Update app.py

app.py

@@ -12,35 +12,33 @@ from huggingface_hub import snapshot_download, hf_hub_download
 import os
 import shutil
 
-from transformers import AutoTokenizer, AutoModel
-import inspect
-from collections import namedtuple
-import json
+from fastapi import FastAPI
+from huggingface_hub import hf_hub_download
+import os
 from pydantic import BaseModel
 from fastapi.responses import JSONResponse
 
+print("Version ---- 2")
 app = FastAPI()
 
-pretrained_path = "aubmindlab/bert-base-arabertv2"  # must match training
-tokenizer = AutoTokenizer.from_pretrained(pretrained_path)
-encoder = AutoModel.from_pretrained(pretrained_path).eval()
+def download_file_from_hf(repo_id, filename):
+    target_dir = os.path.expanduser("~/.sinatools")
+    os.makedirs(target_dir, exist_ok=True)
 
+    file_path = hf_hub_download(
+        repo_id=repo_id,
+        filename=filename,
+        local_dir=target_dir,
+        local_dir_use_symlinks=False
+    )
 
-checkpoint_path = snapshot_download(repo_id="https://huggingface.co/spaces/SinaLab/wojood-api/tree/main/Nested", allow_patterns="checkpoints/")
+    return file_path
 
-args_path = hf_hub_download(
-    repo_id="https://huggingface.co/spaces/SinaLab/wojood-api/tree/main/Nested",
-    filename="args.json"
-)
+download_file_from_hf("SinaLab/Nested-v1","args.json")
+download_file_from_hf("SinaLab/Nested-v1","tag_vocab.pkl")
 
-with open(args_path, 'r') as f:
-    args_data = json.load(f)
-    
-# Load model
-with open("https://huggingface.co/spaces/SinaLab/wojood-api/tree/main/Nested/utils/tag_vocab.pkl", "rb") as f:
-    label_vocab = pickle.load(f)
+snapshot_download(repo_id="SinaLab/Nested", allow_patterns="checkpoints/")
 
-    
 
 BASE_DIR = os.path.expanduser("~/.sinatools")