If the model has not been loaded before calling this method.
         """
+        self.logger.info(f"Called with {args = }")
         if self.pipeline is None:
             msg = "Model was not initialized, have you run load()?"
             raise BrokenPipeError(msg)

npc_bert_models/mlm_module.py

@@ -1,6 +1,7 @@
 from transformers import AutoTokenizer, AutoModelForMaskedLM
 from transformers import pipeline as hf_pipeline
 from pathlib import Path
+from .app_logger import get_logger
 
 class NpcBertMLM():
     r"""A class for performing masked language modeling with BERT.
@@ -21,12 +22,14 @@ class NpcBertMLM():
         pretrained_model (str): The path to 
             the directory containing the fine-tuned model.
     """
+    logger = get_logger()
     def __init__(self):
         self.model = None
         self.tokenizer = None
         self.pipeline = None
         # relative to app.py
         self.pretrained_model = "./models/npc-bert-best"
+        self.logger.info(f"Created {__class__.__name__} instance.")
     
     def load(self):
         """Loads the fine-tuned BERT model and related components.
@@ -61,6 +64,7 @@ class NpcBertMLM():
         Raises:
             BrokenPipeError: If the model has not been loaded before calling this method.
         """
+        self.logger.info(f"Called with {args = }")
         if self.pipeline is None:
             msg = "Model was not initialized, have you run load()?"
             raise BrokenPipeError(msg)

npc_bert_models/summary_module.py

@@ -2,16 +2,17 @@ from transformers import AutoTokenizer, EncoderDecoderModel
 from transformers import pipeline as hf_pipeline
 from pathlib import Path
 import re
-
+from .app_logger import get_logger
 
 class NpcBertGPT2():
-    
+    logger = get_logger()    
     def __init__(self):
         self.model = None
         self.tokenizer = None
         self.pipeline = None
         # relative to app.py
         self.pretrained_model = "./models/npc-bert-gpt2-best"
+        self.logger.info(f"Created {__class__.__name__} instance.")
     
     def load(self):
         """Loads the fine-tuned EncoderDecoder model and related components.
@@ -59,8 +60,10 @@ class NpcBertGPT2():
         if self.pipeline is None:
             msg = "Model was not initialized, have you run load()?"
             raise BrokenPipeError(msg)
+        
+        logger.info(f"Called with arguments {args = }")
+        
         pipe_out, = self.pipeline(*args)
-            
         pipe_out = pipe_out['generated_text']
         
         # remove repeated lines by hard coding