Upload 5 files

config.py

@@ -367,10 +367,9 @@ def load_config() -> AppConfig:
         with open(config_path, "r") as f:
             raw = json.load(f)
     except Exception as e:
-        logger.error(f"Failed to open config.json: {e}", exc_info=True)
+        logger.error(f"Failed to read config.json: {e}", exc_info=True)
         raise
 
-    # Validate and build strong-typed config
     try:
         cfg = AppConfig(**raw)
         logger.debug(f"Config loaded: {cfg.json()}")

handler.py

@@ -81,6 +81,11 @@ class EndpointHandler:
 
     def __call__(self, data: Dict[str, Any], parameters: Dict[str, Any] = None) -> List[Dict[str, Any]]:
         """Handler entry point"""
+        # On first call, if init fails, return the real error
+        if not self.initialized:
+            ok = self.initialize()
+            if not ok:
+                return [{"generated_text": f"Initialization error: {self.init_error}"}]
         try:
             logger.info(f"Handler received request: {data}")
             result = self.predict(data, parameters)
@@ -95,9 +100,8 @@ class EndpointHandler:
                 return [{"generated_text": str(result) if result is not None else "No output generated"}]
         
         except Exception as e:
-            logger.error(f"Error in __call__: {e}")
-            logger.error(traceback.format_exc())
-            return [{"generated_text": f"Error in endpoint handler: {str(e)}"}]
+            logger.error(f"Error in __call__: {e}", exc_info=True)
+            return [{"generated_text": f"Runtime error: {e}"}]
 
     def initialize(self):
         if self.initialized:
@@ -108,7 +112,10 @@ class EndpointHandler:
             self.initialized = True
             return True
         except Exception as e:
+            # log full stack trace
             logger.error(f"Adapter initialization failed for path '{self.path}': {e}", exc_info=True)
+            # store message for client
+            self.init_error = str(e)
             return False
 
     def predict(self, inputs: Dict[str, Any], parameters: Dict[str, Any] = None) -> List[Dict[str, Any]]:

model_PrTr.py

@@ -6,7 +6,7 @@ import torch
 import logging
 import importlib
 import torch.nn as nn
-from config import load_config
+from config import load_config, app_config
 from transformers import AutoTokenizer, AutoModel
 from typing import Optional, List, Dict, Any, Union
 from sentence_transformers import SentenceTransformer
@@ -16,9 +16,6 @@ from service_registry import registry, MODEL, TOKENIZER
 from base_interfaces.common_types import *
 from base_interfaces.model_interface import AbstractModel
 
-# Import environment setup first to ensure config is available
-from model_env_setup import app_config
-
 app_config = load_config()
 logger = logging.getLogger(__name__)
 

requirements.in

@@ -50,7 +50,6 @@ accelerate>=0.20.0
 datasets>=2.10.0
 spikingjelly>=0.0.0.0.12
 norse>=0.0.7
-gradio>=3.50.0
 matplotlib>=3.5.0
 plotly>=5.5.0
 scipy>=1.9.0

requirements.txt

@@ -10,7 +10,6 @@ snntorch>=0.6.0
 spikingjelly>=0.0.0.0.12
 norse>=0.0.7
 nltk>=3.0.0
-gradio>=3.50.0
 matplotlib>=3.5.0
 plotly>=5.5.0
 pandas>=2.0.0